no Cortical evolution in Primates. What Primates are, what Primates were, and why the cortex changed [1/1, 1 ed.] 9780192868398, 9780192694621

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Cortical Evolution in Primates What Primates Are, What Primates Were, and Why the Cortex Changed Steven P. Wise

Great Clarendon Street, Oxford, OX2 6DP, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries © Oxford University Press 2024 The moral rights of the author have been asserted First Edition published in 2024 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this work in any other form and you must impose this same condition on any acquirer Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America British Library Cataloguing in Publication Data Data available Library of Congress Control Number: 2023940944 ISBN 978–0–19–286839–8 eISBN 978–0–19–269462–1 DOI: 10.1093/oso/9780192868398.001.0001 Oxford University Press makes no representation, express or implied, that the drug dosages in this book are correct. Readers must therefore always check the product information and clinical procedures with the most up-to-date published product information and data sheets provided by the manufacturers and the most recent codes of conduct and safety regulations. The authors and the publishers do not accept responsibility or legal liability for any errors in the text or for the misuse or misapplication of material in this work. Except where otherwise stated, drug dosages and recommendations are for the non-pregnant adult who is not breast-feeding Links to third party websites are provided by Oxford in good faith and for information only. Oxford disclaims any responsibility for the materials contained in any third party website referenced in this work.

To Jon H. Kaas, a pioneering primate

Preface

Prizes and progeny In the epigraph, the Dodo of Alice in Wonderland fame announces the results of a race: everyone won. A longer quotation places this verdict in context. The Dodo marked out a race-course, in a sort of circle, (“the exact shape doesn’t matter,” it said) and then all the party were placed along the course, here and there. There was no “One, two, three, and away,” but they began running when they liked, and left off when they liked, so that it was not easy to know when the race was over. However, when they had been running half an hour or so . . . the Dodo suddenly called out “The race is over!” and they all crowded round it, panting, and asking, “But who has won?” This question the Dodo could not answer without a great deal of thought, and it sat for a long time . . . . At last the Dodo said, “EVERYBODY has won, and all must have prizes.”

Evolution is something like that. Every day, individuals race for survival, which permits them, perhaps, to pass their genes to progeny; every day, the race goes on with gusto, but without any end or ends in sight; and every day, it’s like the race is over, but of course it’s not. It’s common to consider living species as a special category: the ones that made it. But that impression is merely an artifact of the fact that today is just another day. Long ago, a different day saw different species, and one of them was the first primate. Primates of the past If, via some Jurassic Park-like resurrection, the earliest primates appeared in a zoo somewhere, they wouldn’t attract much attention. Few people would adulate our ancestors absent the knowledge that their issue includes humankind. Instead, these small, arboreal mammals would have no more claim to fame than flying lemurs or tree shrews have. If a time machine enabled us to visit the first primates in their day, they’d probably be disappointing: just another species living the high life in trees. We would witness little more than ordinary mammals eating a few bugs, berries, and blossoms at night and dozing through the day, and they did little else for millions of years. Then one day, about 50 million years ago, a hot-house world teeming with trees saw the first glimmer of a gradual cooling trend that would continue, off and on, for 16 million years. In time, it caused rainforests to contract: bad news for our primate ancestors, which depended on forests for life and limbs. More hardship followed, beginning 34 million years ago when global cooling accelerated sharply. Forests contracted yet more, nutrients began to vary markedly from season-to-season, and large predators appeared on the scene. It wasn’t pleasant to be a primate back then, but somehow our ancestors survived. Many species did not. The primates that made it through those tough times gave birth to all that followed. It’s only natural for modern primates to pop into the mind’s eye at the mention of extinct ones, but primates of the past differed from those alive today. For one thing, when the earth cooled 34 million years ago, most primates were small animals. Only later did some become the larger animals we know today. Likewise, today’s primates live long lives, but that hasn’t always been the case, either. Our ancestors had short lifespans, like most small mammals. The transition from a brief life to a lengthy one was a big part of primate evolution, which means that it was also a big part of human evolution. Today, nearly everyone understands that human evolution is part of primate evolution. As obvious as this fact seems, we have only known that we are primates for about 0.1% of our existence as a species, maybe less. It was Linnaeus who first classified people as primates ~300 years ago, and it caused quite a commotion at the time. Even to this day, some people renounce their primate heritage, as if unsavory behavior by monkeys at the zoo reflects badly on all of us. On the contrary, I’m proud to be a primate, despite the pompous name of our order. Primates, prelates, and priority Why pompous? Unlike other orders, which have labels that emphasize their physical features or behaviors, ours coveys a deep-seated conceit. Dictionaries and etymologies reveal the self-congratulatory nature of the word “primate.” Merriam-Webster (https://www.merriam-webster.com/dictionary/primate) provides a typical example, defining “primate” as: • . . . a bishop who has precedence in a province, a group of provinces, or a nation. • archaic: one first in authority or rank: leader. • . . . any of an order (Primates) of mammals that are characterized especially by advanced development of binocular vision resulting in stereoscopic depth perception, specialization of the hands and feet for grasping, and enlargement of the cerebral hemispheres and that include humans, apes, monkeys, and related forms (such as lemurs and tarsiers).

The first usage is uncommon, but it does turn up now and then. The Primate of All England is the Archbishop of Canterbury, and the Pope is also the Primate of Italy. What could these prelates possibly have in common with an order of mammals? The answer is that all the meanings of “primate” derive from the Latin primas, meaning first. Thus, primates were once thought to be an order that ranked first in an ordered sequence of orders. In contrast to the narcissism inherent in the name “primate,” other orders have more modest monikers. Bats, for example, compose the order Chiroptera, derived from the Greek kheir for hand and pteron for wing. In any language, the name hand-wing captures the essence of being a bat. Charmingly, Eulipotyphla—a group that includes hedgehogs along with various shrews and moles—means fat and blind: not very flattering and a bit of an exaggeration, but reasonably descriptive. Rodentia comes from the Latin for gnaw, a specialization of the order; Scandentia derives from the Latin for climb, which tree shrews do quite well; and Carnivora means flesh eater. These names say something about each order’s distinctive characteristics, but ours implies that we outrank ordinary orders. At long last, the practice of ranking animals based on their similarity to humans, called the scala naturae or the evolutionary scale, has begun to disappear from the neuroscience literature. According to that outmoded idea, the more closely an animal resembles us, the higher they perch on the “ladder of life.” References to a phylogenetic scale persisted in our field for far too long, but evolutionary biologists have known better for generations. If you hear or read anything about higher primates or lower vertebrates, you are witnessing the dying embers of the scala naturae. Contemporary biology rejects the idea that orders have an order. More importantly, no amount of similarity to humans alters evolutionary relationships. Even if dolphins could squeak sonnets in iambic pentameter, they would still be more distantly related to humans than rabbits are; elephants might mourn their dearly departed friends, but rats remain more closely related to us without giving a rat’s ass about the demise of their fellow vermin; and polar bears might make magnificent mothers, but they are far more distant relatives than the worst mothers in the mammalian world: tree shrews, which only visit their newborns every two days or so—and then only briefly. To understand cortical evolution in primates, phylogenetic relationships matter; the place of primates on an imaginary “ladder of life” does not. Primates are the animals most closely related to us, but they don’t rank higher on a phylogenetic scale. Primates in their proper place Regardless, primates don’t belong on ladders; the proper place of primates is in trees. Not only did early species live in trees, but primates are one branch of the tree of life. As our ancestors raced for survival along both kinds of branches, they evolved a large cerebral cortex. In primates of the present day, most of the brain consists of cortex, but that wasn’t true of early species. Like the Dodo’s running party, every living primate has won a race, and a colossal cortex is one of the prizes. The inspiration for Lewis Carroll’s Dodo might provide a clue about why primates won that particular prize. Dodos were large, flightless pigeons that once lived in dense forests on Mauritius, an island in the Indian Ocean. They thrived for millennia; then one fine day in the 1600s human colonists arrived, and the dodo’s days were numbered. Several anthropogenic factors contributed to their extinction, including the introduction of pigs, rats, and monkeys that plundered the dodo’s nests. The fact that islanders hunted these 20-pound birds didn’t do them any good either, but habitat reduction from deforestation was probably the main reason for their demise. Likewise, deforestation has caused many primate species to go the way of the dodo. The earth’s climate has cooled several times over the past 50 million years, which decreased atmospheric moisture and caused rainforests to dry and contract. Global cooling threatened species that relied on the nutrients and protection that rainforests provide, as primates did back then and many do today. The effects of global climate change might provide some hints about why primates evolved such a large cerebral cortex. As its subtitle says, this book explores what primates are, what primates were, and why the cortex changed. Today, all primates are remarkably brainy; 50 million years ago, primates were run-of-the-mill placental mammals with a cortex to match. The life and times of primates in-between are why the cortex changed.

Acknowledgments

I thank the following colleagues for their comments on individual chapters: Mary Baldwin, Betsy Murray, Daniel Pine, Todd Preuss, and Georg Striedter. Samantha White, Caleb Darden, and Jensen Palmer read the entire book and discussed the chapters with me at length, and I thank Mark Laubach for arranging those meetings. My commissioning editor at Oxford University Press, Martin Baum, encouraged me to undertake this project and provided crucial support along the way, as did Phoebe Aldridge-Turner, who guided the manuscript from submission to publication. Anya Hastwell edited the manuscript with skill and dedication. I owe a different kind of debt to Jon Kaas, which the dedication of this book acknowledges. The book you are holding in your hands would not exist if he and his students hadn’t performed the decades of research that they did. One of them, Leah Krubitzer, deserves special recognition. When I was a graduate student in the 1970s, my research focused on the somatosensory cortex of rats, cats, squirrels, squirrel monkeys, and both rhesus and crab-eating macaques. I also did some work on the auditory cortex of mustache bats. Naturally, I tried to understand how the cortical maps of these diverse mammals related to each other. But it was all a muddle until one day at a Society for Neuroscience annual meeting. Leah presented a poster that summarized her cortical mapping studies of the duckbilled platypus and some ideas about cortical evolution in mammals. Within half an hour, my knowledge of comparative cortical anatomy became much better organized. This book is one among many consequences of that 30minute discussion. Todd Preuss also deserves special recognition for his contributions to understanding cortical evolution in primates, and more than a few of the ideas in this book stem from my coauthors on previous books, in chronological order: Reza Shadmehr, Dick Passingham, Betsy Murray, Kim Graham, and Mary Baldwin, whose inspired artwork enlivens many of the illustrations in this book.

Contents

List of figures and tables Reference figure List of abbreviations: text List of abbreviations: figures Epigraph PART I. WHAT PRIMATES ARE 1.

Topics tackled Overview Why? What? When? Why now? Why not? Why not now? Why try? Chapter summary References

2.

Compact cladistics Overview Introduction Taxing terminology Homology, homoplasy, and analogy Trees and scales Old and new areas Chapter summary References

3.

Present primates Overview Introduction Taxing taxonomy Principal primate clades Strepsirrhines Haplorhines Catarrhines Chapter summary To “the” or not to “the” References

PART II. WHAT PRIMATES WERE 4.

Prolog to paleontology Overview Introduction The dating scene

Bodies from bones Focus on forests Chapter summary References 5.

Arboreal adaptations Overview Introduction Primates true and stem Into the trees Chapter summary Cortical considerations References

6.

Primate paleoecology Overview Introduction Paleocene plesiadapiforms Eocene Euprimates Oligocene openings and Miocene monkeys Miocene modifications and Plio-Pleistocene primates Chapter summary References

PART III. WHAT PRIMATE CORTEX WAS 7.

Great grades of gray Overview Introduction Measures and misconceptions Grades and clades Eocene expansions Chapter summary References

8.

Greater grades of gray Overview Introduction Miocene monkeys and apes Plio-Pleistocene hominins Body or brain? Chapter summary References

9.

Tempo and temperature Overview Introduction Time travel Cooling and crisis Corticalization and speciation Cortex and corpus Chapter summary References

10. Other orders

Overview Introduction Pride of place The origin of mammals Brain expansion Chapter summary Parietofrontia References PART IV. WHAT PRIMATE CORTEX IS 11. Cortical comparisons Overview Introduction A Declaration of Independence Flying primates, feathered apes Crucial comparisons Misconceptions: minor and massive Chapter summary References 12. Suites of specializations Overview Introduction From tip to toe Suite success Chapter summary References 13. Anthropoid adaptations Overview Introduction The big chill Frontal-field phylogeny Changes at the top Sights and sounds on the side Guilt by association Chapter summary References 14. Human hemispheres Overview Introduction Whole hemispheres Area analysis Sensational size Temporal tracts Allocortical alterations Cortex and chromosomes Chapter summary References PART V. WHY THE CORTEX CHANGED 15. Eocene expansions

Overview Introduction What’s new is old The cortex complete Eocene enlargements Chapter summary References 16. Anthropoid augmentations Overview Introduction Groups, grub, and gray matter Statistics and significance Climate and cortex Principal proposal Chapter summary References 17. Pleistocene prizes Overview Introduction Suggestions for selection Self and social systems Chapter summary References 18. Corticalization and composition Overview Introduction Cortical chauvinism Parts and primates Questions and conclusions Instead of intelligence: representations Chapter summary References Epilogue Crucial glossary Extended glossary Index

List of figures and tables

Figures Reference figure

1.1 2.1 2.2 3.1 3.2 3.3 3.4 3.5 3.6 4.1 4.2 4.3 4.4 5.1 5.2 5.3 6.1 6.2 6.3 6.4 6.5 6.6 6.7 7.1 7.2 7.3 7.4 7.5 7.6 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11 8.12 9.1 9.2 10.1 10.2 10.3 10.4 10.5 11.1 11.2 11.3 11.4 11.5 11.6 12.1 12.2 12.3 13.1 13.2 13.3 13.4 13.5 13.6 14.1 14.2 14.3

The brain of an extinct primate Graphical definitions of cladistic terms Phyletic dwarfism in callitrichid primates Differing views of primate relations Mammalian evolutionary tree before molecular phylogenies An evolutionary tree of eutherian mammals An evolutionary tree of Euarchontoglires An evolutionary tree of Euarchontoglires, emphasizing anthropoids An evolutionary tree of catarrhines Geological epochs and eras Chronograms of Euarchontoglires and primates Tooth morphology and diet Global surface temperature during the Cenozoic An evolutionary tree of primates Dentition in plesiadapiforms Eocene geography Convergent evolution in distantly related primates Chronogram of Euarchontans Anthropoid extinctions during the Oligocene bottleneck in North Africa The evolution of body size in anthropoids Selective pressures and anthropoid adaptations Evolutionary trajectories of femur morphology in anthropoids Climate change and adaptive radiations Brain–body relationships in primates and other mammals Virtual cranial endocasts of fossil primates Eocene grade-shifts in encephalization Eocene grade-shifts in corticalization Longevity and encephalization Summary of Eocene grade-shifts Encephalization quotients in mammals Upward grade-shifts during anthropoid evolution The emergence and loss of sulci in anthropoids Olfactory bulb contraction Cortical expansion in hominins Encephalization in hominins Encephalization quotients and estimated divergence times in hominins and panins Changes in human brains and brain shape in hominids Skull shape in Homo species Anterior temporal bulging in four human species and chimpanzees Grade-shifts in brain size–body size allometry in primates Summary of Eocene and Miocene grade-shifts Upward grade-shifts in cortex size and periods of global cooling Brain size, body size, land productivity, and rainfall EQ values in fossil and modern mammals Cortical grade-shifts in cetaceans Cortical grade-shifts in artiodactyls Cortical grade-shifts in carnivores Cortical grade-shifts in rodents Diversity of Euarchontoglires cortex Tree shrew phylogeny Grade difference in posterior parietal cortex Ideas about homologies among frontal areas in rodents and primates Types of cerebral cortex Cortical organization in mammals Cortical maps in selected Euarchontoglires Action maps in selected Euarchontoglires Transcortical networks Relative size of the frontal lobe in Euarchontoglires Cortical maps of an anthropoid and a strepsirrhine Types of frontal cortex in selected Euarchontoglires Phylogeny of traits involved in grasping and manipulation Fingers, “foveas,” and frugivory Action maps in galagos, squirrel monkeys, and rhesus macaques Prefrontal predominance in humans Expansion of the prefrontal cortex in humans Preferential expansion of prefrontal cortex

14.4 14.5 14.6 14.7 14.8 14.9 15.1 15.2 15.3 16.1 16.2 16.3 17.1 17.2 18.1 18.2

Myelin density in the cortex of four anthropoids Relative expansion of cortical regions Evolutionary changes in the temporal lobe of anthropoids and frontotemporal pathways Hippocampal contraction in anthropoids, followed by expansion in humans Neuronal density and counts for the cerebral cortex Genetics of cortical expansion in hominins Conjunctive representations in the cortex of primates The ventral visual stream in anthropoids Primate social and mating systems Relation between clique size and corticalization Anthropoid synapomorphies The sources of visual feature conjunctions in frontal cortex: dorsal and ventral visual streams Transcortical networks in humans Technological knowledge and cortical expansion Cortical evolution in primates Cortical evolution in primates: simplified

Table 17.1 Selective factors that might have contributed to cortical expansion in hominins

Reference figure. Evolutionary relationships among the mammals mentioned in this book. The table at the top gives onset dates for the geological epochs discussed most frequently in the text. Abbreviation: Ma, million years ago

List of abbreviations: text

A1 AF AGm AIP ARHGAP11B BOLD C3 and C4

Primary auditory area Arcuate fascicle Medial agranular frontal area (in rodents) Anterior intraparietal cortex Rho GTPase-activating protein 11B Blood oxygen-level decrease Two metabolic pathways for photosynthesis

CA3 DM DS–DC EECO EOCT EQ ES FEF fMRI FOXP2 Fr2 FST HARE5 HSD IQ ICZN IFOF ILF L LCA LGN LIP LT-ICMS LUCA M M1 M1c M2 Ma MECO MIP MMCO MMCT MRI MST MT MTc NOTCH2NL PC PCA PETM PFdl PFdm PFo PFp PFvl PIm PM PMd PMv Pom PP, PPC PPr PR PrCm PV Rex RMA S

Third area of Ammon’s horn (Cornu Ammonis), the hippocampus Dorsomedial visual area Diagonal sequence, diagonally coupled [gait] Early Eocene climatic optimum Eocene–Oligocene climatic transition Encephalization quotient Extrastriate cortex Frontal eye field Functional magnetic resonance imaging A gene involved in orofacial coordination Second frontal area (in rodents), also known as AGm Fundus superior temporal area Human-accelerated regulatory enhancer 5 Hominini-specific deletion Intelligence quotient International Commission on Zoological Nomenclature Inferior frontal–occipital fascicle Inferior longitudinal fascicle Long wavelength [cone photoreceptor] Last common ancestor Lateral genicular nucleus Lateral intraparietal cortex Long-train intracortical microstimulation Last universal common ancestor Middle wavelength [cone photoreceptor] Primary motor cortex Caudal primary motor cortex In primates, the supplementary motor area; in tree shrews, a motor area Million years ago Middle Eocene climatic optimum Medial intraparietal area Middle Miocene climatic optimum Middle Miocene climatic transition Magnetic resonance imaging Middle superior temporal area Middle temporal area Middle temporal crescent area Notch homolog 2 N-terminal-like protein Principal component Principal components analysis Paleocene–Eocene thermal maximum Dorsolateral prefrontal cortex Dorsomedial prefrontal cortex Granular orbital prefrontal cortex Polar prefrontal cortex; frontal-pole cortex Ventrolateral prefrontal cortex; also known as ventral prefrontal cortex Middle inferior pulvinar nucleus of the thalamus 1., Premotor cortex (in primates); 2., posteromedial area (in rodents) Dorsal premotor cortex Ventral premotor cortex Posterior medial nucleus of the thalamus Posterior parietal cortex Rostral posterior parietal cortex Public relations Medial precentral area (in rodents), also known as AGm Ventral parietal somatosensory area Tyrannosaurus rex Rostral motor area (in rodents), also known as AGm Short wavelength [cone photoreceptor]

S1 S2 SC SMA SRGAP2 ST-ICMS T. rex TA TD TP TPJ V1 V2 V3 VIP

Primary (or first) somatosensory area Second (or secondary) somatosensory area Caudal somatosensory area Supplementary motor area, also known as M2 Slit protein, roundabout-receptor GTPase-activating protein 2 Short-train intracortical microstimulation Tyrannosaurus rex Anterior temporal area Dorsal temporal area Posterior temporal area Temporal–parietal junction Primary (or first) visual area; striate cortex Second (or secondary) visual area Third visual area Ventral intraparietal cortex

List of abbreviations: figures

A. a, A A1 AB AC AF AGm AIP Anc AP APB AS Aud c CA3 CBLN2 cc CG CgS CLI CMc CMr CMv CoA CS d, D DF DL DLPFC DM DMPFC Dys EECO EmC EOCT EQ ERh ES FEF FPC Fr2 FST G G1 Gr H. Hippos hl HSD i Ia Id Ig IFOF IL ILF Ins IPS IT ITS l, L LatS LCA LIP LO LunS m, M

Australopithecus Anterior Primary auditory cortex Auditory belt cortex Anterior cingulate cortex Arcuate fascicle Medial agranular area, also known as Fr2, PrCm, and RMA Anterior intraparietal area Ancestral Anterior-posterior Posterior auditory belt cortex Arcuate sulcus Auditory cortex Caudal An area within the hippocampus Cerebellin 2 precursor Corpus callosum Cingulate gyrus Cingulate sulcus Claustral isocortex Caudal cingulate motor area Rostral cingulate motor area Ventral cingulate motor area Cortical nucleus of the amygdala Central sulcus Dorsal Dorsal frontal area Dorsolateral visual area Dorsolateral prefrontal cortex, also abbreviated PFdl Dorsomedial visual area Dorsomedial prefrontal cortex, also abbreviated PFdm Dysgranular cortex Early Eocene climatic optimum Extreme capsule Eocene–Oligocene climatic transition Encephalization quotient Entorhinal cortex Extrastriate visual areas Frontal eye field Frontopolar cortex, also abbreviated PFp Second frontal area, also known as AGm Fundus of the superior temporal cortex Gustatory cortex, also known as the primary gustatory cortex Primary gustatory cortex Granular Homo Hippopotamuses Hindlimb representation of the primary motor cortex Human-specific deletion (of a gene sequence) Inferior Agranular insular cortex Dysgranular insular cortex Granular insular cortex Inferior fronto-occipital fascicle Infralimbic cortex Inferior longitudinal fascicle Insular cortex Intraparietal sulcus Inferior temporal visual cortex Inferior temporal sulcus Lateral Lateral (Sylvian) sulcus (or fissure) Last common ancestor Lateral intraparietal area Lateral orbitofrontal cortex Lunate sulcus Medial

M1 M1c M2 Ma MECO MF MIP MLF MM MMCO MMCT MO MS MST MT MTc NOTCH2NL o, O O. OB OF, OFC OFO Oligo P. p, P P–P, Plio–Plei Paleo PC PCA PCm PETM PFC PFc PFdl PFdm PFg PFm PFo PFp PFvl Pir PL Plio PM PMd PMv PO PP PPc PPl PPm PPr PR PrCO PRh Pro PS PV r, R Ri RMA Rs Rs ag Rs g S1 S2 SC SMA SPS SR SRGAP2 SS ST STP STS T TA TD

Primary motor cortex Caudal primary motor cortex Except in tree shrews, a nonprimary motor area, also known as SMA Million years ago Middle Eocene climatic optimum Medial frontal area Medial intraparietal area Medial longitudinal fascicle Medial motor area Middle Miocene climatic optimum Middle Miocene climatic transition Medial orbitofrontal cortex Medial sensory area Middle superior temporal visual area Middle temporal visual area Middle temporal crescent area Notch homolog 2 N-terminal-like protein Orbital Orcinus Olfactory bulb Orbitofrontal cortex Opercular orbitofrontal cortex Oligocene Paranthropus Posterior Pliocene–Pleistocene Paleocene Posterior cingulate areas Principal component analysis Medial precentral area Paleocene–Eocene thermal maximum Prefrontal cortex Caudal prefrontal cortex, including the frontal eye field Dorsolateral prefrontal cortex Dorsomedial prefrontal cortex Granular prefrontal cortex Granular medial prefrontal cortex Granular orbital prefrontal cortex Polar prefrontal cortex, also abbreviated FPC Ventrolateral prefrontal cortex Piriform cortex Prelimbic cortex Pliocene Premotor cortex (in primates); part of the parietal cortex (in rodents) Dorsal premotor cortex Ventral premotor cortex Parieto-occipital visual area Posterior parietal cortex, also abbreviated PPC Caudal posterior parietal cortex Lateral posterior parietal cortex Medial posterior parietal cortex Rostral posterior parietal cortex Rostral parietal somatosensory area Opercular proisocortex Perirhinal cortex Prostriate cortex Principal sulcus Ventral parietal somatosensory area Rostral Retroinsular area Rostral motor area Retrosplenial cortex Agranular retrosplenial cortex Granular retrosplenial cortex Primary (or first) somatosensory cortex Second (or secondary) somatosensory cortex Caudal somatosensory cortex Supplementary motor area, also known as M2 Superior precentral sulcus Rostral somatosensory cortex Slit protein, roundabout-receptor GTPase-activating protein 2 Supplementary sensory area Superior temporal cortex Superior temporal polysensory area Superior temporal sulcus Caudal temporal visual area Anterior temporal visual cortex Dorsal temporal visual cortex

TE TEa TEo TF TFo TH TI TIV TL TLo TP UF v, V V1 V2 V3 V3a V4 Vis VIP VLPFC VO VP VS

Fifth temporal area (TA being the first) Anterior part of the fifth temporal area Occipital part of the fifth temporal area Sixth temporal area Occipital part of the sixth temporal area Eighth temporal area Inferior temporal visual cortex Inferior ventral temporal visual cortex Limbic temporal area Occipital part of the limbic temporal area Posterior temporal visual cortex Uncinate fascicle Ventral Primary (or first) visual cortex, also known as striate cortex Second (or secondary) visual cortex Third visual cortex Dorsomedial part of the third visual area Fourth visual area Visual areas Ventral intraparietal area Ventrolateral prefrontal cortex, also abbreviated PFvl Ventral orbitofrontal cortex Ventral pathway Ventral somatosensory cortex

At last the Dodo said, “EVERYBODY has won, and all must have prizes.”

—Lewis Carrol, Alice’s Adventures in Wonderland

PART I WHAT PRIMATES ARE

1 Topics tackled Overview Early primates had a small cerebral cortex: no larger than the one that rodents have today. In all modern primates, the cortex is large. These facts raise five questions: (1) When did cortical expansions occur; (2) in which primates did it happen; (3) what ecological factors selected for a large cortex; (4) what new areas contributed to it; and (5) how similar was cortical enlargement in other mammals? Along with comparative neuroanatomy, three fields of biology provide some answers. Paleontology reveals the skeletal and dental adaptations of extinct primates; paleoecology considers a bygone world and the life of primates in it; and paleoneurology provides important (albeit limited) information about their cortex. Progress in paleoneurology and comparative neuroanatomy prompted this monograph. Findings from these two fields reveal that several independently evolving primate lineages developed a cortex that dominates the brain and includes numerous new areas. The question is: Why?

Ah, but my dear sir, the why must never be obvious. That is the whole point. —Agatha Christie, Five Little Pigs, Dodd, Mead, and Company, 1942

Why? In this chapter, gibbons get insulted; a fossil is found; and I say what the book says. But I begin with two “why” questions that organize this book: • •

Why did the cerebral cortex expand during primate evolution? Why does the cortex have the areas observed in primates today?

Most neuroscience books address a different kind of question: How? “How” questions find their answers in the anatomical, physiological, genetic, and developmental mechanisms of the nervous system: what Mayr called proximate causation.1 “Why” questions have different kinds of answers because evolution is the ultimate cause of cortical expansion and the emergence of new areas. A central theme of this book is that the second question changes the nature of the first. Instead of asking why the cortex expanded in a general sense, we can explore why specific cortical areas emerged or enlarged during primate evolution. What? These two “why” questions lead to a series of “what” questions. A friend and colleague of mine, Dick Passingham, tells a story about his first book, The Human Primate. After he finished a complete draft, he visited a departmental colleague, Alan Cowey, as he did most days. Alan asked Dick a simple question: “What does the book say?” I doubt that the question flummoxed Dick as much as he claims, but—according to his account—he had no idea how to answer. He realized that although his draft discussed many topics, it didn’t really say anything. So, he rewrote the whole thing. To avert a similar question about this book, I offer this four-sentence, 130-word preview of what it says: • • • •

The cerebral cortex expanded more recently than most neuroscientists suspect, and it happened: (1) many times, not just once or twice; (2) independently in the major primate lineages; and (3) in primates very different from any modern species, as they adapted to ecosystems that no longer exist. Neural networks throughout the cortex represent conjunctions of information, such as sensory features, locations, actions, goals, and behavioral outcomes, in diverse combinations that vary in their level of complexity and abstraction. Via genetic developmental programs that add neurons to cortical areas and networks, natural selection fosters the expansion of areas that have adaptively advantageous neural representations, thus improving those representations and facilitating the emergence of new ones. These processes explain why the cortex expanded during primate evolution and has the areas observed today.

Most of the book deals with the first point, but the others are always in the background, supporting the discussion. What’s the word? This book also says what some words mean in the context of evolution. Neuroscientists need a vocabulary that can support an in-depth discussion of cortical evolution, and we don’t have time to become evolutionary biologists. Therefore, instead of using formal zoological nomenclature and the entire lexicon of evolutionary biology, I have simplified both. For instance, I ignore traditional taxonomy and use common names whenever possible: dodos rather than Raphus cucullatus or, yet more enigmatically, R. cucullatus. When discussing fossil primates, unfamiliar terms make the text challenging, but I limit it to genus-level names when I can and ignore the strictures of binomial nomenclature. The International Commission on Zoological Nomenclature (ICZN) makes the rules biologists live by,

and the specialty literature adheres to them rigorously. I don’t, although when I’ve exhausted the alternatives, I knuckle under from time to time. As an example of technical writing in the evolution literature, the following paragraph comes from a fascinating paper on octopus evolution (p. 8):2 Syllipsimopodi bideni gen. et sp. nov. is a Carboniferous gladius-bearing vampyropod with ten robust, sucker-bearing arms. It is the oldest known vampyropod and neocoleoid. Neocoleoids originated before the Serpukhovian; likely in the Late Devonian, potentially as a component of the broader pelagic reorganizations of the Famennian to Tournaisian. Our Bayesian FBD [fossil birth–death] phylogeny reconstructs Syllipsimopodi as the basalmost vampyropod, Idahoteuthis as a vampyropod, Prototeuthidina as the basalmost octobrachian clade, Proteroctopus as the basalmost stem vampyromorph, Loligosepiina as a clade of stem vampyromorphs, Teudopseina as a paraphyletic grade of stem octopods, Phragmoteuthis as a belemnoid, and a monophyletic Belemnoidea as sister to Decabrachia. By the Serpukhovian, vampyropods already possessed a fully-formed gladius without a phragmocone. This, together with the reaffirmed rejection of the phragmoteuthid-origins hypothesis, suggests the proostracum/gladius is unlikely to be a remnant of the phragmocone body chamber.

Although neuroscience papers probably seem equally obscure to evolutionary biologists, students and practitioners in our field need something different. So, I cut corners. I also use words forbidden by some authorities. Any biologist can tell you why there’s no such thing as a monkey or a prosimian. Both are paraphyletic groups, not natural taxa, concepts that Chapter 2 explains. However, because neuroscientists understand such terms, I use them freely. Likewise, primates are sometimes “us” and at other times “them,” as the spirit of the occasion seems to warrant. I even mention great apes from time to time. In doing so, I beg forgiveness from the lesser apes, which I admire every bit as greatly as I do the greatest of great apes. Great, in this sense, means big; it’s not an insult to gibbons. Two glossaries should help with unfamiliar terminology. One (p. 353), entitled “Crucial glossary,” defines the terms needed to understand the text and figures. On their first use, these words appear in bold type, mostly in Chapters 2 and 3. An “Extended glossary” (p. 355) defines words that come up now and then but aren’t as fundamental to the discussion. Cross-references within a chapter appear in parentheses and quotation marks, like this: (“Section heading”). References across chapters include the chapter number, like this: (Chapter #, “Section heading”). Words are also important for brain structures. Sometimes they facilitate an understanding of brain evolution, but they can hinder it, too. For example, the optic tectum of amphibians and the superior colliculus of mammals share a common ancestry but have unrelated names. (Tectum means roof; colliculus means little hill.) This convention makes it difficult to recognize that they’re the same thing. It can be equally unhelpful to apply the same label to brain structures that don’t descend from a common ancestor. Chapter 8 explains that some species of New World monkeys and Old World monkeys have a principal sulcus in their frontal cortex. Because it has the same name and many other similarities, it might seem obvious that both groups of monkeys inherited the principal sulcus from a common ancestor. Fossil evidence shows that they didn’t. By emphasizing ancestry rather than labels, discussions of the cortex, evolution, and primates become a lot easier. What’s the cause? This book also says something about the selective factors that produced the cortex of modern primates, but it doesn’t advocate an overarching one (or two, for that matter). Based on studies of extant species, many authorities argue that one or two dominant influences led to the large cortex of primates, such as sophisticated social systems, monogamy, longevity, manual dexterity, maternal investment, dietary specializations, foraging in a complex environment, or predator avoidance. I discuss all these ideas but don’t place any one of them above the others. It seems likely that different influences operated at various times and places, often in combination. The primatologist Robert Martin put it well in a Facebook® comment that discussed human brains and intelligence (April 3, 2021): I have often wondered why any new proposal announcing discovery of THE factor that drove evolution of the very large human brain is at once picked up and lionized by the media. This has to be the most trivial pursuit to be found in discussions of human evolution. It should be blindingly obvious that multiple factors have been involved in the evolution of the human brain. As a result, there is a complex network of correlations between factors. Picking out just one correlation and interpreting it as a causal relationship is . . . not particularly intelligent.

One ecological influence seems to stand out, however: climate change. For example, some pulses of cortical expansion seem to have occurred in relation to global cooling. That theme runs throughout the book, but climate change affected primates only indirectly. The cooling and drying of the atmosphere changed ecosystems and thereby influenced diet, body size, foraging strategies, predator avoidance, social systems, and much more, so it doesn’t point to any one or two selective factors in a simple way. What’s the scope? This book also says something about the cerebral cortex in its entirety. To appreciate such a broad scope, consider a phrase that sometimes appears in the neuroscience literature: “the cortex and the hippocampus.” That wording implies that the hippocampus is not part of the cerebral cortex, but it is. Evidence from comparative neuroanatomy shows that the hippocampus changed during primate evolution, so it’s part of the story, too. A problem with cortex-wide discussions is that they are sometimes prone to vague generalizations. However, I resist the temptation to relate cortical evolution to abstract notions such as intelligence, advanced cognition, or higher brain function. Evolutionary changes in the cortex certainly influenced all that, but these ill-defined concepts contribute little to understanding what produced the cortex that primates have today. Instead, this book focuses on new and expanded areas that perform advantageous functions more specific than terms like intelligence, advanced

cognition, or higher brain functions can convey. An adage I learned as a graduate student goes like this: “Anatomy tells us nothing, but at least we can be sure of it.” That saying has a nice ring to it, but it’s wrong. Subsequent chapters illustrate something that anatomy tells us: The areas composing the cerebral cortex changed during primate evolution. What’s the audience? Authors can’t control their readership, but I have a specific audience in mind. My goal is to provide a stand-alone resource for neuroscience graduate students and established neuroscientists who have an interest in both cortical evolution and primates. Except for Part V, I have failed if a reader in either of these categories needs to look elsewhere to understand the text or figures. If someone from another field of research, such as biological anthropology, looks at this book, that’s fine, but they’ll go through many chapters before they encounter anything they don’t already know. Such researchers could begin at Chapter 11 without missing much beyond my effort to make selected findings from paleontology, paleoecology, and paleoneurology accessible to neuroscientists. What’s not covered? What follows is a highly selective review; there are many relevant topics this book doesn’t cover. One is evolution per se. This isn’t the place to learn about evolutionary biology because I present only the bare minimum: just what’s needed to understand the text and figures. It also doesn’t cover brain evolution in general; the genetic and molecular basis of cortical evolution; ontogenetic cortical development; the evolution of subcortical structures, such as the striatum, thalamus, or cerebellum; or the evolution of subsystem traits, such as cell types, neurotransmitters, receptors, and so forth. I touch on some of these topics but don’t deal with any of them in detail. It’s not that they’re unworthy—all of them are important—but to limit the book’s length, I focus on other things. Readers in search of a discussion about von Economo cells, metabolic changes in cortical evolution, genetic diversity among primate brains, evolutionary divergence of gene expression, or proteomic comparisons need to look elsewhere. For different reasons, the same goes for advanced topics specific to human evolution, such as hemispheric specialization and language. There’s enough of that material in the literature without rehashing it here. When? Earlier, I said that this book explores why certain cortical areas emerged or enlarged during primate evolution. A huge hint about the “why” is the “when.” If the cortex changed during primate evolution—and it did—a crucial question is: When did changes occur? “When” questions, in turn, raise an issue of scale. In daily discourse, people sometimes use the phrase: “not in a million years.” But a million years isn’t very long in the context of evolution. Instead, it’s the fundamental unit used in discussing and thinking about the topic, so much so that it has its own abbreviation: Ma, short for million years ago (mega-annum). Some authors use the friendlier “mya,” but I might as well be hardcore because that’s one of the smaller hurdles neuroscientists will encounter in this book. In conjunction with “why” questions, “when” questions lead to activities entirely foreign to most neuroscientists: measuring time in Ma, not ms; pondering why there’s no such thing as a monkey when you might see such creatures in the laboratory every day; rowing across a river of “rhines” (platyrrhines, catarrhines, strepsirrhines, and haplorhines); and harboring a morbid taste for bones3 rather than the usual fascination with neural systems. Part I’s title is “What primates are,” and what many primates are is extinct. Besides a few teeth, nothing remains but fossilized bones.4 To understand when the cortex changed, it’s necessary to know something about fossil primates. Why now? In addition to explaining the book’s scope and topics, I should say why I decided to write it now. One reason is that the field of paleoneurology—the study of fossil crania to draw inferences about brain evolution—has advanced considerably in the past decade or so. But it’s a difficult literature for neuroscientists who have ~106 other things to do. (The abbreviation for which is Mc, which stands for megacommitments.) Paleoneurology reveals that, by any measure, early primates had much less cortex than modern species have. This finding prompts the five questions listed in the Overview, expanded a little here: • • • • •

When and how many times did the cortex enlarge in primates? In which ancestral primates did these evolutionary developments occur? What selective pressures influenced cortical expansion? What new cortical areas, if any, contributed to these evolutionary developments? How did cortical evolution in primates compare with what happened in other mammals?

Until relatively recently, no one could provide good answers to any of these questions. Three obstacles precluded progress in the past:

• • •

As recently as the 1990s, uncertainty about the evolutionary relationships among mammals prevented neuroscientists from knowing, with confidence, which modern species are the closest relatives of primates. Studies of endocranial volume and sulcal patterns in fossil species were limited to the few specimens that could yield brain endocasts without risk of damaging the fossils.5,6 Inferences about brain evolution depended almost entirely on comparisons among modern species, using databases that had significant flaws and rarely considered either phylogenetic sampling or the effort devoted to studying each variable.

Evolutionary biologists, paleontologists, and comparative neuroanatomists have made significant progress on all three problems: •

• •

Molecular phylogenies have clarified the relationships among mammals.7,8,9,10 They highlight the importance of tree shrews in understanding the evolution of primate brains, and there’s now a much better understanding of their cortex than existed 10–15 years ago.11,12,13,14 The advent of high-resolution, three-dimensional imaging has led to new information about the size, shape, and sulci of the cortex in extinct species.15,16,17,18,19,20,21,22,23 The development of phylogenetic statistics and improved databases have been major advances.21,23,24,25,26,27,28,29,30,31,32,33

In view of these developments, the time is ripe for a book on cortical evolution in primates. Why not? And why not? Everyone reading this book is a primate, and who doesn’t want to understand the evolution of their own cortex? As neuroscientists, we’re in a better position to appreciate the details of its organization and function than experts in other fields. For example, discussions in primate paleoneurology papers often reveal the assumption that any expansion of the visual cortex must have something to do with an improved ability to perceive and identify things that primates can see. As neuroscientists, however, we know better (or, at least, we should). An equally important role of vision—some might say a more important one—involves the visual guidance of movement, especially in relation to affordances: the available actions based on what an individual can see. Of course, as an author of an entire book on how to pick up a coffee cup,34 I’m more than a little biased. Such predilections aside, paleoneurology is a fascinating field. Take Figure 1.1, for example. It illustrates the cerebral cortex of a primate, but no zoo in the world exhibits this species, and no nature photographer has ever captured its image. In fact, no one has ever laid eyes on one, and no one ever will. It lived ~40–37 Ma,* but we know something about its cortex because of a fossil discovered in Texas. In 1964, a paleontologist named Jack Wilson found it on a ranch near the Big Bend National Park. From a single fossil cranium, paleontologists can estimate the weight of the animal, the volume of its brain, and how much of its brain was neocortex. But even without such knowledge, any halfway-decent neuroanatomist can recognize it instantly as a primate brain.

Figure 1.1 The brain of an extinct primate. Shape of the cortex in a fossil primate, Rooneyia, which lived in North America. Drawing by Mary K.L. Baldwin from images published by E.C. Kirk et al., Cranial anatomy of the Duchesnean primate Rooneyia viejaensis: new insights from high resolution computed tomography, Journal of Human Evolution 74, 82–95, 2014.

Primate cortex is unique in many ways, the overall shape being one. Evidence from comparative neuroanatomy reveals that primate brains also have a different complement of cortical areas35,36 and different transcortical networks.37,38,39 As a result, primates can process and store a more diverse repertoire of neural representations than their nonprimate ancestors could. Primates also have the unique property of maintaining a high density of cortical

neurons as their cortex expanded during evolution,40 which probably contributes to the same advantage. The cortex enlarged so much that it dominates Figure 1.1, as it would any drawing or photograph of a modern primate brain. In fact, many depictions of primate brains consist of nothing but cortex—or nearly so. Paleoneurology can provide estimates of when and in which evolutionary lines such dominance developed. Despite my interest in paleoneurology, I don’t have any expertise in that field, and the same goes for paleontology and ecology. The late historian David McCullough said that he wrote books he would like to read, but couldn’t, because no one had written them. I’ve long wanted to read a neuroscience-friendly monograph on the paleontology, paleoecology, and paleoneurology of primates, one with a discussion about how findings from those fields inform comparative and functional neuroanatomy. By drafting and revising this book, I have finally read one, several dozen times. Why not now? By the question “why not now?”, I mean: Why not get right to the heart of the matter? If I’m hell-bent on revisiting cortical evolution in primates, why not start in the next paragraph? The answer is that discussions of cortical evolution rely on terms and concepts unfamiliar to many neuroscientists. Accordingly, Part I reviews some pertinent terminology (Chapter 2) and taxonomy (Chapter 3), and Part II summarizes primate paleontology, adaptations, and paleoecology. All that introduction seems like a lot, and it is, but it’s important for what follows. Paleoecology, the topic of Chapter 6, is especially valuable. Knowledge about the lives of extinct primates contributes to understanding cortical evolution in a unique way. At various times after they first evolved, primates encountered ecological challenges unlike those faced by any modern species. They lived in habitats that no longer exist, and they had to cope with now-extinct competitors and predators in ever-changing ecosystems. Primates of the past differed from each other in body size, locomotion, dietary preferences, foraging strategies, social systems, circadian patterns, and seasonal variation in behavior, and they differed from modern primates, too. I need to introduce all these topics to make sense of cortical evolution in primates. Then, Part III summarizes a neglected source of data on cortical evolution: fossils. Because they tell us only about external features of the cortex, we need to go deeper—both literally and metaphorically. Accordingly, Part IV harnesses the power of comparative neuroanatomy to examine how cortical maps changed during primate evolution. Finally, Part V explores the selective factors at work. Why try? Given the challenges of the topic, some might wonder whether it’s worth the effort. To many neuroscientists, what happened millions of years ago doesn’t seem pertinent to understanding cortical functions and dysfunctions today. One reason for this attitude is the persistence of discredited ideas about cortical evolution, which devalues the literature. Critics also emphasize the obvious fact that it’s impossible to turn back the clock and run experiments on brain evolution; and still others view inferences on the topic as so uncertain that they should be neglected entirely. In decades past, these were reasonable opinions. Nearly nothing I read about brain evolution41 as a graduate student in the 1970s has withstood the test of time, and a lot of it was clearly nonsense, even then. Nevertheless, reasonable inferences are possible,42 and they contribute to neuroscience for at least three reasons: (1) There’s no way to stop speculation about cortical evolution, and bad ideas about it regularly lead neuroscientists astray; (2) new cortical areas emerged and others expanded at various times in the past, and the advantages they provided to extinct species influence what these areas do today; and (3) for the optimal use of animal models in translational neuroscience, it’s crucial to know how both the model species and humans differ from their most recent common ancestor. Like all brains, those of animal models have a combination of conserved and derived features. Translational neuroscience depends mostly on the former, but derived traits matter, too. A balanced appreciation of both will improve translational neuroscience for the simple reason that any model species will certainly have both. Accordingly, insight into cortical evolution promises to enhance our understanding of neurological and mental health disorders. That’s a prize worth winning, but there’s also the prospect of something more: a vision of ancestral primates, their life and times, and a grasp of why our cortex came to be. Chapter summary In early primates, the cortex was much like that of many other placental mammals, but it grew and added areas over time. The published literature emphasizes three approaches to understanding these momentous evolutionary developments: analyses involving the size of the cortex or its components in modern species, usually in relation to other parts of the body or brain (allometry); cortical mapping studies, also in modern species; and descriptions of cortical volume, shape, and sulcal patterns in extinct primates (paleoneurology). This book attempts to synthesize these three strands of research, and to do so in the context of both primate paleontology, which traces the skeletal and dental evolution of primates, and paleoecology, which describes their life and times. References 1.

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* Throughout this book, older dates appear before newer ones, as in this example: 40–37 Ma, meaning “from 40 to 37 million years ago.” This convention traces developments from the more remote past toward the present in the order they occurred.

2 Compact cladistics Overview Discussions of cortical evolution rely on several terms rarely encountered in the neuroscience literature, some of which have narrower meanings in evolutionary biology than in neuroscience or everyday discourse. Fortunately, a few figures and examples can clarify the terminology enough for a discussion of cortical evolution. In addition, three sets of concepts are crucial to understanding this book: (1) homology, homoplasy, and analogy; (2) a tree versus a ladder as a metaphor for evolution; and (3) old versus new cortical areas. Although the neuroscience literature often mentions evolution, faulty inferences sometimes escape the review and editorial process. A common error involves assumptions about homologies based on similarities alone, which can situate the emergence of an innovative trait too far in the past and therefore in the wrong ecological context. A curious aspect of the theory of evolution is that everybody thinks [they understand] it. —Jacques Monod, Selected Papers in Molecular Biology, Academic Press, 1978

Introduction In this chapter, imaginary animals volunteer to be eaten; a mandible mauls a boat; and an extinct orangutan considers where to sit. But I begin with the epigraph, which highlights something I encountered repeatedly during my career as a neuroscientist: colleagues who expressed (and sometimes published) firmly held opinions about cortical evolution but had little background in evolutionary biology more generally. Their approach was something like attempting to practice neurosurgery without the bother of medical school. Unfortunately, this chapter won’t substitute for a postgraduate education in evolutionary biology. It would be wonderful if I could write a 6,000-word chapter that conveyed the essence of that specialized field to neuroscientists. But I can’t: for so many reasons that listing them alone would break the word budget for this chapter. Fortunately, some selected definitions and a few key concepts should suffice for understanding the text and figures in this book. Taxing terminology What in Hades are clades? For starters, the words “Hades” and “clades” don’t rhyme. The word “clades” rhymes with grades, a term that’s also important for understanding cortical evolution (Chapter 7, “Grades and clades”). Part I of this book addresses what primates are. Among many other things, primates are a monophyletic group, which has two implications: (1) all modern primates have descended from a single extinct species, their last common ancestor (LCA); and (2) no other modern species has descended from that LCA. The concept of a monophyletic group—also known as a clade, a natural group, and a natural taxon—comes from a branch of evolutionary biology called cladistics.

Figure 2.1 Graphical definitions of cladistic terms. The red circle marks the last common ancestor (LCA) of modern locavores, a fictional clade whose members forage only locally. Vertical red bars and labels indicate evolutionary developments that are characteristic of a lineage, including: neophobia (the avoidance of novel foods), neophilia (an attraction to novel foods), phagophilia (a desire to be eaten), and flight. To the right, rounded rectangles bound two kinds of groups: paraphyletic (magenta) and monophyletic (green). The large rounded rectangle also encloses a monophyletic group. Both the stem group of locavores and its crown group include extinct species, marked by daggers (†). The green lines illustrate an adaptive radiation of shmoos, another imaginary group of animals; the magenta lines are for shminks. The black circle marks the LCA of Shminkoshmoos.

Figure 2.1 uses an evolutionary tree to illustrate some terms and concepts from cladistics. It illustrates an imaginary order of mammals called locavores (formally, Locavora),* which forage only locally and avoid novel foods (neophobia). The locavores compose a monophyletic group (or, equivalently, a clade). Among locavores and their closest extinct relatives, species can be classified as members of a stem group or a crown group: stem locavores and crown locavores, respectively. The stem group includes extinct species near the LCA, including those that branched off before its time; the crown group includes modern and extinct species that descended from the LCA. Early members of a group are sometimes referred to as basal, as in the phrase: basal locavores. A sister group is the lineage most closely related to a given clade; for the locavores, it’s the globavores. An outgroup is any closely related lineage used for comparison with a clade, including its sister group. When a clade undergoes a relatively rapid phase of speciation and diversification, its crown species compose an adaptive radiation. The figure illustrates an adaptive radiation of shmoos,† a fictional family of locavores noted for their eagerness to be eaten by people (phagophilia). In addition to several monophyletic groups, Figure 2.1 illustrates a paraphyletic group. It includes an ancestral species and some of its descendants, but not all of them. In this imaginary example, the paraphyletic group includes one shmoo species and three other locavores known as shminks. All four species in the group descend from a common ancestor, but because this ancestor also gave rise to additional shmoos, the four species are not a monophyletic group. This distinction is important because some classifications of primates include paraphyletic groups, such as monkeys, prosimians, apes, and great apes. The fact that they are not monophyletic groups has important implications for understanding cortical evolution. A third kind of group, polyphyletic, consists of a set of species without reference to their LCA. Note that paraphyletic and polyphyletic groups are arbitrary, whereas monophyletic groups are not. There’s no evolutionary principle that will guide a taxonomist about how many shmoos to include in the paraphyletic group enclosed by the magenta line in Figure 2.1. However, it’s clear how many species are in the shmoo group (8), as well as how many are in the shmink group (3). Thus, the shmoos and the shminks are monophyletic groups of locavores: in traditional taxonomic terms, a shmoo family and a shmink family within the locavore order. Because shmoos and shminks are sister groups, together they compose yet another monophyletic group: the Shminkoshmoos. Such inelegant labels are common in cladistics, and you’re a member of one: the Euarchontoglires, as Chapter 3 explains. At its core, cladistics is about the origin of species (speciation), and its main message is this: When the ancestors of a monophyletic group first diverged from the ancestors of its sister group, individuals in the two populations began with nearly identical genotypes and phenotypes. In this case, the LCA of locavores and globavores was a single, interbreeding population of individuals. After their divergence, the locavores developed novel traits over time, including neophobia; the globavores also evolved novel traits, but not that one. It’s tempting to assume that neophobia emerged soon after the divergence of locavores and globavores (also known as a split*), but it could have arisen much later. This point’s important because traits that characterize the cortex of modern primates don’t necessarily date to the origin of primates, and this understanding is fundamental to later chapters. Figures and phylogeny In addition to phylogenetic trees like Figure 2.1, evolutionary relationships are also depicted by cladograms, chronograms, and phylograms. Cladograms only illustrate branching patterns; chronograms plot the time-course of cladogenesis—the origin of clades—on a historical timescale (usually linear); and phylograms provide information about the degree of evolutionary change in some trait. Figure 2.2A is a chronogram; Figure 2.2B is a phylogram. Box 2.2 discusses the

information presented in the figure, but for now it’s the diagrammatic format that’s important. Figure 2.2A indicates, for example, that pygmy marmosets and common marmosets diverged approximately 7 million years ago (abbreviated ~7 Ma). Figure 2.2B illustrates the estimate that after they split, pygmy marmosets decreased in a specific trait, body size, by 69.5%, whereas common marmosets did so by 32.8%.

Figure 2.2 Phyletic dwarfism in callitrichid primates. (A) A chronogram of cebids, a group of New World monkeys. This clade includes the callitrichids: marmosets and tamarins. Red font highlights the two species mentioned in the text. (B) A phylogram of callitrichids indicating evolutionary changes in adult body mass. The endpoint of each line (black circle) corresponds to the mean body size for each species, which can be read off the x-axis. The percentages are changes in body mass since the most recent divergence. Two species became slightly larger than their immediate ancestors. However, most callitrichids show an evolutionary trend toward smaller body size. Abbreviations: LCA, last common ancestor; g, grams; Ma, million years ago. Adapted from S.H. Montgomery and N.I. Mundy, Parallel episodes of phyletic dwarfism in callitrichid and cheirogaleid primates, Journal of Evolutionary Biology 26, 810–19, 2013.© 2013 S. H. Montgomery, N. I. Mundy. Journal of Evolutionary Biology. European Society For Evolutionary Biology

Keystone and diagnostic traits The simplest way to identify a clade relies on keystone traits, which are features of a monophyletic group that evolved only once and were present in their LCA. Molars and hair serve as examples. Mammals have them; other animals don’t. Thus, molars and hair are among the keystone traits of mammals, and they serve as diagnostic traits, which identify an animal as a mammal: living or extinct. Unfortunately, primates don’t have a keystone trait that distinguishes them from all other mammals.

Forward-facing eyes exemplify this point. As Chapter 5 explains, one of the most important developments during the evolution of primates was a reorientation of the eyes toward the front of the head. But some extinct species usually classified as primates, called plesiadapiforms, lack this trait, and nonprimate mammals such as carnivores also have forward-facing eyes. Therefore, forward-facing eyes don’t identify a modern or extinct mammal as a primate. This trait is simply a similarity among certain species, and attempts to infer evolutionary relationships based on similarities alone often fail. The reason is that similarities arise for many reasons, only one of which is descent from a common ancestor. Similarities also arise from homoplasy. Homology, homoplasy, and analogy What the hell is homoplasy? I have noticed that when the talk turns to homoplasy, quite a few neuroscientists feel the need to be somewhere else. A degree of aversion is understandable because discussions invoking this term are often long on scolding and short on explanation. But the long and short of it is this: Homoplasy is evolution’s way of fooling neuroscientists into thinking that a trait’s homologous when it’s not. Evolution is not malevolent, but it often produces the same sorts of changes in response to similar selective pressures and developmental constraints. When it does, homoplasies can look a lot like homologies. Knowing the difference matters a lot for understanding cortical evolution. Homology The term homology refers to traits inherited by two or more species from a common ancestor. In the present context, the word “trait” is synonymous with character and feature, all of which apply to anything that reproduces from one generation to the next, including behaviors and cognitive capacities. Two additional terms describe traits: primitive and derived. Notwithstanding its meaning in everyday language, in biology “primitive” has a narrow and precise meaning: resemblance to an ancestral condition. The word “derived” refers to divergence from that condition. Jaws serve as an example of homology. The earliest vertebrates had mouths that lacked jaws. Consequently, a primitive vertebrate mouth is jawless. Two groups of vertebrates—lamprey and hagfish—have retained a primitive mouth, which explains one of their collective names: agnathan (no jaw). (Their other name, cyclostome, means round mouth.) In contrast, the derived form of the vertebrate mouth is one supported by cartilage or bone within distinct upper and lower jaws. This trait is shared by a clade of animals called gnathostomes (jaw mouth). The mouth is homologous among all vertebrates, but in agnathans it has retained a primitive, jawless state, whereas in gnathostomes it has a derived form (in fact, a wide variety of derived forms). Primitive traits often reflect a generalized condition, and derived traits are commonly specializations, but neither is necessarily the case. For example, a lineage can lose a diagnostic trait during evolution. When that happens, the derived condition involves the loss of a specialization rather than the development of one. More importantly, the idea that a trait is either primitive or generalized doesn’t imply that it’s simple or poorly adapted to its function. Take feet, for instance. A vertebrate clade called tetrapods (four feet) includes all reptiles, birds, amphibians, and mammals. The generalized state of tetrapod limbs is to have five digits on each one. Some early tetrapods had more than five digits on each limb, but because the LCA of modern tetrapods had five, its early descendants passed along a genetic program that produces five-digit feet. Thus, a five-digit foot is the generalized and primitive condition for tetrapod limbs. Such feet are neither simple nor poorly adapted to their functions in movement and posture. Later in evolution, all manner of variations emerged as specializations. For instance, horses and other perissodactyls (odd-toed ungulates) run on a single digit, with the others absent or vestigial; pigs and other artiodactyls (even-toed ungulates) use two digits to form a stable and sturdy hoof. Such feet are specialized and derived. Homologies come in two fundamental types: plesiomorphies and synapomorphies.* Although the –morph part of these terms refers to anatomy, they can apply to any inherited traits, including physiological, behavioral, and cognitive ones. As one example, all mammals have a spinal cord by virtue of inheritance from the LCA of mammals, so the spinal cord is homologous among mammals. However, in contrast to hair and molars, the spinal cord is not a mammalian innovation. It originated long before the advent of mammals, in an ancestor of the first vertebrates. Accordingly, the spinal cord is a mammalian plesiomorphy, which does not distinguish mammals from other vertebrates. Plesiomorphies are also known as shared primitive traits. Synapomorphies and apomorphies are also known as shared derived traits. They are evolutionary innovations that emerged in an ancestral species and characterize a clade. More than 530 Ma, as the first vertebrates and their immediate ancestors evolved, the spinal cord was a synapomorphy, which all vertebrates have inherited. Much later, when mammals first appeared, the spinal cord was a plesiomorphy. In phylogenetic trees, synapomorphies are commonly marked to indicate the lineages in which they emerged. Figure 2.1 shows that the locavores, as noted earlier, developed a diagnostic trait: an avoidance of new foods (neophobia). Because shmoos are locavores, they also exhibit neophobia (so there’s no need to mark that trait on the diagram), but they differ from other members of the same clade because they developed their own synapomorphy: a desire to be eaten by people (phagophilia). Quite often, some members of a clade lose synapomorphies. When this happens, it’s common to say that they lost the trait secondarily. In Figure 2.1, the top species of locavores secondarily lost neophobia and reverted to the ancestral condition: neophilia. This species remains a locavore by virtue of descent, even though its foraging behavior more closely resembles that of the globavores. A real-world example of this principle involves the tetrapods mentioned earlier. Snakes and whales remain tetrapods despite being four feet short of what the name “tetrapod” implies. In cladistics, it’s ancestry that matters. Synapomorphies can serve keystone traits if they only evolved once; otherwise, they’re the diagnostic traits mentioned earlier. Because molars only evolved once during evolution, they unambiguously identify a modern animal or a fossil as a mammal. Other traits evolved more than once. Complex eyes with a roughly spherical shape and a lens, for example, evolved independently in both vertebrates and cephalopods (squid and octopuses). Accordingly, these traits alone don’t identify an animal as either a vertebrate or a cephalopod.

Homoplasy and analogy The concept of homology can be tricky. A simple reason for misunderstanding homology is that, like many words, it has several meanings. A deeper reason is that homology refers to ancestry and—here comes the tricky part—not to similarities. Biological similarities fall into one of two buckets: homologies and homoplasies. Homoplasy occurs because of parallel evolution, convergent evolution, and the loss of derived traits (phylogenetic reversals). All three evolutionary processes create similarities. Parallel evolution produces similar traits in separate lineages that change in much the same way over time, from similar initial conditions; convergent evolution produces similar traits from different prior conditions; and the loss of derived traits can produce similarities via reversion to a primitive, often generalized condition. An overarching term, independent evolution, applies to all the processes that produce homoplasy, and sometimes the term “convergent evolution” is used in this broad sense. Homology also contrasts with analogy. In everyday language, an “analogy” is any kind of similarity or correspondence, but in biology its meaning is much more specific. An analogy is a structure that performs a similar function in two or more species but evolved independently. Accordingly, homology and analogy are mutually exclusive; either a trait has been inherited from a common ancestor or it hasn’t. Because homology refers to inheritance and ancestry, any similarity that developed in other ways can’t be a homology. Analogies result from homoplasy (or, equivalently, from independent evolution). In my experience, neuroscientists tend to harbor undue skepticism about convergent evolution and other forms of homoplasy. In contrast, evolutionary biologists and paleontologists develop a starkly different intuition. According to one such expert, Susan Cachel1 (pp. 97–8), “homoplasy is rampant in the animal world” and “convergent evolution is ubiquitous.” Primates are no exception; in fact, these two quotations come from a book devoted entirely to primates. I repeat them in subsequent chapters because, too often, neuroscientists assume that a trait is homologous simply because two modern species share it. Because homoplasy is “ubiquitous” and “rampant,” such assumptions are often invalid. In part because of the complexity of neural systems, it strikes many neuroscientists as unlikely that such intricacies could evolve more than once, which leads to many mistakes. One final caution: The neuroscience literature contains numerous references to “functional homology.” Properly used in neuroscience, a “functional homology” (or, equivalently, a “homologous function”) would correspond to some neural function that two or more species inherited from a common ancestor, like any other homology. However, some neuroscientists use this phrase in a different way, in which homology serves (wrongly) as a synonym for similarity. If a neural structure has similar functions, physiological properties, or connections across species, then some neuroscientists call that structure a functional homology. When used this way, the claim of homology is invalid because similarities can also arise from homoplasy. Equally invalid are arguments against homology based solely on dissimilarities. These misconceptions are understandable, and for a simple reason: homologous structures often have similar functions, which leads to the mistaken impression that homology and functional similarities are obligatorily coupled. Two examples, again involving jaws, illustrate how utterly uncoupled homology and function can be: (1) Jaws, which function in feeding, arose from the first brachial arch in jawless ancestral species. Brachial arches, also known as pharyngeal arches, have a respiratory function in fish, but in gnathostomes the front-most arch adapted to function as mechanical support for biting things. The shark in the movie Jaws didn’t respire through its mandibular arch; it bit through a boat with it. Jaws and brachial arches are homologies that have completely different functions. (2) As jaws evolved in land animals, they came to consist of several articulated bones. In the ancestors of mammals, as in modern reptiles, two components of the jaw are called the articular and quadrate bones. During the evolution of mammals, these bones detached from the jaw when the teeth-bearing part of the lower jaw expanded and formed a new joint with the skull. Eventually, the articular and quadrate morphed into the incus and malleus, respectively: two tiny bones in the mammalian middle ear. (The same thing happens during embryonic development.) Together with the stapes, they compose the ossicles, which have an auditory function, specifically the amplification of high-frequency air-borne sounds. Thus, bones that originally functioned in feeding went through an evolutionary transition in which they contributed to both feeding and hearing.2 These structures now empower mammals to hear high-pitched sounds, and they no longer play any role in feeding, which is the function of their homologs in reptiles. Such sounds are important for decoding speech, as anyone (like me) with a high-frequency hearing impairment can attest. Accordingly, the evolution of middle-ear ossicles in stem mammals was an exaptation for human speech and language: an evolutionary development that established the foundation for future ones. Although these are just two examples, jaws and ossicles are not biological curiosities; homologous structures often change function over time and retain few, if any, similarities. Certain fish developed homologs of shoulders, elbows, and wrists long before their descendants moved onto land3 and long before the land contained sufficient nutrients to support amphibious or fully terrestrial animals. Likewise, a group of dinosaurs had feathers and hollow bones while they led a terrestrial life, well before these traits supported flight in their avian descendants.4 A list of homologs that changed function during evolution could go on for many pages because that’s the way new functions evolve: from a structure in ancestral species that did something different. For readers interested in a deeper and more nuanced discussion of homologies, Striedter and Northcutt5 discuss variants on that concept, such as field homologies, transformational homologies, serial homologies, etc. However, none of them are relevant to this book. A concrete example can help consolidate the key concepts. Wings have buttressed traditional explanations of homology, analogy, and homoplasy from time immemorial, but primates only have wings in Wizard of Oz. Consider something that primates do have, instead: bodies. Box 2.1 summarizes the range of body sizes observed in extinct and modern primates. For the sake of discussion, consider bodies over 1 kg as large and those under 1 kg as small. Box 2.1 Primate size range

The size range for modern primate species is 0.03–150 kg. Pygmy mouse lemurs (Microcebus myoxinus) weigh 25–50 grams in adulthood, and their infants weigh so little that mothers carry them around in their mouths.6 The smallest extinct primates probably had a similar body size or weighed even less. One estimate for an extinct primate from ~45 Ma is as low as 12 grams.7 That might seem impossibly low, but several adult mammals weigh much less. Etruscan pygmy shrews (Suncus etruscus), for example, weigh 2–3 grams in adulthood, and bumblebee bats (Craseonycteris thonglongyai) weigh slightly less. At the other extreme, the body-size prize for modern primates goes to males of a gorilla species (Gorilla beringei), which average 150 kg but can reach 200 kg. An extinct orangutan from Southeast Asia (Gigantopithecus blacki) might have weighed as much as 400 kg, although estimates of 200–300 kg are more common.8 (If you ever pass through Oneonta, New York, be sure to visit a statue of one on the campus of Hartwick College.) These behemoths evolved ~2 Ma and didn’t become extinct until ~300,000 years ago.8 Archaic humans, specifically Homo erectus of the middle Pleistocene, probably encountered these gargantuan orangutans. Modern anxieties about the mythical Bigfoot or Yeti pale in comparison with the reality of a 200–400 kg ape. The humans in their ecosystem probably exercised considerable discretion when encountering these vast vegans, which probably dined on bamboo and durian fruit. Many readers will be familiar with the riddle, supposedly humorous, about where an 800-pound (360 kg) gorilla can sit, which is “anywhere it wants.” Gorillas rarely reach that size, however, and neither did Gigantopithecus. All the same, Gigantopithecus blacki probably chose its resting places without much interference from humans or any other species. Sadly, their freedom of action didn’t prevent extinction of the species. Among other traits, a large body size distinguishes anthropoids—monkeys, humans, and apes—from prosimians such as tarsiers and lemurs. With an exception that Box 2.2 explains, all anthropoids have large bodies, and they come in two groups: (1) New World anthropoids, which live in South and Central America; and (2) Old World anthropoids, which come from Africa and Asia. Because both New World and Old World anthropoids have large bodies, it might seem obvious that both groups descend from a large-bodied ancestor. If so, then their large bodies would be a homologous trait. By considering only modern species, no one would suspect that large bodies evolved independently in these two groups of anthropoids. But the fossil record shows that earliest anthropoids weighed ~30–300 grams9 and that body size increased independently in New World and Old World anthropoids.10, 11 Accordingly, the large body size of anthropoids is an example of homoplasy, not homology, and Chapter 8 explains that a similar conclusion applies to the size of anthropoid brains and cortex. Box 2.2 Phyletic dwarfs The main text says that all adult modern anthropoids exceed 1 kg, with an exception. The exception includes a clade of New World monkeys, the callitrichids: marmosets and tamarins. Callitrichids diverged from other cebid monkeys ~20 Ma, and Figure 2.2A depicts 11 modern species that descended from their LCA. Phylogenetic analysis shows that, to varying extents, the different species of marmosets and tamarins have secondarily decreased in body size.12, 13 The reversion to a previous character state, small body size, produced what’s called phyletic dwarfs. Figure 2.2B illustrates these decreases in body size. Phylogenetic analysis leads to the inference that the ancestors of marmosets and tamarins weighed ~1.2–1.3 kg, and fossil evidence supports that estimate.14 The callitrichids thus exhibited a long-term evolutionary trend toward smaller body size. Trees and scales In contemporary biology, a branching tree serves as the main metaphor for evolution.15, 16 It captures two of the principal themes of cladistics: speciation and diversification. An older view, which dates to ancient Greek philosophers, stresses ascent, progress, and advancement. It arranges biological organisms on a linear scale: the Great Chain of Being. According to one version, humans sit atop other biological organisms, “beasts” and plants, in that order. Above humans are metaphysical entities such as angels and gods; beneath plants come flames and stones, among other inanimate objects. In time, the supranatural and inanimate objects went by the wayside, leaving only biological organisms or sometimes only animals. Primates ranked at the top of the animal kingdom, with humans above the primates. The culmination of evolution, according to that view, was the production of humans. The Great Chain of Being has many other names, including the phylogenetic scale and the phyletic scale, but it’s best known as the scala naturae: the scale of nature. As mentioned in the Preface, the scala naturae is based on the similarity of other animals to humans. In addition to ranking primates highest on the ladder of life, the scala naturae once seemed to apply within the primate order. Chapter 11 (“Tree shrews find their tree”) explains why tree shrews were once classified as primates, as well as why they seemed to be at the bottom of the primate scale. Lemurs, lorises, and galagos ranked next; then came higher primates in a series that “advanced” or “progressed” from tarsiers to monkeys, lesser apes, great apes, and humans, in that order.17, 18 Modern biology rejects the idea of evolutionary progress and advancement, along with higher and lower rankings. The scala naturae seems silly today, but bad ideas die hard.* To find evidence that the scala naturae survives, simply read page 5 of the 8th edition of the Guide for the Care and Use of Laboratory Animals (©National Academy of Sciences, 2011). As Striedter and Northcutt5 pointed out, it requires that researchers substitute “animals such as vertebrates with animals that are lower on the phylogenetic scale.” According to the National Academy of Sciences, an Institutional Animal Care and Use Committee is compelled to consult the mythical phylogenetic scale to decide whether to approve a research proposal. Likewise, when a former director of the National Institutes of Health banned biomedical research on chimpanzees, his reasoning relied in large part on an implicit acceptance of the scala naturae: a misconception also common in the burgeoning bioethics industry.

It’s easy to find echoes of the scala naturae in the neuroscience literature, and it’s long past time for reviewers and editors of neuroscience journals to put a stop to it. Chapter 7 (“Grades and clades”) includes a delightful quotation from Stephen Jay Gould, who via indirection—and with exquisite eloquence and tact—characterized such views as moronic as long ago as the 1970s. Instead of a linear scale, biologists now understand that evolution led to a diverse, tree-like array of descendants from a universal common ancestor. Old and new areas The cerebral cortex of primates is a mosaic of old and new cortical areas: some, like the hippocampus and piriform cortex, emerged in stem amniotes (the ancestors of modern birds, reptiles, and mammals); others, such as the primary auditory and somatosensory areas, arose with the origin of mammals; still others appeared during primate evolution (Chapters 11–13).19 Other mammalian lineages also have specialized cortical areas, with echolocating bats serving as a useful case study.20,21,22 The concept of a new area doesn’t imply that it lacks similarity with old areas or that it arose de novo. One idea about how new cortical areas evolve draws on the concept of expansion and differentiation.23 According to this idea, after an area expands, part of it undergoes more modifications than the other, so it makes sense to consider the more-changed part as a new area and the less-changed part as an old area. New and old areas have many properties in common, including connectivity patterns, but they don’t have an identical set of connections, so they don’t perform precisely the same functions. The expansion of areas can lead to enhanced and innovative neural representations. When sufficient differences accrue in a given region, a new area is born. For some parts of the cortex, it’s probably better to concentrate on the representations rather than discrete cortical fields, which are often difficult to define and may owe more to aping previous authorities than to any valid neuroscientific data. Despite these caveats, many discrete cortical areas can be defined rigorously, most convincingly when based on complete topographic maps: retinotopic, cochleotopic, or somatotopic. The classification of cortical areas as old or new relates to the concept of homology. As applied to the neocortex of mammals, homologs are the old areas, some of which can be traced to the earliest mammals. New areas evolved later in a particular clade and are specializations. In cladistic terms, old areas are plesiomorphies and new areas are synapomorphies. These concepts have some important nuances, and the primary motor cortex (M1) provides an example of two of them: •

•

The designation of an area as new depends on the reference group. M1 first emerged in ancestral placental mammals.24 When considering mammals as a reference group—including placental, marsupial, and monotreme mammals—M1 is a new area found only in placental mammals. In other words, M1 is new (synapomorphic) to placental mammals and, according to this view, has no homolog in either marsupial or monotreme mammals. However, when considering primates as a reference group, M1 is an old (plesiomorphic) area inherited from the LCA of placental mammals. Old cortical areas can develop new features or lose old ones, and new cortical areas can project to phylogenetically old structures. For example, a part of M1 in anthropoid primates has more and stronger projections to α-motor neurons than M1 does in other mammals.25 This finding doesn’t, in itself, provide convincing evidence of a new cortical area; and an axonal projection to a phylogenetically old target says almost nothing about whether an area is old or new.

Chapter summary A monophyletic group, also known as a clade, consists of an LCA, all its descendant species, and only its descendants. This kind of group is of fundamental biological significance because all organisms are part of a phylogenetic tree, which consists of related monophyletic groups. Other kinds of groups, called paraphyletic or polyphyletic, can provide economy of reference and are often useful for this purpose. In addition to terms from evolutionary biology, several concepts are central to any discussion of cortical evolution in primates. Traits inherited by descendant species from their LCA are homologies, a concept that contrasts with analogies and homoplasies, which refer to similarities that evolved independently. Because of homoplasy, it’s often invalid to conclude that a structure or function “goes back” to a remote common ancestor simply because two or more modern species have it. The concept of homology is essential to this book because by identifying the cortical areas that are homologous in primates and other mammals (plesiomorphies) we simultaneously discover which areas are primate innovations (synapomorphies, also known as specializations). Knowledge about when specializations arose and in which kinds of primates it happened provides irreplaceable insight into their functions, and, even if it didn’t, such information is central to understanding cortical evolution in primates. In addition to the vocabulary introduced in this chapter, understanding cortical evolution in primates depends on another set of terms. As the next chapter says, primates are many things, and as such, they have many names. References 1. 2. 3. 4.

Cachel, S. Fossil Primates (Cambridge University Press, Cambridge, 2015). Wang, J., Wible, J.R., Guo, B., Shelley, S.L., Hu, H., & Bi, S. A monotreme-like auditory apparatus in a Middle Jurassic haramiyidan. Nature 590, 279–83 (2021). Clack, J.A. Gaining Ground: The Origin and Evolution of Tetrapods (Indiana University Press, Bloomington, MD, 2002). Larson, D.W., Brown, C.M., & Evans, D.C. Dental disparity and ecological stability in bird-like dinosaurs prior to the end-cretaceous mass extinction. Current Biology 26, 1325–33 (2016). 5. Striedter, G. & Northcutt, R.G. Brains Through Time: A Natural History of Vertebrates (Oxford University Press, New York, 2020). 6. MacDonald, D.W. Encyclopedia of Mammals (Oxford University Press, Oxford, 2006). 7. Gebo, D.L., Dagosto, M., Beard, K.C., & Qi, T. The smallest primates. Journal of Human Evolution 38, 585–94 (2000). 8. Zhang, Y. & Harrison, T. Gigantopithecus blacki: a giant ape from the Pleistocene of Asia revisited. American Journal of Physical Anthropology 162 Suppl 63, 153–77 (2017). 9. Bajpai, S., Kay, R.F., Williams, B.A., Das, D.P., Kapur, V.V., & Tiwari, B.N. The oldest Asian record of Anthropoidea. Proceedings of the National Academy of Science USA 105, 11093–8 (2008). 10. Seiffert, E.R., Tejedor, M.F., Fleagle, J.G., Novo, N.M., Cornejo, F.M., Bond, M., de Vries, D., & Campbell, K.E. Jr. A parapithecid stem anthropoid of African origin in the Paleogene of South America. Science 368, 194–7 (2020). 11. Silvestro, D., Tejedor, M.F., Serrano-Serrano, M.L., Loiseau, O., Rossier, V., Rolland, J., Zizka, A., Höhna, S., Antonelli, A., & Salamin, N. Early arrival and climatically linked geographic expansion of New World monkeys from tiny African ancestors. Systematic Biology 68, 78–92 (2018). 12. Ford, S.M. Callitrichids as phyletic dwarfs, and the place of the callitrichidae in platyrrhini. Primates 21, 31–43 (1980).

13. Martin, R.D. Goeldi and the dwarfs: the evolutionary biology of the small New World monkeys. Journal of Human Evolution 22, 367–93 (1992). 14. Porter, L.M., de la Torre, S., Pérez-Peña, P., & Cortés-Ortiz, L. Taxonomic diversity of Cebuella in the western Amazon: molecular, morphological and pelage diversity of museum and free-ranging specimens. American Journal of Physical Anthropology 175, 251–67 (2021). 15. Baum, D.A. & Smith, S.D. Tree Thinking: An Introduction to Phylogenetic Biology (Roberts, Greenwood Village, CO, 2012). 16. Nunn, C.L. The Comparative Approach in Evolutionary Anthropology and Biology (University of Chicago Press, Chicago, IL, 2011). 17. Le Gros Clark, W.E. The Antecedents of Man (Edinburgh University Press, Edinburgh, 1959). 18. Romer, A.S. Vertebrate Paleontology (University of Chicago Press, Chicago, IL, 1966). 19. Preuss, T.M. & Goldman-Rakic, P.S. Myelo- and cytoarchitecture of the granular frontal cortex and surrounding regions in the strepsirhine primate Galago and the anthropoid primate Macaca. Journal of Comparative Neurology 310, 429–74 (1991). 20. Suga, N., Yan, J., & Zhang, Y. Cortical maps for hearing and egocentric selection for self-organization. Trends in Cognitive Science 1, 13–20 (1997). 21. Fitzpatrick, D.C., Suga, N., & Olsen, J.F. Distribution of response types across entire hemispheres of the mustached bat’s auditory cortex. Journal of Comparative Neurology 391, 353–65 (1998). 22. Kossl, M., Hechavarria, J., Voss, C., Schaefer, M., & Vater, M. Bat auditory cortex: a model for general mammalian auditory computation or special design solution for active time perception? European Journal of Neuroscience 41, 518–32 (2015). 23. Krubitzer, L.A. & Seelke, A.M. Cortical evolution in mammals: the bane and beauty of phenotypic variability. Proceedings of the National Academy of Science USA 109 Suppl 1, 10647–54 (2012). 24. Kaas, J.H. The evolution of brains from early mammals to humans. Wiley Interdisciplinary Reviews: Cognitive Science 4, 33–45 (2013). 25. Rathelot, J.A. & Strick, P.L. Subdivisions of primary motor cortex based on cortico-motoneuronal cells. Proceedings of the National Academy of Science USA 106, 918–23 (2009).

* Locavores shouldn’t be confused with locovores, a term for people whose dietary preferences impel them to describe the contents of their digestive systems to anyone within earshot. † Shmoos and shminks are mythic creatures that once populated a comic strip called Li’l Abner by Al Capp. * Unlike splits that occur between two humans, after this kind of split interbreeding can continue to some extent, at least for a while. On second thought, perhaps it’s not so different after all. * Because of an unfortunate coincidence, neuroscientists scanning the word synapomorphy might think, at least for an instant, that it has something to do with the structure of synapses. The word “synapse” comes from the Greek for join (hapsis) together (syn); “synapomorphy” derives from Greek words meaning together (syn) in moving away from (apo) an [ancestral] form (morph). * In neuroscience, bad ideas never die; they skulk in the background until enough new researchers have entered the field and others have forgotten the evidence that discredits them. In time, they come back into vogue like fashion disasters from decades past. For examples, examine the literature on the medial temporal lobe memory system, the role of the orbitofrontal cortex in behavioral inhibition, and the working memory theory of the prefrontal cortex.

3 Present primates Overview Primates are many things. Primates are Euarchontans, which means that they are more closely related to tree shrews and colugos (also known as flying lemurs) than to other mammals. Primates are also Euarchontoglires, which means that their next closest relatives are rodents and lagomorphs (rabbits and their relatives). Primates are best understood in the context of these relationships: knowledge that strengthens inferences about cortical evolution. Primates are also one of two kinds of “rhines”— strepsirrhines or haplorhines—along with being members of other clades: anthropoids (also known as simians), platyrrhines (New World monkeys), catarrhines (Old World anthropoids), cercopithecoids (Old World monkeys), hominoids (apes and humans), hominids (great apes and humans), and hominins (modern humans and extinct relatives such as australopithecines). Primates are diverse, and primates are plural; a common phrase—“the primate”—refers to a cultural construct that has no corporeal reality. Taxonomy is described sometimes as a science and sometimes as an art, but really it’s a battleground. —Bill Bryson, A Short History of Nearly Everything, 2003, Broadway Books, New York Taxonomy is often regarded as the dullest of subjects, fit only for mindless ordering and sometimes denigrated within science as mere “stamp collecting” (a designation that this former philatelist deeply resents). —Stephen Jay Gould, Hen’s Teeth and Horse’s Toes: Further Reflections in Natural History, 1983, W. W. Norton, New York, p. 72

Introduction In this chapter, tarsiers become enraged at the company they keep; tree shrews turn out to be more important than even they knew; and more than 400 words are wasted on a ridiculous rant about the word “the.” But I begin with a few words about taxonomy. Taxing taxonomy Any discussion of cortical evolution requires at least a touch of taxonomy, and as the epigraphs of this chapter show, this field provokes two reactions. Experts in biological systematics have waged wonky wars over the classifications of organisms and the labels attached to them. Specialists care deeply about such matters. Other scientists, however, sometimes view taxonomy as fit for school-age science-fair projects and little else. Fortunately, cladistics has changed the nature of taxonomy. Its system of classification depends on descent from a common ancestor, and it’s been a major advance in biology. As a result of the cladistics revolution, several aspects of traditional taxonomy have gone by the wayside in recent decades. Generations of biology students memorized the classification of organisms in terms of kingdoms, phyla, classes, orders, families, genera, and species. Two mnemonic aids helped: “kings and pharaohs can order families going south”; and “King Philip came over from Genoa, Spain.” Perhaps because Genoa is not in Spain, but mainly because biologists found a sevenlevel hierarchy inadequate, students also needed to learn an unwieldy assortment of intermediate classifications, including subphyla, infraphyla, superclasses, subclasses, infraclasses, cohorts, superorders, grandorders, mirorders, suborders, infraorders, parvorders, superfamilies, subfamiles, tribes, subtribes, and subgenera. Mercifully, contemporary biology has transcended this taxonomic torture. Ancestry and phylogenetic relationships matter more than any system of classification. Accordingly, no one needs to stress about whether the taxon Cephalochordata is an infraphylum, a subphylum, or a superclass. The knowledge that all cephalochordates have the founding chordate as an ancestor, as do all vertebrates, is more meaningful. The ancestral chordate had a neural tube, as all chordates do, at least at some point during their development. By descent, all vertebrates have a neural tube, too, although we call it something else: the central nervous system, comprising the brain, retina, and spinal cord. These facts have important implications for neuroscience; the classification of cephalochordates as a subphylum means much less. Classifications like suborder, superfamily, family, etc. designate somewhat arbitrary levels of phylogenetic trees, and despite their persistence, it’s nearly pointless to debate whether a group deserves one or another of these labels. Quite often, plain language conflicts with the strictures of formal taxonomy. In the specialty literature, naming conventions create clarity where ambiguity might otherwise thrive. For instance, the distinctions among Hominoidea, Hominini, Hominidae, Homininae, Hominina, and Hominine convey a unique form of information. Unfortunately, few neuroscientists glean much from these terms other than confusion among them. With such readers in mind, I only use three of those six terms—hominoid, hominid, and hominin—with two exceptions, both involving figures in Chapter 14. A reference figure near the front of this book (p. xx) should suffice to keep the names and relationships straight enough to follow subsequent chapters. For readers unfamiliar with primate taxonomy, I suggest bookmarking or printing that figure. (One reader of a draft manuscript suggested ripping it out of the book, which seems a bit extreme to me, but it’s your book. Readers of electronic versions have better options.) Principal primate clades Among other things, primates are Euarchontans and Euarchontoglires. The latter combines Euarchonta and Glires, like the Shminkoshmoos of Chapter 2 (Figure 2.1). The ancestor of all Glires split into rodents and lagomorphs (rabbits, hares, and pikas), and the ancestral Euarchontan eventually gave rise to tree shrews (Scandentia), colugos (Dermoptera, also known as flying lemurs), and Euprimates, sometimes known by the mellifluous phrase: primates of modern aspect.1 This expression

seems to imply that early Euprimates resembled modern species closely, but as Chapter 7 explains, it doesn’t apply very well to their cortex. Trees with tree shrews In later chapters, the place of tree shrews on the Euarchontan tree becomes important. W.E. Le Gros Clark* recognized the affinity of tree shrews and primates nearly a century ago,2 and he wasn’t the first anatomist to do so. But until the turn of the twenty-first century, evolutionary biologists considered elephant shrews, tenrecs, and bats to be as closely related to primates as tree shrews—or more so.3 Chapter 11 (“Flying primates, feathered apes”) explores this history in more detail, but for now it’s enough to know that Figure 3.1A illustrates the understanding of primate relationships about a quarter century ago.

Figure 3.1 Differing views of primate relations. (A) The understanding of relationships among mammalian orders before the advent of molecular phylogenetics. Colored type highlights the three Euarchontan orders. (B) Two contemporary views of primates and our closest relatives. (A) Adapted from T.J. Halliday, P. Upchurch, and A. Goswami, Resolving the relationships of Paleocene placental mammals, Biological Reviews of the Cambridge Philosophical Society 92, 521–50, 2017. (B, top) Adapted from J.E. Janečka et al., Molecular and genomic data identify the closest living relative of primates, Science 318, 792–4, 2007. (B, bottom) Relationships from M.A. O’Leary et al., The placental mammal ancestor and the post-K–Pg radiation of placentals, Science 339, 662–7, 2013.

The field of molecular phylogenetics, combined with traditional comparative morphology, has now established the relation of tree shrews to other placental mammals and primates, albeit with a few remaining uncertainties.4,5,6 Figure 3.1B presents two possibilities for evolutionary relationships among Euarchontoglires. Tree shrews and colugos compose the Sundatheria, named for the Sunda Islands (Indonesia, Borneo, Brunei, and Malaysia). One possibility is that colugos and tree shrews have a closer relationship to each other than to primates.7, 8 If that’s the case, then the sundatherians are a monophyletic group, as illustrated by the bottom tree in Figure 3.1B, and they would be the sister group of primates. Alternatively, it’s possible that colugos and primates have the closer relationship,9, 10 as illustrated by the top tree in Figure 3.1B. If so, then primates and colugos compose a monophyletic group called Primatomorpha, and tree shrews are the sister group of primatomorphs. On balance, current evidence favors the primatomorph option.6 However, this uncertainty has almost no impact on contemporary neuroscience because so little is known about colugo brains. Unfortunately, this is unlikely to change anytime soon because, as endangered species, they are unavailable for research. Among mammals that neuroanatomists have studied in sufficient detail, tree shrews are most closely related to primates. Accordingly, a comparison of primates and tree shrews is crucial to understanding cortical evolution in primates. Cortex, comparisons, classifications, and confidence To understand how molecular phylogenies have changed the nature of comparative neuroanatomy, consider both the phylogenetic tree in Figure 3.1A, from the 1990s, and the more comprehensive version, Figure 3.2, from the 1980s. They include many mistakes and acknowledged gaps in knowledge, but the way I see it they aren’t so wrong. By and large, taxonomies prior to the advent of molecular phylogenetics captured a fair number of the relationships among placental mammals. The problem was that—even where they were correct—missing data and conflicting evidence undermined any confidence in them. Note the disconnected origin of tree shrews in Figure 3.2; taxonomists of the era weren’t quite sure how they fit in.

Figure 3.2 Mammalian evolutionary tree before molecular phylogenies. A view of the relationships among mammalian orders from the early 1980s. Blue lines highlight the lineages now classified as Euarchontoglires. Adapted from D. Macdonald, ed., The Encyclopedia of Mammals, Facts on File, New York, New York, 1984.

Despite their shortcomings, late-twentieth-century phylogenies recognized the relationship among rabbits, rodents, colugos, tree shrews, and primates: now known as Euarchontoglires. However, they wrongly thought that other orders were also closely related to primates. Among them were elephant shrews and bats, as illustrated in Figures 3.1A and 3.2. Another was a now-defunct order called insectivores (Insectivora) or basal insectivores in the literature of that era (Figure 3.2). Molecular phylogenies have revealed that the order Insectivora, which included hedgehogs, tenrecs, and a number of moles and shrews, is a polyphyletic group drawn from several distantly related lineages, which either retained or developed similar traits in widely separated geographic locations. For example, tenrecs, elephant shrews, and golden moles evolved in Africa; in parallel, hedgehogs and a wide variety of shrews and moles evolved in the northern continents. Names like shrew, mole, and insectivore attest to a generalized mammalian morphology, but they don’t indicate a close phylogenetic relationship.

Cladists call such groups wastebasket categories because they are, in a sense, what’s leftover after they recognize the clearcut monophyletic groups. Because of uncertainties about mammalian phylogeny, comparative neuroanatomists of past decades lacked clear guidance about which species would provide the most useful comparisons with primates. Now we know that the closest relatives of primates are tree shrews and colugos, in some order, and the next closest are lagomorphs and rodents, collectively. Figure 3.3 presents an updated analysis of the relationships among placental mammals, also known as eutherians. Primates are Euarchontans and Euarchontoglires, and they are also Boreoeutherians, a basal branch of the eutherian tree that established itself in the Northern Hemisphere. Two other basal branches, Xenarthans and Afrotherians, lived on southern continents, specifically South America and Africa when both were islands (like Australia is today). At more basal levels of the vertebrate tree (not illustrated), primates are also therians (marsupial and placental mammals), amniotes (reptiles, birds, and mammals), tetrapods (amphibians and amniotes), and gnathostomes.

Figure 3.3 An evolutionary tree of eutherian mammals. Relations among placental mammals according to a recent molecular phylogeny. Adapted from W.J. Murphy et al., Phylogenomics and the genetic architecture of the placental mammal radiation, Annual Review of Animal Bioscience 9, 5.1–25, 2021.

Two examples illustrate why robust, detailed phylogenies have such an important bearing on understanding cortical evolution. Chapter 12 (“Oculomotor oversight”) discusses ideas about the frontal eye field (FEF), a part of the prefrontal cortex in primates. Preuss and Goldman-Rakic11 concluded that the FEF is a primate synapomorphy, which implies that it lacks a homolog in other mammals. Against this view are claims of the FEF’s existence in laboratory rats and domestic cats. The evidence for the former is not very convincing, but it’s often cited in reviews comparing primates with rodents. However, even on the assumption that rats have something resembling the FEF of primates, and the additional assumption that it reflects a shared rodent trait, that wouldn’t necessarily mean that the FEF is homologous in rats and primates. If so,

then tree shrews should have the FEF, too (with some caveats that Chapters 11 and 12 discuss). Careful investigation of their frontal cortex reveals that they don’t.12 In view of the well-earned confidence about the taxonomic placement of tree shrews as close relatives of primates (Figure 3.3), it’s likely that whatever kind of FEF rats have, if any, resulted from homoplasy, not descent from a common ancestor, and the same arguments apply to cats and their FEF. In contrast to that level of clarity, consider how we would view the same data in the context of the wishy-washy taxonomy that prevailed prior to today’s molecular phylogenies. Even when the traditional taxonomy turned out to be correct, a lack of confidence in it would have undermined conclusions about homology versus homoplasy for the FEF. Comparative neuroanatomists of the era wouldn’t have considered data from tree shrews to be any more significant than information about the cortex of bats, elephant shrews, or tenrecs. Another example involves the striate and extrastriate cortex. The striate cortex is also known as the primary visual cortex (V1), and other visual areas in the occipital, temporal, and parietal lobes of primates compose the extrastriate visual cortex. The superficial layers of area V1 contain cytochrome-oxidase-rich “blobs” in both primates and carnivores,13 and both groups have a large number of extrastriate visual areas.14 Primates and carnivores are distantly related mammals (Figure 3.3), so it’s tempting to conclude that these two traits—blobs in V1 and a large number of extrastriate areas—are homologous and have been inherited from a remote common ancestor, perhaps the last common ancestor (LCA) of placental mammals or maybe the founding Boreoeutherians. However, species with a closer evolutionary relationship to primates, such as rats and tree shrews, lack blobs in their V1 cortex and have relatively few extrastriate areas. These findings indicate that both traits are homoplasies in primates and carnivores.13, 15 Only in the context of confidence about evolutionary relationships among placental mammals is it possible to draw such firm conclusions. Traditional taxonomy Figure 3.4 presents the phylogeny of Euarchontoglires another way. It shows the major taxa, and it includes selected synapomorphies. It also indicates two paraphyletic groups: prosimians and monkeys.

Figure 3.4 An evolutionary tree of Euarchontoglires. Italic type above or below black bars indicates synapomorphies (shared derived traits) in the indicated lineage. Brackets to the right identify paraphyletic groups. Animal drawings by Mary K.L. Baldwin.

To remember the names of the major primate taxa, it helps to know noses: rhine, from Greek. Primate noses can be oriented inward or downward, and they can be simple or flat, which leads to the names of four major subordinal primate clades: strepsirrhines, haplorhines, catarrhines, and platyrrhines. Taken in turn: • • • •

Strepsirrhine means inward or twisted nose, said to be wet because the rhinarium—a patch of mostly hair-free skin surrounding the nostrils—connects with mucosal tissue in the nasal passage. Haplorhine means simple nose, said to be dry because those animals lack a rhinarium. Catarrhine means downward nose. Platyrrhine means flat nose.

Ask yourself: Which way do my nostrils point? They probably point down; in which case you can classify yourself as a catarrhine. Even if they don’t, you’re still a catarrhine, as well as an anthropoid, haplorhine, Euprimate, and Euarchontan. As a mnemonic aid, Box 3.1 gives the etymology for taxonomic terms applied to Euarchontoglires. Box 3.1 Etymology of the Euarchontoglires

• • • • • • • • • • • • • • •

Lagomorph refers to animals that resemble hares (Greek). Scandentia refers to a climbing habit (Latin). Rodent refers to a gnawing habit (Latin). Dermoptera have folds of skin for wings (Greek). Euarchonta has something to do with the archons (magistrates) of ancient Athens. This is probably another instance of exalting one’s own group, like the term “primate.” Glires comes from dormouse (Latin). Tarsiform and tarsier refer to long ankle bones (Latin). Lemuriform and lemur come from the Latin for death spirit, a reference to their nocturnal habit. Lorisiform and loris refers to clownishness (said to derive from archaic Dutch or French). Galago comes from a word in a West African language (Wolof) for monkey, which they are not. Cercopithecoid comes from the Greek for apes with tails. They’re not apes, but nearly all of them do have tails, the secondary loss of which in one cercopithecoid species, Macaca sylvanus, led to their misclassification as Barbary “apes.” Tupaia derives from the Malay word for squirrels, which they are not. Hominoid comes from the Latin for human-like. Anthropoid comes from the Greek for human-like. Euprimate: The Preface mentioned the “–primate” part; Chapter 5 (“Gnawing away at primate evolution”) explains the “Eu–” part.

Strepsirrhines The strepsirrhines include lorises, lemurs, and several other species. Lorises, pottos, and galagos compose one group of strepsirrhines (Lorisiforms); lemurs, aye-ayes, indris, and sifakas another (Lemuriforms). (Colugos, also known as flying lemurs, are not lemurs despite their informal name.) Prosimians are a paraphyletic group that includes all the strepsirrhines and one lineage of haplorhines: tarsiers (Figure 3.4). It’s common to use the terms strepsirrhine and prosimian interchangeably, a practice that tarsiers resent, I suspect. However, much like the situation with colugos, the nearly complete absence of tarsiers from neuroscience laboratories leads to such neglect, so both they and we will have to live with it. The fact that tarsiers are more closely related to anthropoids than to strepsirrhines explains why some cladists say that there’s no such thing as a prosimian. Only monophyletic groups count as things by their way of thinking. Notwithstanding this cladistic shortcoming, similarities among tarsiers and strepsirrhines often justify a common label. For example, species in both taxa have small bodies, tend to forage nocturnally, eat insects, and live in trees. One recent phylogenetic analysis showed that among modern primates, tarsiers and strepsirrhines cluster together in diversification, speciation, and extinction rates.16 Chapter 7 discusses a prosimian grade of brain size and cortical extent, which applies mainly to strepsirrhines but also to tarsiers and to some extinct primates. Haplorhines Haplorhines diversified into the aforementioned tarsiers and anthropoids. A synonym for anthropoid, simian (Simii or Simiiform), is coming into vogue because of precedence and priority. The rules of zoological nomenclature give priority to the earliest valid identifier of a natural group, with later terms for the same group called inferior or junior synonyms. However, because neuroscientists understand the term anthropoid, I use it instead of simian, which is more likely to be misconstrued as referring only to monkeys. In view of the prominence of anthropoids, it’s common to contrast strepsirrhines with anthropoids, setting aside the tarsier branch of the haplorhine tree. Tarsiers probably find this practice as infuriating as the use of “strepsirrhine” and “prosimian” interchangeably. The prevailing view is that anthropoids are a monophyletic group, some members of which rafted to South America from Africa. Paleontologists have discovered that at least two anthropoid species made the journey, but one line died out.17 The successful species established the lineage informally called New World monkeys and formally known as platyrrhines. Examples of platyrrhines include tamarins, marmosets, owl monkeys, howler monkeys, spider monkeys, woolley monkeys, titi monkeys, sakis, squirrel monkeys, and capuchin monkeys. Figure 3.5 illustrates some relationships among those lineages.

Figure 3.5 An evolutionary tree of Euarchontoglires, emphasizing anthropoids. Orange lines indicate that catarrhines and platyrrhines are sister groups; red lines show that hominoids and cercopithecoids are sister groups; pink lines are for sister groups of Old World monkeys; and purple lines are for sister groups of hominoids. Adapted from T.M. Preuss and S.P. Wise, Evolution of prefrontal cortex, Neuropsychopharmacology 47, 3–19, 2022.

Catarrhine anthropoids eventually diversified into Old World monkeys, apes, and humans. Among catarrhines, Old World monkeys are the most speciose (species-rich) clade. They are formally known as cercopithecoids, which eventually split into two groups: colobines and a group that includes cercopithecines (Figure 3.5). In zoological nomenclature, the –oid suffix indicates a group higher than the family level, and –ine denotes a level below the family. They contrast with the –id suffix, which applies to families. Accordingly, some Old World monkeys are cercopithecoids (above family), cercopithecids (family), and cercopithicines (below family), and some are also in the genus Cercopithecus. Colobines are a facultatively folivorous (leaf-eating) group that includes langurs, proboscis monkeys, and colobus monkeys; cercopithicines are a mainly omnivorous group that includes macaques, baboons, vervets, talapoin monkeys, patas monkeys, guenons, mangabeys, and mandrills. The informal name savanna monkeys is becoming a popular shorthand for cercopithecines, and they are also called pouch monkeys because of a buccal fold that enables them to store about as much food in their mouths as they can pack into their stomachs. The paraphyletic term “monkey” applies to more than 250 species of anthropoids (Figure 3.4).18 In cladistic terms, monkeys are nonhominoid anthropoids, meaning that once we exclude hominoids (apes and humans) from the anthropoids, the remainder consists of monkeys. Some experts have attempted to ban references to paraphyletic groups, but terms like “monkey” are too helpful to discard, and the same goes for prosimian, ape, great ape, reptile, and amphibian. Paraphyletic terms are also harmless provided that both authors and readers recognize their limitations. Although I refer to monkeys hundreds of times in this book—more than two dozen in this chapter, alone—this practice doesn’t imply that they’re all equally related to humans. Figure 3.5 shows that platyrrhines and cercopithecoids are distinct lineages that are more closely related to other primates than they are to each other. The closest relatives of cercopithecoids

are the hominoids; and the closest relatives of platyrrhines are the catarrhines. This might seem like an arcane point, but it’s important for understanding cortical evolution. Catarrhines In addition to the taxonomic terms mentioned thus far, another set applies exclusively to catarrhines. Figure 3.6 illustrates these taxa and their relationships.

Figure 3.6 An evolutionary tree of catarrhines. The brackets identify paraphyletic groups. Terms in bold type are used often in later chapters.

One term, hominin, has several inconsistent meanings in the literature because the suffix “–in” applies to several levels of classification. In this book, hominin refers to humans and closely related species. About 6.3 Ma, the LCA of chimpanzees, bonobos, and humans diverged into two populations, which underwent a period of hybridization—perhaps a couple of million years long19—before splitting into two noninterbreeding populations. The descendants of the human–chimpanzee LCA are either panins or hominins. The former group includes chimpanzees and bonobos; the latter includes both modern and extinct human (Homo) species and extinct hominins such as australopithecines. Figure 8.7 presents one view of the relationships among extinct hominins. As mentioned earlier, the term hominoid refers to apes and humans, and hominid refers to the great ape–human clade. Like prosimians and monkeys, apes (gibbons and great apes) and great apes (orangutans, gorillas, and panins) are paraphyletic groups. Chapter summary This chapter concludes Part I (“What primates are”). As the Overview says, primates are many things, and Part I has established several of them: • • • • • •

Primates are a clade, which means that they have all descended from a single founding species. Primates are Euarchontans, Euarchontoglires, and placental (eutherian) mammals, not to mention therian mammals, amniotes, tetrapods, and gnathostomes. Primates are mosaics of homologies and homoplasies, as well as of old and new cortical areas. Primates are the result of evolved developmental programs, which produce the homologies, homoplasies, old, and new areas. Primates are speciose, with hundreds of species in their crown group. Finally, and most importantly, primates are diverse.

Until surprisingly recently, uncertainty about evolutionary relationships between primates and other placental mammals hampered studies of cortical evolution. Molecular phylogenies, combined with traditional taxonomic methods, have now

established colugos and tree shrews as the closest relatives of primates, with rodents and lagomorphs as the next closest group, collectively. To “the” or not to “the” To read this section’s title as intended, it helps to pronounce “the” to rhyme with bee. Why do I place so much stress on the ubiquitous article “the”? The answer lies in a phrase: “the primate.” The neuroscience literature contains uncountable references to this mythical beast. For example, one paper20 on the neurophysiology of the hippocampus has the title: “Context-dependent representations of objects and space in the primate hippocampus during virtual navigation” [italics mine]. Although the authors probably didn’t intend to imply that the hippocampus has the same properties in all primate species, it’s easy to read their title that way. The hippocampus of primates is diverse because primates are diverse. The hippocampus undoubtedly performs many conserved functions, which have received their due emphasis in the literature. However, as primate brains evolved, new neocortical areas emerged, and they have both direct and indirect connections with the hippocampus. The evolution of these transcortical networks changed what the hippocampus does. The only question is whether such changes are small variations on a fundamental function or important innovations that contribute to emergent properties. I advocate the latter, which is one of the main themes of two previous books.21, 22 In general, I’m against banning words in discussions of evolution. In my opinion, there are no bad words, only bad concepts. A phrase like “the primate” seems to deny (or at least neglect) diversity. If authors and speakers take care not to do that, then the expression does little harm. The same goes for “the monkey.” Rather than repressing such language, I think it’s better to urge neuroscientists to be aware of how such phrases can mislead readers and audiences (or even oneself). Use of the plural, such as “the cortex of primates,” serves as a reminder of diversity that can on occasion get lost in discussions of “the primate cortex.” In this vein, and with apologies to the Bard of Avon, I close this chapter with a quintain that, although questionable in its quality, contemplates and captures the quandary of “the”: To “the,” or not to “the,” that is the question Whether ‘tis nobler to use that convention For primates and for their structures neural Or take arms against a misconception By dumping “the” and using the plural

For better or verse, my doggerel exposes one final thing that some primates are: pathetically poor poets. Even worse, some of them have an amateurish affection for alliteration and assonance. This practice is distracting and pointless, or so I’m told. With that opinion firmly in mind, I present Part II, which pivots from primates of the present to ponder the past. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

Simons, E.L. Primate Evolution: An Introduction to Man’s Place in Nature (Plenum, New York, 1972). Le Gros Clark, W.E. On the anatomy of the pen-tailed tree shrew (Ptilocercus lowii). Proceedings of the Zoological Society of London 96, 1179–309 (1926). Halliday, T.J., Upchurch, P., & Goswami, A. Resolving the relationships of Paleocene placental mammals. Biological Reviews of the Cambridge Philosophical Society 92, 521–50 (2017). Esselstyn, J.A., Oliveros, C.H., Swanson, M.T., & Faircloth, B.C. Investigating difficult nodes in the placental mammal tree with expanded taxon sampling and thousands of ultraconserved elements. Genome Biology and Evolution 9, 2308–21 (2017). Upham, N.S., Esselstyn, J.A., & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. Public Library of Science, Biology 17, e3000494 (2019). Murphy, W.J., Foley, N.M., Bredemeyer, K.R., Gatesy, J., & Springer, M.S. Phylogenomics and the genetic architecture of the placental mammal radiation. Annual Review of Animal Bioscience 9, 5.1–25 (2021). Janečka, J.E., Miller, W., Pringle, T.H., Wiens, F., Zitzmann, A., Helgen, K.M., Springer, M.S., & Murphy, W.J. Molecular and genomic data identify the closest living relative of primates. Science 318, 792–4 (2007). Murphy, W.J., Eizirik, E., O’Brien, S.J., Madsen, O., Scally, M., Douady, C.J., Teeling, E., Ryder, O.A., Stanhope, M.J., de Jong, W.W., & Springer, M.S. Resolution of the early placental mammal radiation using Bayesian phylogenetics. Science 294, 2348–51 (2001). O’Leary, M.A., Bloch, J.I., Flynn, J.J., Gaudin, T.J., Giallombardo, A., Giannini, N.P., Goldberg, S.L., Kraatz, B.P., Luo, Z.X., Meng, J., Ni, X., Novacek, M.J., Perini, F.A., Randall, Z.S., Rougier, G.W., Sargis, E.J., Silcox, M.T., Simmons, N.B., Spaulding, M., Velazco, P.M., Weksler, M., Wible, J.R., & Cirranello, A.L. The placental mammal ancestor and the post-K-Pg radiation of placentals. Science 339, 662–7 (2013). Chester, S.G.B., Bloch, J.I., Boyer, D.M., & Clemens, W.A. Oldest known euarchontan tarsals and affinities of Paleocene Purgatorius to primates. Proceedings of the National Academy of Science USA 112, 1487–92 (2015). Preuss, T.M. & Goldman-Rakic, P.S. Myelo- and cytoarchitecture of the granular frontal cortex and surrounding regions in the strepsirhine primate Galago and the anthropoid primate Macaca. Journal of Comparative Neurology 310, 429–74 (1991). Baldwin, M.K.L., Cooke, D.F., & Krubitzer, L.A. Intracortical microstimulation maps of motor, somatosensory, and posterior parietal cortex in tree shrews (Tupaia belangeri) reveal complex movement representations. Cerebral Cortex 27, 1439–56 (2017). Preuss, T.M. Taking the measure of diversity: comparative alternatives to the model-animal paradigm in cortical neuroscience. Brain, Behavior and Evolution 55, 287–99 (2000). Lyon, D.G. The evolution of visual cortex and visual systems. In: Evolution of Nervous Systems (ed. L.A. Krubitzer & J.H. Kaas) 3, 1–40 (Elsevier, 2006). Kaas, J.H. The organization and evolution of neocortex. In: Higher Brain Function: Recent Explorations of the Brain’s Emergent Properties (ed. S.P. Wise) 347–78 (John Wiley, New York, 1987). Scott, J.E. Variation in macroevolutionary dynamics among extant primates. American Journal of Biological Anthropology 179, 405–16 (2022). Seiffert, E.R., Tejedor, M.F., Fleagle, J.G., Novo, N.M., Cornejo, F.M., Bond, M., de Vries, D., & Campbell, K.E. Jr. A parapithecid stem anthropoid of African origin in the Paleogene of South America. Science 368, 194–7 (2020). Mitani, J.C., Call, J., Kappeler, P.M., Palombit, R.A., & Silk, J.B. Primate behavioral diversity. In: The Evolution of Primate Societies (ed. J.C. Mitani, J. Call, P.M. Kappeler, R.A. Palombit, & J.B. Silk) 7–16 (University of Chicago Press, Chicago, IL, 2012). Patterson, N., Richter, D.J., Gnerre, S., Lander, E.S., & Reich, D. Genetic evidence for complex speciation of humans and chimpanzees. Nature 441, 1103–8 (2006). Gulli, R.A., Duong, L.R., Corrigan, B.W., Doucet, G., Williams, S., Fusi, S., & Martinez-Trujillo, J.C. Context-dependent representations of objects and space in the primate hippocampus during virtual navigation. Nature Neuroscience 23, 103–12 (2020). Murray, E.A., Wise, S.P., Baldwin, M.K.L., & Graham, K. The Evolutionary Road to Human Memory (Oxford University Press, Oxford, UK, 2020). Murray, E.A., Wise, S.P., & Graham, K.S. The Evolution of Memory Systems: Ancestors, Anatomy, and Adaptations (Oxford University Press, Oxford, 2017).

* Wilfrid E. Le Gros Clark was a pioneer in comparative neuroanatomy. Via E.G. (Ted) Jones and T.P.S. (Tom) Powell, he was also my academic great grandparent. His autobiography, The Chant of Pleasant Exploration (E. & S. Livingstone: London, 1968) is worth reading if for no other reason than the story he tells about his brother.

PART II WHAT PRIMATES WERE

Prolog to paleontology Overview According to molecular phylogenies, primates probably emerged during the late Mesozoic. Even so, all primate fossils postdate the end-Cretaceous cataclysm. The oldest ones were found in geological formations dated to ~200,000 years after the extinction, ~66 Ma; and the oldest Euprimate fossils are ~55 million years old. Among other things, fossils enable paleontologists to estimate the body size of extinct species, which along with dental morphology yields clues about their diets and lifestyles. Body size also provides information about how primates adapted to climate change. Primates arose in a warm, wet world dominated by dense rainforests. Then, during the past 50 million years, the earth cooled several times, sometimes gradually and sometimes abruptly. Each period of global cooling decreased atmospheric and soil moisture, which caused rainforests to dry and contract. Because early primates depended on the protection and nutrients these habitats provided, deforestation created strong selective pressures. If a large extraterrestrial object—the ultimate random bolt from the blue—had not triggered the extinction of dinosaurs 65 million years ago, mammals would still be small creatures, confined to the nooks and crannies of a dinosaur’s world, and incapable of evolving the larger size that brains big enough for self-consciousness require. —Stephen Jay Gould, Full House: The Spread of Excellence from Plato to Darwin, 1996, p. 216.

Introduction In this chapter, mammals hide out, dinosaurs die out, and rainforests dry out. But I begin by urging neuroscientists to take an interest in extinct primates. It’s worth the effort because delving into their lives reveals some unexpected relatives: stem primates that resembled rodents; Euprimates with a rodent-size cortex; and large-bodied anthropoids that had a cortex no larger than prosimians have today, and maybe smaller. Later chapters address the transition to modern forms of primate cortex, but this chapter and the next two set brains aside for the most part.

Figure 4.1 Geological epochs and eras. Arrows show approximate transition dates. The timescale is nonlinear. Abbreviation: Ma, million years ago.

Figure 4.1 illustrates ten key geological terms that organize discussions of primate evolution. It’s probably best to memorize these names and dates, but a reference figure on p. xx includes the most important ones if you don’t. A mnemonic aid for the Cenozoic (new animal) epochs might help. Imagine the following slogan from a purveyor of smoked meat: “People enjoy our mammoth presliced party hams” for Paleocene, Eocene, Oligocene, Miocene, Pliocene, Pleistocene, and Holocene. That’s awful, of course, but it’s better than the two most prominent ones I found on the internet: “Pretty eager old men play poker hard”; and “pigeon egg omelets make people puke heartily.” It also might help to remember the six main Cenozoic epochs in pairs, with ~34 Ma as a key date: the Paleocene and Eocene before ~34 Ma; the Oligocene and Miocene afterward; and then the Pliocene and Pleistocene (the epochs of hominin evolution). In this vein, many authors combine the Pliocene and Pleistocene into the Plio-Pleistocene. A later section (“Focus on forests”) explains why ~34 Ma is such an important date in the history of primates. The dating scene Although this chapter and the next two don’t include much neuroscience, they’re important for understanding our main topic. To explore why the cortex expanded and added new areas, we need to have some idea about the ecology of extinct primates. For that, we need to know when specific lineages originated and diversified, at least roughly. Tick-tock molecular clock Estimates for divergence dates depend on the rate of genetic mutations, called a molecular clock. A molecular clock can draw on single-nucleotide polymorphisms in either mitochondrial or nuclear DNA, whole-genome sequences, or other kinds of molecular data. For example, some molecular clocks depend on examining nucleotide insertions or deletions (indels) that have accumulated in a well-studied genomic sequence. One such study examined sequences of protein-coding genes (exons).1 It found three indels indicating that primates, colugos, and tree shrews are a monophyletic group. One exon had a 3-amino-acid deletion in primates and colugos but not in tree shrews. That’s the kind of finding that supports the idea that colugos have a closer relationship to primates than tree shrews have. Figure 4.2 displays data from three molecular-clock studies.1,2,3 According to these estimates, the last common ancestor of modern primates lived ~74 Ma, and eight Euarchontoglires lineages—rodents, lagomorphs, tree shrews, colugos, lemuriforms, lorisiforms, tarsiers, and anthropoids—had emerged prior to the Paleocene (Figure 4.2A). A more recent reassessment concurs,4 and most molecular analyses place primate origins somewhere between 78 Ma and 70 Ma.

Figure 4.2 Chronograms of Euarchontoglires and primates. (A) Uncorrected molecular-clock estimates for divergence times. The three black circles in Part A also appear in the gray-shaded area of Part B, at the same times. (B) Uncorrected molecular-clock estimates for divergence times (filled black circles) and estimates corrected for changes in mutation rates, based on: body size (unfilled blue circles), relative brain size (unfilled red circles), and absolute brain size (unfilled green circles). The letter ‘F’ indicates the earliest clear fossil evidence for a given lineage. Abbreviations: Ma, million years ago; Plio-Plei, Pliocene-Pleistocene. (A) Adapted from two sources: L. Pozzi et al., Primate phylogenetic relationships and divergence dates inferred from complete mitochondrial genomes, Molecular Phylogenetics and Evolution 75, 165–83, 2014; and J.E. Janečka et al., Molecular and genomic data identify the closest living relative of primates, Science 318, 792–4, 2007. (B) Adapted from M.E. Steiper and E.R. Seiffert, Evidence for a convergent slowdown in primate molecular rates and its implications for the timing of early primate evolution, Proceedings of the National Academy of Sciences USA 109, 6006–11, 2012.

Molecular divergence times typically predate those based on fossil evidence by tens of millions of years. Figure 4.2B illustrates molecular-clock estimates for eight key divergence dates, along with the date of the oldest well-established fossils in the same lineages, from Steiper and Seiffert.3 Discrepancies arise for several reasons, including the sparsity of the fossil record and a lag between the genetic isolation of a new species and an accumulation of diagnostic morphological traits.5 When a species diverges into two populations, they begin with similar features, which change over time in response to selective pressures and genetic drift. Hybridization can remain possible for tens of millions of years, depending on the circumstances that led to speciation. Many factors contribute to divergence-date discrepancies, but Steiper and Seiffert focused on variation in the rate of molecular change. The fossil record shows that primates (along with many other mammals) increased in size during the Oligocene and Miocene. The longer lives of large animals, their lower metabolic rates, and increased intergenerational intervals contribute to slowing the molecular clock. In addition to body size, the clock’s rate correlates inversely with both absolute brain size and relative brain size (i.e., brain volume relative to body mass).3 Chapters 7 and 8 say a lot about relative brain size in primates, but here this measure is simply a correlate of the molecular-clock rate. These correlations imply that the molecular clock “ran” faster before the Oligocene, so it wouldn’t have taken as long as usually estimated to accumulate the observed number of molecular changes. As a heuristic example, imagine that you set a computer’s timer to run for an hour and then buzz. If the timer ran properly, when you heard the buzz, you would know that an hour had elapsed. But what if, because of a programming bug, the timer ran at twice the normal rate for the first half hour,

then performed properly for the second half hour? When it buzzed, you would think that an hour had passed, but after only 45 minutes. In an analogous way, a more rapid accumulation of molecular changes in the distant past would bring the estimates for divergence times closer to the present and therefore closer to the fossil record. Figure 4.2B includes divergence estimates based on three correction factors: body size, relative brain size, and absolute brain size.3 The corrected estimates agree much better with the fossil record, and a more recent analysis reached similar conclusions.6 The chronogram lines in Figure 4.2B align with the Steiper–Seiffert corrections based on relative brain size (an arbitrary choice on my part) and thus differ from the divergences depicted in Figure 4.2A. A prodigious plop In most cases, it wouldn’t matter much whether a divergence occurred earlier or later within a range of a few million years. However, when that range encompasses ~66 Ma, it matters a great deal. Something big happened ~66 Ma, which one study dated more precisely to 66.043 ± 0.043 Ma.7 (In the epigraph, Gould cites an older estimate: 65 Ma.) Because of the calamity that occurred then, the differences between corrected and uncorrected molecular-clock estimates have a big impact (no pun intended*) on understanding the evolution of primates and their cortex. As nearly everyone knows, an asteroid (or a comet) crashed into the southern Gulf of Mexico and triggered a mass extinction. This prodigious plop created a crater in and near the Yucatán Peninsula, and it changed life on Earth forever. It was a very bad day for the dinosaurs, which dominated many ecosystems at that time. In the impact’s aftermath, particulate matter filled the atmosphere to the point that it blocked photosynthesis, which led to the collapse of virtually all the ecosystems on Earth. The liberation of sulfates from volcanic eruptions acidified the oceans and caused yet more havoc. What’s more, the asteroid hit a deposit of calcium carbonate in the Yucatán, which increased atmospheric carbon and caused extreme global warming. First, however, the aforementioned atmospheric particulates reflected enough sunlight to cause a transient global cooling (sometimes known as nuclear winter).8 Only one of every four species survived the cascade of catastrophes that followed the dinosaurs’ bad day: first the explosion, earthquakes, rain of molten glass, and fires; then persistent winter-like conditions caused by garbage in the atmosphere; followed by a long period of intense global warming. The end-Cretaceous extinction influenced primate evolution in several ways. As Gould points out in the epigraph, mammals had been hiding out in “nooks and crannies of a dinosaur’s world” during the Mesozoic. The extinction opened niches that mammals later exploited, primates among them. More specifically, it eliminated the sauropod dinosaurs, which because of their large bodies and long necks could reach resources high in the arbor of trees. After ~66 Ma, these gigantic herbivores were gone. Likewise, the dinosaurs and pterosaurs that preyed on mammals had died out. “May you live in interesting times” is supposedly an ancient Chinese curse (which, apparently, is a recent concoction that no ancient Chinese person ever heard or uttered). The end-Cretaceous extinction certainly made life interesting for the animals that endured the tumult of those times. Among animal species, those that stood the best chance of survival probably had omnivorous or insectivorous diets and small bodies. Omnivory allowed survivors to subsist by foraging for foods like seeds and nuts, which have a long “shelf life.” Birds with beaks and gizzards did better than those without them because they could survive on tough seeds. Accordingly, all modern birds have beaks and gizzards.9 Large animals and species with specialized diets of plants or animals faced a bleak and brief future. Some insects survived, so small omnivorous and insectivorous species could subsist on that food source once insect populations recovered from the disaster. A burrowing habit helped, too,10 as did species with large populations. Impoverished conditions persisted until new ecosystems developed to replenish the resources destroyed by the mass extinction. The recovery began remarkably quickly in geological terms, within the first few hundred thousand years after the impact.11, 12 Did primates share the dinosaurs’ bad day? For the purposes of this book, it’s important to know which primates, if any, survived the end-Cretaceous extinction. If we accept most molecular-clock estimates for the divergence of primates from other Euarchontans (~78–70 Ma), then quite a few primate species would have experienced the upheaval.4, 6, 13 However, Figure 4.2B shows divergence estimates that span the end-Cretaceous extinction. By “span,” I mean that standard molecular-clock estimates suggest that a lineage emerged prior to the exinction, but at least some corrected molecular-clock dates and fossil dates indicate that it originated afterward. One study that combined fossil and molecular data with an analysis of phenotypic traits suggested that only the stem lineage of placental mammals survived the end-Cretaceous extinction.14 Then, during the first ~200,000–400,000 years of the Paleocene, ~10 speciation events produced most of the orders observed today, including primates. According to that analysis, no primates experienced the end-Cretaceous extinction. Other investigators have reached a different conclusion. One recent molecular phylogeny supports the idea that primates diverged from other Euarchontans 10 million years before the extinction.4 Another, like the work of Steiper and Seiffert, incorporated molecular and fossil data, but differed in the kinds of correction factors considered.6 It estimated that primates emerged just prior to the extinction, as depicted in Figure 4.2B, but with 95% confidence limits of ~15 million years in both directions, which leaves the question open. Taken together, the evidence remains inconclusive, but most studies place primate origins in the late Mesozoic, prior to the end-Cretaceous extinction. Ironically, the best fossil evidence for a Mesozoic origin of primates comes from fossils dated to the Cenozoic. Chapter 2 (“Keystone and diagnostic traits”) mentioned a group of animals called plesiadapiforms. There’s a lot to say about them, but for now two points are relevant: (1) Some of their fossils date to the earliest part of the Paleocene (~66 Ma); and (2) most paleontologists consider them to be stem primates (but not Euprimates). Plesiadapiform genera include Purgatorius and related animals, collectively called purgatoriids. Wilson Mantilla et al.12 dated two fossil species from that group with more-than-the-usual precision. Most likely, these animals lived ~105,000– 139,000 years after the end-Cretaceous extinction; and it’s nearly certain that they lived within 208,000 years of it. These arboreal species appear to have immigrated into North America from Asia shortly after forests became re-established during

the initial 100,000 years of ecological recovery. To quote Wilson Mantilla et al.12 (p. 2), within “1 million years of their arrival in northeastern Montana, plesiadapiforms outstripped archaic ungulates in numerical abundance and dominated the arboreal omnivore–frugivore niche” among mammals. Given that it takes at least a few million years for a clade to radiate into multiple species, these findings provide the best fossil evidence that stem primates originated in the late Mesozoic, prior to the mass extinction. Cortical considerations At this point, neuroscientists might wonder what any of this has to do with cortical evolution. Hang in there, the relevance is this: We need to have some idea about when various primate lineages existed in order to understand the selective forces acting on their cortex. One discrepancy is especially important for what comes later. It bears on whether primates experienced the dinosaurs’ bad day. If, as molecular-clock estimates imply (Figure 4.2A), primates had existed for as much as 8–12 million years before the end-Cretaceous extinction,4 they must have had traits that enabled them to survive the cataclysm. If not, there’s no need to posit such traits. Given that all modern primates have a large brain with an expansive neocortex, it’s tempting to imagine that these assets saw them through the struggles that followed the ecological collapse of 66 Ma. But that can’t be true. Chapter 7 reviews fossil evidence showing that Paleocene primates had a cortex in the size range of modern rodents, as did most mammals then. Therefore, if primates survived the extinction, it wasn’t because they had a larger cortex than other mammals living at the same time. Another important discrepancy is whether hominoids (the ape–human lineage) diverged from cercopithecoids (Old World monkeys) ~31 Ma, as uncorrected estimates suggest, or closer to ~26–23 Ma, as corrected dates imply. The timing of cortical expansion in hominoids, as well as their subsequent radiation and dispersal, makes more sense if they emerged nearer the latter dates. Bodies from bones Body size informs an understanding of cortical evolution because of the relationships among body and cortex size, dietary preferences, ecology, and climate. In modern primates, body size affects many aspects of their lives: the distance over which they forage; the mechanics of locomotion and posture (sometimes called positional behavior); their diet and digestive system; metabolic rate; gestation time; and reproductive rate, among other aspects of life history.15 The same correlates apply to fossil primates, so paleontologists can study modern species to understand extinct ones. Specifically: •

•

•

The sizes of different body parts are correlated, so paleontologists can use measures such as molar width, cranial length, or foramen-magnum diameter to estimate a fossil primate’s body size, even without evidence from the postcranial skeleton.15 One recent study, for example, found a close correlation (r2 = 0.96) between the body mass of primates and the size of postcanine teeth (specifically, the surface area of their roots).16 Body size has a relationship with climate. Larger individuals tend to live in cool climates; smaller animals in warm ones (Bergmann’s rule). This correlation helps paleontologists interpret fossil evidence. When climates warm, animals in each lineage tend to decrease in size; when climates cool, they often become larger. Individuals in many mammalian lineages increased in size during evolution (Cope’s rule), and this happened for two main reasons: to exploit a specialized resource, or to lower an animal’s surface-to-volume ratio, which conserves body heat. An increase in body size could also suggest that members of a lineage invaded cooler climes. However, larger size comes with vulnerabilities: the need for more food and, usually, larger home ranges that expose individuals to greater predation risks. Larger body size also correlates with lower reproductive rates, which can increase the likelihood of extinction.15 Body size affects dietary specializations. The smallest primates can survive on insects (insectivory or faunivory) or tree exudates, mainly saps and gums (gumivory). As a primate increases above ~500 grams, however, such a diet makes it difficult to obtain sufficient nutrition,17 although patas monkeys manage to do so. Primates above that size have longer digestive tracts, which allows better digestion of leaves, especially young and tender ones.15 Among primates larger than 1 kg, fruit becomes important because of its high nutritional value. These relationships provide paleontologists with clues about the dietary habits of a fossil primate from its body size, alone. Dental evidence, of course, also provides important clues about diet, and sometimes plant material becomes incorporated into fossil teeth.

Figure 4.3 Tooth morphology and diet. An example of how dental traits can yield insights into the diet of extinct animals, based on a principal component analysis. Triangles show data from plesiadapiform species. The percentages in the axis labels indicate the variance captured by the first two principal components. Adapted from G.P. Wilson Mantilla et al., Earliest Paleocene purgatoriids and the initial radiation of stem primates, Royal Society Open Science 8, 210050, 2021.

Figure 4.3 presents a principal component analysis (Box 4.1) that illustrates how dental evidence can support inferences about dietary specializations.12 It plots data from insectivores and frugivores that lived near the end-Cretaceous extinction. Most likely, positive values of the first principal component capture shorter molars with wide basins and sharp edges, features that process fruits and nuts efficiently. The figure shows that plesiadapiform molars had shapes between those of contemporaneous insectivores and frugivores, which indicates that plesiadapiforms had an omnivorous diet that included both kinds of food. The earliest of these species lived shortly after the extinction event; others lived a few million years later. Their molar morphology reveals an evolutionary trend toward a diet that included more fruit as plesiadapiforms radiated and diversified during the early Paleocene. Box 4.1 PCA Principal component analysis (PCA) is a statistical method used commonly in comparative morphology (as well as for spike sorting in neurophysiology). The basic idea is that a complex set of measurements can be reduced to (usually) two or three uncorrelated dimensions that capture the most variance in the data. This transformation simplifies morphological variation that’s otherwise difficult to articulate or measure directly. Sometimes, it’s possible to describe in words what a principal component captures, and sometimes it isn’t. But even mysterious principal components serve to distinguish complex morphologies. Focus on forests To understand why the cortex changed during evolution, it helps to know what sort of habitats primates occupied in the past. This field of research, called paleoecology, draws on both fossil and geological evidence, as well as contemporary ecology. For most of primate history, arboreal niches provided nutrients and protection from predators, and for many primates they still do. Consequently, tree cover—the percentage of the sky blocked by leaves—defines many of the habitats relevant to primate paleoecology. Chapter 6 delves into this subject, and it depends on the following classifications:15 • • • •

Grasslands have less than 40% leafy tree cover. Woodlands have 40–80% tree cover; the lower the percentage, the more open the woodland. Savannas are a mixture of grasslands and woodlands. Forests have more than 80% tree cover; the more trees and cover, the denser the forest.

Among forests, some are classified as either tropical or subtropical. A tropical rainforest receives copious amounts of rainfall during most of the year, which produces a high density of trees. In such conditions, the canopy of the forest closes (i.e., arbors from its trees abut and interleave), which blocks sunlight and limits the growth of smaller plants at ground level. Subtropical forests have a similar character, but with a lower tree density, somewhat less soil moisture, and slower growth. These dry definitions facilitate a discussion of something wet: the habitats of ancestral primates. There’s an idiom meant to convey to someone that they aren’t grasping the big picture: “You can’t see the forest for the trees.” That’s especially apt because many discussions of primate evolution focus on the fine-branch niche: the thin and flexible terminal branches of angiosperm trees. This concept is important for understanding primate evolution, but a terminal arbor is only one part of a tree, and a tree is only one part of a forest. Also important are the ecological communities that forests supported and the effects of global climate change on forest ecology. Tropical and subtropical forests require warm and moist soil, which in turn requires a warm and humid atmosphere. For each degree Celsius that the atmosphere warms, it can hold ~7% more moisture. During periods of global warming and stable warmth, forests thrived and expanded; during periods of global cooling, forests dried and contracted. As they did, woodlands and savannas took over some of their territory. That’s the big picture: the forest, not the trees. Ancestral primates thrived in the fine-branch niche, but only in the context of healthy, moisture-dependent forests. Deforestation drove many primates to extinction, while others adapted to new challenges and opportunities.

Figure 4.4 Global surface temperature during the Cenozoic. Green arrows indicate the onsets of major episodes of global cooling; blue arrows and labels show selected periods of warming or thermal stability. The blue shapes at the top correspond to times when a persistent ice sheet covered either the Antarctic or Arctic, with the thickness of the ice sheet roughly indicated by the thickness of the blue shading. The red curve shows a highly smoothed average temperature; the blue curves show temperatures with less smoothing. These estimates depend on the fact that the lighter 16O is more abundant during warmer periods, whereas the heavier 18O is more abundant during cooler periods. Abbreviations: EECO, early Eocene climatic optimum; EOCT, Eocene–Oligocene climatic transition; Ma, million years ago; MECO, middle Eocene climatic optimum; MMCO, middle Miocene climatic optimum; MMCT, middle Miocene climatic transition; PETM, Paleocene–Eocene thermal maximum; P-P, Pliocene-Pleistocene. Adapted from T. Westerhold et al., An astronomically dated record of Earth’s climate and its predictability over the last 66 million years, Science 369, 1383–7, 2020.

Figure 4.4 highlights several episodes of climate change during the Cenozoic:

•

• • • • • •

Rainforests thrived in the warmth of the Paleocene (~66–56 Ma), which ended about the same time as the Paleocene– Eocene thermal maximum (PETM). This huge spike in global temperatures occurred ~56–55 Ma, and it made for hot times in a wet world. Conditions conducive to dense rainforests continued throughout the early Eocene climatic optimum (EECO), which extended from ~54–50 Ma. After ~50 Ma, a long period of gradual global cooling was interrupted by a period of relative thermal stability called the middle Eocene climatic optimum (MECO) from ~45–40 Ma. This period of warmth enabled rainforests to continue dominating the globe throughout most of the Eocene, which provided the resources and protection that primates needed. Gradual cooling resumed ~40 Ma, a period of climate change that continued for the remainder of the Eocene. Forests began to contract. Then, ~34 Ma, an abrupt global-cooling event called the Eocene–Oligocene climatic transition (EOCT) stressed forest habitats over much of the world. This chilling turn of events killed off a large number of animal species, primates among them. It’s a key date in the history of primates. After the EOCT, the Oligocene and early Miocene were characterized by ~15 million years of relative thermal stability, followed by a warming trend called the middle Miocene climatic optimum (MMCO) from ~18–15 Ma. Then, ~14 Ma, global cooling resumed and continued for 1.5 million years or so: the middle Miocene climatic transition (MMCT). Forests contracted yet again, and not for the last time. Another cooling trend began later in the Miocene, ~9.6 Ma, which initiated the Vallesian crisis: a time of many primate extinctions. About 2.9 Ma, the first of several periods of cooling and glaciation (Ice Ages) affected primate evolution during the Pliocene and Pleistocene, and some previously forested areas turned into treeless tundra (as they remain today). The last Ice Age ended 11,700 years ago.

The causes of global cooling include mountain building after tectonic plates collide. For example, ~55 Ma India collided with Asia, which produced the Himalayas. The exposure of such massive rock surfaces led to a decrease in atmospheric carbon dioxide via a weathering process that sequestered carbon. Continental drift also changed ocean currents, abruptly shutting down some and initiating others, which may have contributed to the global cooling of ~34 Ma (the EOCT). Cooling of the atmosphere exerted a major influence on primate evolution, mainly via its effects on rainforests. As the atmosphere dried, forests changed. Figure 4.4 highlights the onsets of six cooling trends, some of which are important for understanding primate evolution. Specifically, the late Eocene (~40 Ma) and middle Miocene (~14 Ma) cooling trends produced powerful selective pressures on the primates living then. Later chapters propose that some of these influences contributed to cortical expansion. Chapter summary According to molecular divergence dates, the four major primate lineages—lorisiforms, lemuriforms, tarsiers, and anthropoids—all emerged during the Mesozoic, before the end-Cretaceous extinction of ~66 Ma (Figure 4.2A). However, no fossil evidence documents the existence of Mesozoic primates. Figure 4.2B accounts for some of this discrepancy by correcting for variation in mutation rates. Most likely (but by no means certainly), some primates survived the extinction. Perhaps the Mesozoic lineage that eventually produced primates resembled contemporaneous eutherians so closely that they have no diagnostic traits to identify them as primates.18, 19 A group of Euarchontan fossils called plesiadapiforms appears very early in the Paleocene fossil record, and most (but not all) paleontologists classify them as stem primates. Plesiadapiforms quickly became the dominant placental mammals in the forests of North America, which were much more tropical and extensive than they are today. Chapter 7 deals with fossil evidence about plesiadapiform brains, including the size of their neocortex. From ~50 Ma onward, the earth experienced a series of global cooling trends: sometimes sharp and steep like the one that ushered in the Oligocene, and sometimes long and gradual, separated by periods with relatively stable temperatures (Figure 4.4). Global cooling caused the dislocation and extinction of many arboreal species because a cooler, drier atmosphere decreases soil moisture and causes rainforests to contract, leaving dry forests, open woodlands, grasslands, and savannas in their place. At least some primates survived each episode of climate change, and amid waves of extinction, their cortex changed. Later chapters explore when, in which primate lineages, and why those changes occurred. In the epigraph of this chapter, Stephen Jay Gould links the extinction of dinosaurs to the evolution of large brains and novel self-representations—and he does so in a single sentence! The rest of this book does more-or-less the same thing, but it takes a little longer. After the ecological recovery that began ~66 Ma, open niches presented surviving species with many opportunities, and primates were among the mammals that exploited the warm, wet, densely forested world of the Paleocene and Eocene. At first, primates were much like other placental mammals, but eventually that changed, as the next chapter explains. References 1. 2. 3. 4. 5. 6. 7. 8.

Janečka, J.E., Miller, W., Pringle, T.H., Wiens, F., Zitzmann, A., Helgen, K.M., Springer, M.S., & Murphy, W.J. Molecular and genomic data identify the closest living relative of primates. Science 318, 792–4 (2007). Pozzi, L., Hodgson, J.A., Burrell, A.S., Sterner, K.N., Raaum, R.L., & Disotell, T.R. Primate phylogenetic relationships and divergence dates inferred from complete mitochondrial genomes. Molecular Phylogenetics and Evolution 75, 165–83 (2014). Steiper, M.E. & Seiffert, E.R. Evidence for a convergent slowdown in primate molecular rates and its implications for the timing of early primate evolution. Proceedings of the National Academy of Science USA 109, 6006–11 (2012). Murphy, W.J., Foley, N.M., Bredemeyer, K.R., Gatesy, J., & Springer, M.S. Phylogenomics and the genetic architecture of the placental mammal radiation. Annual Review of Animal Bioscience 9, 5.1–25 (2021). Hedges, S.B., Marin, J., Suleski, M., Paymer, M., & Kumar, S. Tree of life reveals clock-like speciation and diversification. Molecular Biology and Evolution 32, 835–45 (2015). Upham, N.S., Esselstyn, J.A., & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. Public Library of Science, Biology 17, e3000494 (2019). Renne, P.R., Deino, A.L., Hilgen, F.J., Kuiper, K.F., Mark, D.F., Mitchell, W.S., Morgan, L.E., Mundil, R., & Smit, J. Time scales of critical events around the Cretaceous– Paleogene boundary. Science 339, 684–7 (2013). Brusatte, S. The Rise and Reign of the Mammals: A New History from the Shadow of the Dinosaurs to Us (Mariner Books, New York, 2022).

9.

Larson, D.W., Brown, C.M., & Evans, D.C. Dental disparity and ecological stability in bird-like dinosaurs prior to the end-Cretaceous mass extinction. Current Biology 26, 1325–33 (2016). 10. Martin, A.J. The Evolution Underground: Burrows, Bunkers, and the Marvelous Subterranean World Beneath Our Feet (Pegasus, New York, 2017). 11. Lyson, T.R., Miller, I.M., Bercovici, A.D., Weissenburger, K., Fuentes, A.J., Clyde, W.C., Hagadorn, J.W., Butrim, M.J., Johnson, K.R., Fleming, R.F., Barclay, R.S., Maccracken, S.A., Lloyd, B., Wilson Mantilla, G.P., Krause, D.W., & Chester, S.G.B. Exceptional continental record of biotic recovery after the Cretaceous–Paleogene mass extinction. Science 366, 977–83 (2019). 12. Wilson Mantilla, G.P., Chester, S.G.B., Clemens, W.A., Moore, J.R., Sprain, C.J., Hovatter, B.T., Mitchell, W.S., Mans, W.W., Mundil, R., & Renne, P.R. Earliest Paleocene purgatoriids and the initial radiation of stem primates. Royal Society Open Science 8, 210050 (2021). 13. Martin, R.D., Soligo, C., & Tavare, S. Primate origins: implications of a cretaceous ancestry. Folia Primatologica (Basel) 78, 277–96 (2007). 14. O’Leary, M.A., Bloch, J.I., Flynn, J.J., Gaudin, T.J., Giallombardo, A., Giannini, N.P., Goldberg, S.L., Kraatz, B.P., Luo, Z.X., Meng, J., Ni, X., Novacek, M.J., Perini, F.A., Randall, Z.S., Rougier, G.W., Sargis, E.J., Silcox, M.T., Simmons, N.B., Spaulding, M., Velazco, P.M., Weksler, M., Wible, J.R., & Cirranello, A.L. The placental mammal ancestor and the post-K-Pg radiation of placentals. Science 339, 662–7 (2013). 15. Cachel, S. Fossil Primates (Cambridge University Press, Cambridge, 2015). 16. Deutsch, A.R., Dickinson, E., Whichard, V.A., Lagomarsino, G.R., Perry, J.M.G., Kupczik, K., & Hartstone-Rose, A. Primates body mass and dietary correlates of tooth root surface area. American Journal of Biological Anthropology 177, 4–26 (2021). 17. Kay, R.F. The functional adaptations of primate molar teeth. American Journal of Physical Anthropology 43, 195–216 (1975). 18. Halliday, T.J., Upchurch, P., & Goswami, A. Resolving the relationships of Paleocene placental mammals. Biological Reviews of the Cambridge Philosophical Society 92, 521–50 (2017). 19. Halliday, T.J., dos Reis, M., Tamuri, A.U., Ferguson-Gow, H., Yang, Z., & Goswami, A. Rapid morphological evolution in placental mammals post-dates the origin of the crown group. Proceedings in Biological Science 286, 20182418 (2019).

* OK, maybe a little.

5 Arboreal adaptations Overview The Paleocene began with empty arboreal niches in recovering rainforests. A group of stem primates—the plesiadapiforms—entered these habitats and had specializations resembling those of arboreal rodents. During the Eocene, many animals exploited the moist, dense forests that thrived on the northern continents, but primates did so differently. A suite of adaptations coalesced in early Euprimates: skeletal and muscular mechanisms for pedal and manual grasping of branches and items on them; fingernails, toenails, and papillary ridges on fingers and toes to secure a precisely calibrated grip; a hindlimb-dominated, leaping–grasping mode of locomotion suited to movement among flimsy and discontinuous branches; a novel gait that kept the animal’s center of gravity passing over branches rather than alongside them; and forward-facing eyes, along with other changes in vision that transformed primates into “visual animals”: a reversal of the de-emphasis on vision that occurred in Mesozoic mammals. The reason why new concepts in any branch of science are hard to grasp is always the same; contemporary scientists try to picture the new concept in terms of ideas which existed before. —Freeman Dyson, Scientific American, vol. 199, p. 76, 1958

Introduction In this chapter, primates ape rodents; lorisiforms leap; and foveas fondle food. But I begin with the epigraph. The late Freeman Dyson—a physicist best known to the general public as a climate-change denier—used three metaphors in a single sentence: to “grasp” a concept, a “branch” of science, and to “picture” new ideas. As this chapter explains, the ability to grasp branches was fundamental to primate evolution: literal grasping and literal branches, not metaphorical versions. And in one primate species, humans, the ability to “picture” a concept is the ultimate affirmation of vision’s primacy among the senses. However, vision wasn’t always so important to the ancestors of primates. Mesozoic mammals foraged at night and kept a low profile during the day.1 This activity pattern had a major effect on the cerebral cortex because Mesozoic mammals deemphasized vision in favor of audition, somatic sensation, and olfaction. Accordingly, reconstructions of the ancestral mammalian cortex2 include: • • •

Auditory areas, which process acoustic inputs from a cochlea functionally enhanced by the conversion of ancestral jaw bones into the ossicles of the middle ear. Somatosensory areas, which process inputs from sensory receptors associated with hairs and skin, among other mechanoreceptors.3 An extensive piriform (olfactory) cortex and large olfactory bulbs. (In addition, ancestral mammals evolved an expanded olfactory epithelium, which houses a diverse assortment of odor receptors.4 In fact, a substantial amount of the mammalian genome encodes olfactory receptors.4,5)

Primates began with a cortex dominated by these areas, as did many mammals. The fossil record shows that, at first, primates retained much of the generalized dental and skeletal morphology common to many eutherians,5 and there’s no reason to suppose that their cortex was anything special, either. Then, primate specializations began accumulating, many related to vision and many involving the cortex. Later chapters deal with that. This chapter sets the cortex aside for the most part to focus on other primate adaptations. Primates true and stem If evolutionary biologists consider it useful to designate some species as true primates (Euprimates), and if that group includes all living primates (which it does), then some other kind of primates must have existed in the past. Because no one would stand for calling them faux primates, they go by a name based on a type specimen. Chapters 2 and 4 introduced these animals: plesiadapiforms (sometimes spelled plesiadapiformes). Their name derives from a fossil genus called Plesiadapis. The prefix plesi– refers to a likeness, in this case to a well-established fossil Euprimate called Adapis; and the suffix –forms is another reference to likeness. So, a plesiadapiform is a fossil primate that has some resemblance to both the true primate, Adapis, and to other plesiadapiforms. Most paleontologists consider them to be stem primates, although some dissent. Figure 5.1 includes a selection of plesiadapiform genera, along with some Euprimates that appear in the fossil record from the Eocene onward. To simplify the discussion, I restrict the text and figures to genus-level designations for the most part and ignore family and species designations. There’s a downside to this approach. What I present here is a compromise: enough to understand the text and figures, but not enough to decipher the specialty literature, at least not easily. Papers and reviews in paleontology assume that readers know the families, genera, and species of plesiadapiforms and other extinct Euarchontans, and they rarely explain enough for neuroscientists to follow along. Our revenge is that we can make our publications equally inscrutable to them.

Figure 5.1 An evolutionary tree of primates. Fossil primates are labeled in colored font. For the most part, they lived during the epoch with the matching color at the bottom. The question mark indicates an uncertain classification. The black shapes to the right represent diversification into many species, which ranges from ~20 tarsier species to more than 100 cercopithecoids (Old World monkeys). The height of each black shape provides a very rough indication of changes in diversity and a little about timing. For example, the diversification of hominoids (humans and apes) preceded that of cercopithecoids, but hominoid dominance over cercopithecoids reversed during the late Miocene. Lemur diversity has decreased recently because of anthropogenic habitat destruction. Plesiadapiform relations adapted from S.G.B. Chester et al., Oldest known Euarchontan tarsals and affinities of Paleocene Purgatorius to Primates, Proceedings of the National Academy of Sciences USA 112, 1487–92, 2015. Adapiform and omomyiform phylogeny mostly adapted from A. Ramdarshan and M.J. Orliac, Endocranial morphology of Microchoerus erinaceus (Euprimates, Tarsiiformes) and early evolution of the Euprimates brain, American Journal of Physical Anthropology 159, 5–16, 2016, with a variant on the relation of omomyiforms, tarsiers, and anthropoids that follows S. Bajpai et al., The oldest Asian record of Anthropoidea, Proceedings of the National Academy of Sciences USA 105, 11093–8, 2008.

In what follows, I assume that plesiadapiforms were a paraphyletic group of stem primates, as illustrated in Figure 5.1. For interested readers, Silcox et al.6 discuss this classification and several alternatives. Not every expert accepts plesiadapiforms as primates, but as far as I know no one disputes their classification as Euarchontans. It’s tempting to think of plesiadapiforms as a stage that primates went through during a transition from generalized placental mammals to Euprimates, but evolution works in more subtle ways. Instead, several Euarchontan lineages probably accumulated some primate-like traits independently, with one of them giving rise to Euprimates and others to various plesiadapiform lineages. So, rather than thinking about plesiadapiforms as a “thing” that gave rise to Euprimates, it’s probably better to view them as several things, with a variety of relationships to Euprimates and to each other. Part of the controversy about primate origins involves a disagreement about where to place the word “Primates” on a phylogenetic tree like Figure 5.1. Some authorities prefer to move that label to the place that “Euprimates” occupies in the figure and thereby exclude plesiadapiforms from the group of extinct and extant animals classified as primates.7 If the figure reflects evolutionary relationships accurately, then moving the label “Primates” doesn’t alter much of substance. It would simply mean that plesiadapiforms, such as Purgatorius, were the extinct Euarchontans most closely related to primates, rather than primates per se. So, some of these debates are merely semantic. Of course, I’m a neuroscientist, so you should view my opinion on this matter skeptically. Accordingly, I invoke the authority of a paleontologist, Brusatte8 (p. 369), who wrote: Purgatorius is a plesiadapiform, a tongue-twister name for the ancestral stock from which primates evolved. Some scientists . . . [call] them primates, others prefer to call them “stem primates” and restrict the primate title to the crown group consisting of today’s species and all descendants of their most recent common ancestor (these are the “true primates” [Euprimates] . . .) Whatever. It’s nomenclature, a human exercise in organization and not important. What is important is that Purgatorius is the oldest-known animal on the primate bloodline, after it diverged from the other major groups of mammals, and the first to show key changes to diet and behavior indicative of a new lifestyle.

There’s another angle, however. The debate about where to place the label “Primates” in Figure 5.1 bears on Cartmill’s visual predation hypothesis about primate origins, which posits that primates began as insectivores.9,10,11,12 In the face of conflicting evidence from plesiadapiforms, most of which were herbivores,6,13 rejecting them as primates helps support Cartmill’s idea. The inference (sometimes disputed) that early Euprimates weighed less than 280 grams, and often much less, reinforces the

idea that they could have survived on insects, although they probably had a mixed diet of fruit, insects, blossoms, tender leaves, and tree saps.14 As Sussman et al.15 (p. 95) concluded: . . . our earliest primate relatives were likely exploiting the products of co-evolving angiosperms, along with insects attracted to fruits and flowers, in the slender supports of the terminal branch milieu.

There are reasons for caution beyond a debate about labels. Most phylogenies place plesiadapiforms among the primates, but others don’t.16,17,18 For example, Figure 6.2 situates plesiadapiforms, including Purgatorius, as more distantly related to Euprimates than colugos, which no one classifies as primates. Figure 5.1 follows Silcox et al.,6 Cachel,5 and Brusatte8 in placing the label “Primates” at the root of an evolutionary tree that includes both plesiadapiforms and Euprimates. The alternatives might change some of the wording in this and subsequent chapters, but not much of the substance. Into the trees Gnawing away at primate evolution Our intuition about primates depends entirely on Euprimates because all modern species are in this group. Plesiadapiforms weren’t. During the Paleocene, plesiadapiforms adapted to niches that arboreal rodents exploit today, and they had many rodent-like traits. True rodents don’t appear in the fossil record until the end of the Paleocene, after which they drove plesiadapiform primates to extinction (Chapter 6, “The prime of plesiadapiforms”).5 As the Preface explained, the term primate refers to primacy, so the idea that rodents defeated early primates in a struggle for survival might seem unlikely to neuroscientists. However, even the paleontologists most assertive in classifying plesiadapiforms as primates acknowledge that they did not resemble modern primates very closely. Euprimates are sometimes called “primates of modern aspect,” which implies that the plesiadapiforms were primates of ancient aspect and quite different.19 During the evolution of plesiadapiforms, different lineages developed various primate traits, sometimes in strange combinations. Accordingly, plesiadapiforms had only some of the traits of modern primates, and they had many traits that modern primates lack.5 For example, one plesiadapiform, Carpolestes, had highly enlarged, blade-like premolars that resemble the dentition of some modern marsupials.5 No modern primates have teeth like that. The discussion associated with Figure 4.3 said that, compared with the molars of insectivorous eutherians, those of plesiadapiforms had reduced height and sharpness. Their broader basins suggest a diet that included fruits and nuts because molars with that characteristic process such foods efficiently.15 Purgatorius had this trait, as did another early plesiadapiform, Pandemonium.20,21 Figure 4.3 indicates a shift toward more frugivory as plesiadapiforms radiated during the first four million years of the Paleocene. The type specimen for plesiadapiforms, Plesiadapis, had several rodent-like traits. It had a tail something like modern squirrels, long and bushy, and it had digits with claws on both its hindpaws and forepaws.5 Like other plesiadapiforms, Carpolestes and Plesiadapis had long snouts and laterally situated eyes, as most Euarchontoglires do.5,22

Figure 5.2 Dentition in plesiadapiforms. A lateral view of a Plesiadapis skull. Adapted from S. Cachel, Fossil Primates, Cambridge University Press, Cambridge, 2015.© Cambridge University Press

As perhaps their most important specialization, plesiadapiforms had a pair of forward-projecting incisors separated from molars and premolars by a toothless gap. Incisors like that are called procumbent (forward leaning), and the gap is known as a diastema (Figure 5.2). In rodents, the gap enables them to draw in their cheeks to block the ingestion of inedible material as they gnaw through the less nutritious parts of plants. Presumably, the same front-to-back arrangement of incisors, diastema,

and molars (or premolars) enabled plesiadapiforms to avoid ingesting the nutritionally low-value coverings—exocarps, pericarps, and husks (seed coats)—that protect fruits and seeds. Once they reached the nutritious flesh of fruits or the endosperm of seeds, they could relax their cheeks to swallow the food. Plesiadapiforms flourished during the Paleocene (~66–56 Ma). When Euprimates appear in the fossil record, in the early Eocene (~55 Ma), they have the entire suite of skeletal adaptations that characterizes modern primates: (1) grasping hands and feet, with finger- and toenails replacing most or all claws; (2) a distinctive, hindlimb-dominated form of locomotion; and (3) forward-facing eyes, along with other visual adaptations. The next three sections take up these sets of traits, in turn, followed by a brief mention of some additional primate adaptations. Grasping primate success Ancestral Euprimates had hands and feet capable of precisely controlled, visually guided grasping, which empowered them to exploit a small-branch niche in dense forests. Several other mammals—including tree shrews, colugos, rodents, and marsupials—also entered arboreal niches, but Euprimates did so differently. Early Euprimates not only foraged in the finebranch niche, but they also adapted to a life largely confined to it.15,23 Tree shrews, for example, often forage at ground level despite their name. The primate way of moving among branches contrasts with that of other animals. Colugos, for example, glide from branch to branch, and most arboreal mammals cling to branches and tree trunks with sharp, pointy claws. These scansorial (climbing) species include arboreal rodents and tree shrews, among other animals. Even large cat species climb trees this way, including tigers, jaguars, and cheetahs. In contrast, early Euprimates traveled along thin branches via pedal and manual grasping. Long digits that function in conjunction with fingernails and toenails were among the Euprimate adaptations that enabled them to move effectively along a substrate of unstable and discontinuous branches. The success of birds and arboreal rodents shows that many lineages can exploit arboreal resources simultaneously, often via niche partitioning. However, the grasping specializations of early Euprimates distinguished them from their competitors. It’s partly for that reason that arguments for a homology between primate and rodent mechanisms for reaching and grasping24 are so misguided. Grasping with long fingers and short wrist bones is fundamental to the primate way of life, but not to rodents. And, of course, rodents have nothing remotely resembling the pedal grasping mechanisms of primates. Box 12.1 elaborates on these points. As mentioned earlier, some ideas about primate origins have stressed visually guided predation and insectivory as the main driving force for forward-facing eyes and grasping hands.10,11,12 However, most paleontologists emphasize other dietary specializations, mainly herbivorous or omnivorous diets.6,13,15 These ideas emphasize the fact that grasping hands provided primates with two sets of advantages: a new way to move through trees and instruments for obtaining items on or attached to branches (including bending branches in a way that other mammals can’t). Insects were one such item, but there were many others. The precise association between diet and grasping remains uncertain, but as Brusatte8 (p. 371) concluded: What does seem to be linked . . . is the evolution of grasping and fruit-eating. Features of the hands and feet that permitted better branch-grabbing evolved alongside dental features enabling better fruit chewing.

The insect-free diet of Darwinius, a middle Eocene Euprimate dated to ~47 Ma, reinforces this point. Some experts consider Darwinius to be closely related to strepsirrhines, others to haplorhines.14,25,26 Classification aside, the animal’s fossil is so well preserved that paleontologists have described the contents of its digestive system. It died in a lake within a tropical rainforest, and it foraged for fruit and leaves but not insects. Nonprimate fossils in the same place had insect remains in their guts, which implies that Darwinius had the opportunity to eat insects but chose other foods, instead.5 From a neuroscience perspective, it would be easy to gloss over the evolution of grasping hands and feet without giving these primate innovations the attention they deserve. After all, they are not neural structures. What’s more, the brain mechanisms that control reaching and grasping fall within the domain of motor systems research, which never ranks among the most popular subdisciplines in neuroscience.* But along with the visual adaptations of primates, the neural control of pedal and manual grasping was a major evolutionary breakthrough. Eventually, these mechanisms depended on new cortical areas and networks (Chapter 12), and they enabled early Euprimates to gain the foothold (literally) they needed to compete successfully with arboreal rodents and birds. According to fossil evidence, grasping specializations evolved in stages, in a mosaic-like fashion.27 The oldest plesiadapiform specimens, the aforementioned Purgatorius, date to ~66–62 Ma. They had some specializations for an arboreal life but lacked the grasping adaptations that characterize modern primates. These animals probably couldn’t leap very well, but their tarsal bones permitted movements with more degrees of freedom, and thus ankle flexibility, than in the ancestral Euarchontan condition.28 Other plesiadapiform species had similar traits. Thus, the early plesiadapiforms had some of the Euprimate adaptations to an arboreal life, but very few of the ones that permitted Euprimate-like grasping. Another plesiadapiform, Torrejonia, dates to ~62 Ma. It resembled other plesiadapiforms in having limbs adapted for arboreal foraging and clinging to variously oriented branches.29 Plesiadapis (~58–55 Ma) had elongated digits, which improved manual grasping. These stem primates could grasp things, but they lacked some important characteristics of Euprimate-like grasping, such as nails and a divergent hallux, a condition in which the big toe (hindlimb digit 1) has a different orientation than the other toes. Carpolestes (~55 Ma) had most of the grasping specializations of Torrejonia and Plesiadapis, but—in contrast to both of them—it also had a divergent hallux with a toenail, which endowed these animals with an enhanced grasping ability.22 In summary, most plesiadapiforms shared the Euprimate trait of long proximal phalanges (the bones at the base of fingers and toes) and short metacarpal and metatarsal bones (which connect digits to the wrist and ankle, respectively). Boyer et al.30 found that all known plesiadapiforms and early Euprimates had hands of about the same size relative to their bodies. They also had similar ratios of proximal phalange to metacarpal size. According to Boyer and his colleagues, manual grasping emerged before Euprimate-like pedal grasping during the transition from stem primates to early Euprimates (Figure 5.1).31 If so, then feeding specializations probably preceded those for leaping. Eventually, though, leaping led to a new way of life.

Branch managers In conjunction with their grasping adaptations, Euprimates adopted a new mode of locomotion for traversing small, spatially separated, variably oriented, and often-unstable branches.32 A leaping–grasping mode of locomotion and a distinctive primate gait probably originated as adaptations to this substrate. Leaping to a graspable branch enables primates to move swiftly and therefore serves as an efficient predator avoidance strategy.33 Unlike most animals, many primate leapers both launch with their hindlimbs and land on them. The long hindlimbs of the oldest Euprimate fossils indicate that this adaptation occurred early in Euprimate evolution. In some modern primates, leaping can set world records. One lorisiform species, Galago moholi, can propel itself across 4 meters of horizontal space while climbing 2 meters higher: a higher multiple of leap-distance to body-length than any other vertebrate.33 Early Euprimates also evolved what’s called a diagonal sequence–diagonally coupled (DS–DC) gait.32,34 In this way of moving, diagonal limbs, such as the left forelimb and right hindlimb, work together in alternation with the other diagonal pair of limbs. The hindlimbs remain in contact with the substrate longer and generate more force than the forelimbs, which establishes the hindlimb-dominated mode of locomotion that characterizes most primate gaits. As they move through the arbor of trees, small primates use grasping feet to support their weight on branches that they had tested previously with their grasping hands. By transferring force from one of their hindlimbs to the contralateral forelimb, the animal’s center of mass passes over the branch rather than alongside it, which promotes stability. The next foothold on a handhold-tested branch frees the contralateral hand to test the next substrate for stiffness and stability, and so forth in a cycle of footholds and contralateral handholds. This diagonal sequence minimizes instances in which stability depends only on the forelimb and hindlimb on one side of the body, although the latter kind of gait, common in quadrupeds, sometimes helps in climbing and clinging to vertically oriented branches.35 Early Euprimate locomotion thus depended on adaptations of hand and foot morphology that promoted leaping, climbing, and grasping, including relatively long legs, long digits to secure footholds and handholds, and both fingernails and toenails to secure a grip on branches and quietly sneak up on insect prey. Neural changes accompanied these developments, including modifications of central pattern generators in support of a DS–DC gait, along with improved control of pedal grasping movements. Chapter 12 pursues the latter point. Looking ahead Later in the transition to Euprimates, another novel trait emerged: forward-facing eyes. Like many primate synapomorphies, this trait evolved independently in other vertebrates, such as birds of prey (raptors) and carnivores. Plesiadapiforms lacked forward-facing eyes, as do the modern nonprimate Euarchontans: tree shrews and colugos. The forward-facing eyes of Euprimates improved reaching and grasping under visual guidance, as well as aiming aspects of leaping–grasping locomotion.36,37,38 They did so, in part, because this new eye orientation created a large binocular visual field, which enhanced stereoscopic depth perception. Other Euarchontans, such as tree shrews, can see straight ahead, but their binocular field of vision is small. Additional advantages include an improved ability to see around branches and other obstructions when they block the line of sight from one eye39 and improved dim-light vision via signal summation from the two retinas.40,41 There are discussions in the literature about which of these advantages drove the evolution of forward-facing eyes. Regardless of which came first, or which one made the largest contribution, they all contributed to the fitness of early Euprimates. In part because of their convergent orbits, Euprimates became “visual animals.”42 Accordingly, a major theme in primate evolution was a reversal of the de-emphasis on vision that occurred in Mesozoic mammals. The prominence of vision set in train a lifestyle that changed the brain, in general, but especially the cortex. Additional primate specializations include a small cornea, which increases visual acuity, and an unusually high density of neurons in the striate (V1) cortex and extrastriate visual areas.43 Also, bony structures on the lateral rim of each orbit, called postorbital bars, separate the eye and the temporal muscle. These stiff partitions protect the eye from deformation during large eye movements and during the contraction of masticatory muscles. Chapter 12 discusses neural adaptations that distinguish the visual system of primates from that of other mammals, including changes in retinotectal projections, the emergence of new visual areas, and new features in older areas. Most such discussions emphasize vision’s role in identifying objects, but vision has another role, which was central to the life of early Euprimates: the control of reaching and grasping movements. Readers interested in the neural mechanisms of reaching and grasping—especially the role that visual coordinate frames play in the motor system of primates—might consult my monograph with Shadmehr on the subject.37 Although out of date, it remains a useful introduction to the principles of visuomotor control in primates. It’s difficult to avoid drawing on our own visual experience when considering early primates, but their vision differed from ours in many ways. Crucially, they lacked a fovea, which evolved later: in haplorhines for primates and convergently in several other vertebrates.44 Accordingly, early Euprimates depended on a much lower level of visual acuity than we do: a conclusion that applies both to the control of movements and the identification of objects. Perhaps the visual experience of early Euprimates resembled our peripheral vision. People with macular degeneration lose central vision but retain the ability to identify objects with peripheral vision, provided that the objects have high contrast and are not crowded in a cluttered array.45 If early Euprimates had an entire retina that functioned something like our peripheral retina, it could have performed adequately across the entire visual field, even without a fovea. As Chapter 12 explains, a new cortical area, the frontal eye field, evolved in the prefrontal cortex of primates, and one of its roles is to resolve the problem posed by the crowding of items into a small part of visual space. This function results in a perceptual phenomenon called pop-out, in which an item that differs from others in its vicinity attracts attention in a rapid and automatic way. None of this, of course, rules out the possibility that early Euprimates had their own retinal specializations, attuned to their mode of foraging, as some strepsirrhines have today.

Feeling fingers and other specializations In addition to visual adaptations, modern primates also have specializations of the somatosensory and olfactory systems, and it seems likely that they also evolved in early Euprimates.5 Papillary ridges, which form fingerprints, increase friction at the surface of the glabrous (hairless) skin of the hand and fingers, and the same goes for the foot and toes. Euprimate fingers and toes also have dense concentrations of Meissner’s corpuscles, especially in pads on the distal digits. These mechanoreceptors lack the protective membrane seen in other mammals, so primate fingers and toes are extraordinarily sensitive to pressure on or stretching of the skin.5 Meissner’s corpuscles send signals to the cortex, which provide feedback for the control of fine movements.37 The fingertips of primates are so sensitive that they are sometimes called a “tactile fovea” by analogy with the actual fovea.46 The combination of papillary ridges with exquisitely sensitive cutaneous receptors endowed primates with the peripheral part of a mechanism for finely calibrated force control, which—along with grasping digits and digital nails—promoted their grip on and maneuvering among the branches of angiosperm trees. It’s somewhat ironic that early Euprimates had structures for tactile sensations that some researchers have called a “fovea,” although they lacked an actual fovea. The accessory olfactory bulbs, which process pheromone odorants transduced by the vomeronasal organ, are absent entirely in catarrhines (Old World monkeys, humans, and apes). However, in strepsirrhines both the main and accessory olfactory bulbs have roughly the same absolute and relative size as in Euarchontans generally.47 In anthropoids (and in haplorhines, more generally), the main olfactory bulb decreased in size relative to the rest of the brain and the body (Figure 8.4C). Of less interest to neuroscientists, primates have several additional synapomorphies: an elaborate, two-horned placenta; a lengthened period of postnatal development; the production of only one offspring per pregnancy (most of the time); and a shortened snout (which reversed later in some lineages, such as macaque monkeys and baboons). The bone encasing the ossicles of the middle ear, called the tympanic bulla, arises from the petrosal bone at the base of the skull, which differs from its origin in other mammals.5 Of special interest to neuroscientists, large brains and an extensive neocortex also characterize modern primates. However, those developments come later in two senses: in evolution and in this book (Chapters 7 and 8). Chapter summary Plesiadapiform primates had some Euprimate traits in various combinations, but not all of them. None had forward-facing eyes, and most lacked important aspects of the grasping specializations that characterize Euprimates. Put another way, plesiadapiforms had some adaptations for life in the trees, but not the entire suite of primate adaptations that had accumulated by the time of early Euprimates: grasping hands and feet; flat fingernails and toenails; papillary ridges with sensitive touch receptors; forward-facing eyes, along with other visual adaptations; and hindlimb-dominated locomotion with a diagonal sequence–diagonally coupled (DS–DC) gait. Although this chapter discusses these primate synapomorphies one by one, it’s also important to consider them together. Rather than a single keystone trait, it was the combination of primate specializations that accounted for their success. Many primate traits evolved independently in other vertebrates, but none developed them all. Birds also adapted to a fine-branch niche, and many have forward-facing eyes. But, for obvious reasons, they can’t use their forelimbs for grasping and, as bipeds, they have nothing like a DS–DC gait. Carnivores evolved forward-facing eyes and primate-like visual specializations (in both the retina and cortex), but they don’t have the long fingers or toes that enabled early primates to live full-time in an arboreal niche. Likewise, some marsupials evolved a DS–DC gait and other primate traits, including primate-like grasping, but they didn’t develop the kind of cortex that primates have. Cortical considerations This chapter concentrates on subjects outside the neurosciences, but they have direct relevance to our main topic. Early Euprimates had the synapomorphies that characterize modern primates, with at least one major exception. As Chapter 7 explains, they had rodent-size brains and a rodent-size cortex.

Figure 5.3 Eocene geography. The location of continents ~50 million years ago. Note that North America, Greenland, and Eurasia were connected by land bridges, but Africa, India, South America, and Australia were island continents then, as the latter remains. Adapted from Benjamin J. Burger, Wikimedia Commons general license https://commons.wikimedia.org/wiki/File:Changing-Position-of-Continents-Climate-DueTo-Ocean.jpg#file, 2020.

As noted in this chapter’s epigraph, it’s difficult to grasp new ideas by picturing them in familiar terms. An excessive reliance on intuition presents a barrier to understanding the small-cortex primates of the Paleocene and Eocene because they lived in a world utterly unfamiliar to us and because no small-cortex primates exist today. If we can picture these animals in their time and place, it might help us appreciate the challenges they encountered and the adaptations that developed, including the pulses of cortical expansion that initiated something monumental in the history of primates: an evolutionary trend toward large brains dominated by neocortex. To this end, Figure 5.3 portrays the planet as it was about 50 million years ago, when primate brains were still small. Earth was hot and humid enough for tropical rainforests to reach very high latitudes. Primates prospered and migrated extensively through this long-gone world, but the climate was about to change. The first of several Cenozoic cooling trends began ~50 Ma (Figure 4.4), forests began to shrink, and primates faced new challenges, as the next chapter explains. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.

Maor, R., Dayan, T., Ferguson-Gow, H., & Jones, K.E. Temporal niche expansion in mammals from a nocturnal ancestor after dinosaur extinction. Nature Ecology and Evolution 1, 1889–95 (2017). Kaas, J.H. The evolution of brains from early mammals to humans. Wiley Interdisciplinary Reviews: Cognitive Science 4, 33–45 (2013). Rowe, T.B., Macrini, T.E., & Luo, Z.X. Fossil evidence on origin of the mammalian brain. Science 332, 955–7 (2011). Striedter, G. & Northcutt, R.G. Brains Through Time: A Natural History of Vertebrates (Oxford University Press, New York, 2020). Cachel, S. Fossil Primates (Cambridge University Press, Cambridge, 2015). Silcox, M.T., Bloch, J.I., Boyer, D.M., Chester, S.G.B., & Lopez-Torres, S. The evolutionary radiation of plesiadapiforms. Evolutionary Anthropology 26, 74–94 (2017). Seiffert, E.R., Tejedor, M.F., Fleagle, J.G., Novo, N.M., Cornejo, F.M., Bond, M., de Vries, D., & Campbell, K.E. Jr. A parapithecid stem anthropoid of African origin in the Paleogene of South America. Science 368, 194–7 (2020). Brusatte, S. The Rise and Reign of the Mammals: A New History from the Shadow of the Dinosaurs to Us (Mariner Books, New York, 2022). Kirk, E.C., Cartmill, M., Kay, R.F., & Lemelin, P. Comment on Grasping primate origins. Science 300, 741 (2003). Cartmill, M. New views on primate origins. Evolutionary Anthropology 1, 105–11 (1992). Cartmill, M. Rethinking primate origins. Science 184, 436–43 (1974). Cartmill, M. Arboreal adaptations and the origin of the order Primates. In: Functional and Evolutionary Biology of Primates (ed. R. Tuttle) 97–122 (Aldine-Atherton, Chicago, IL, 1972). Silcox, M.T., Sargis, E.J., Bloch, J.I., & Boyer, D.M. Primate origins and supraordinal relationships: morphological evidence. In: Handbook of Paleoanthropology (ed. K. Henke & I. Tattersall) 1053–81 (Springer-Verlag, Berlin, 2015). Bajpai, S., Kay, R.F., Williams, B.A., Das, D.P., Kapur, V.V., & Tiwari, B.N. The oldest Asian record of Anthropoidea. Proceedings of the National Academy of Science USA 105, 11093–98 (2008). Sussman, R.W., Rasmussen, D.T., & Raven, P.H. Rethinking primate origins again. American Journal of Primatology 75, 95–106 (2013). O’Leary, M.A., Bloch, J.I., Flynn, J.J., Gaudin, T.J., Giallombardo, A., Giannini, N.P., Goldberg, S.L., Kraatz, B.P., Luo, Z.X., Meng, J., Ni, X., Novacek, M.J., Perini, F.A., Randall, Z.S., Rougier, G.W., Sargis, E.J., Silcox, M.T., Simmons, N.B., Spaulding, M., Velazco, P.M., Weksler, M., Wible, J.R., & Cirranello, A.L. The placental mammal ancestor and the post-K-Pg radiation of placentals. Science 339, 662–7 (2013). Bloch, J.I., Silcox, M.T., Boyer, D.M., & Sargis, E.J. New Paleocene skeletons and the relationship of plesiadapiforms to crown-clade primates. Proceedings of the National Academy of Science USA 104, 1159–64 (2007). Soligo, C. & Smaers, J.B. Contextualising primate origins: an ecomorphological framework. Journal of Anatomy 228, 608–29 (2016). Simons, E.L. Primate Evolution: An Introduction to Man’s Place in Nature (Plenum, New York, 1972). Fox, R.C., Scott, C.S., & Buckley, G.A. A ‘giant’ purgatoriid (Plesiadapiformes) from the Paleocene of Montana, USA: mosaic evolution in the earliest primates. Palaeontology 58, 277–91 (2015). Fox, R.C., Rankin, B.D., Scott, C.S., & Sweet, A.R. Second known occurrence of the early Paleocene plesiadapiform Pandemonium (Mammalia: Primates), with description of a new species. Canadian Journal of Earth Science 51, 1059–66 (2014). Bloch, J.I. & Boyer, D.M. Grasping primate origins. Science 298, 1606–10 (2002). Fleagle, J.G. Primate Adaptation and Evolution (Academic Press, San Diego, CA, 1999). Sacrey, L.A.R., Alaverdashvili, M., & Whishaw, I.Q. Similar hand shaping in reaching-for-food (skilled reaching) in rats and humans provides evidence of homology in release, collection, and manipulation movements. Behavioural Brain Research 204, 153–61 (2009). Franzen, J.L., Gingerich, P.D., Habersetzer, J., Hurum, J.H., von Koenigswald, W., & Smith, B.H. Complete primate skeleton from the middle Eocene of Messel in Germany: morphology and paleobiology. Public Library of Science One 4, 1–27 (2009). Williams, B.A., Kay, R.F., Kirk, E.C., & Ross, C.F. Darwinius masillae is a strepsirrhine: a reply to Franzen et al. (2009). Journal of Human Evolution 59, 567–73 (2010). Hamrick, M.W. The developmental origins of mosaic evolution in the primate limb skeleton. Evolutionary Biology 39, 447–55 (2012).

28. Chester, S.G.B., Bloch, J.I., Boyer, D.M., & Clemens, W.A. Oldest known euarchontan tarsals and affinities of Paleocene Purgatorius to Primates. Proceedings of the National Academy of Science USA 112, 1487–92 (2015). 29. Chester, S.G.B., Williamson, T.E., Bloch, J.I., Silcox, M.T., & Sargis, E.J. Oldest skeleton of a plesiadapiform provides additional evidence for an exclusively arboreal radiation of stem primates in the Palaeocene. Royal Society Open Science 4, 170329 (2017). 30. Boyer, D.M., Yapuncich, G.S., Chester, S.G.B., Block, J., & Godinot, M. Hands of early primates. Yearbook of Physical Anthropology 57, 33–78 (2013). 31. Boyer, D.M., Seiffert, E.R., Gladman, J.T., & Bloch, J.L. Evolution and allometry of calcaneal elongation in living and extinct primates. Public Library of Science One 8, e6772 (2013). 32. Larson, S.G. Unique aspects of quadrupedal locomotion in nonhuman primates. In: Primate Locomotion: Recent Advances (ed. E. Strasser, J.G. Fleagle, A.L. Rosenberger, & H.M. McHenry) 157–73 (Plenum, New York, 1998). 33. Crompton, R.H. & Sellers, W.I. A consideration of leaping locomotion as a means of predatory avoidance in prosimian primates. In: Primate Anti-predator Strategies (ed. S.L. Gursky & K.A.I. Nekaris) 127–45 (Springer, New York, 2007). 34. Cartmill, M., Brown, K., Atkinson, C., Cartmill, E.A., Findley, E., Gonzalez-Socoloske, D., Hartstone-Rose, A., & Mueller, J. The gaits of marsupials and the evolution of diagonal-sequence walking in primates. American Journal of Physical Anthropology 171, 182–97 (2020). 35. Granatosky, M.C., Schmitt, D., & Hanna, J. Comparison of spatiotemporal gait characteristics between vertical climbing and horizontal walking in primates. Journal of Experimental Biology 222, jeb185702 (2019). 36. Wise, S.P. The evolution of ventral premotor cortex and the primate way of reaching. In: Evolution of Nervous Systems (ed. L.A. Krubitzer & J.H. Kaas) 3, 157–66 (Elsevier, New York, 2007). 37. Shadmehr, R. & Wise, S.P. The Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning (The MIT Press, Cambridge MA, 2005). 38. Wise, S.P. The evolution of the prefrontal cortex in early primates and anthropoids. In: Evolution of Nervous Systems (ed. L.A. Krubitzer & J.H. Kaas) 3, 387–422 (Elsevier, New York, 2017). 39. Changizi, M.A. The Visual Revolution (Benbella, Dallas, 2009). 40. Hughes, A. The topography of vision in mammals of contrasting life style: comparative optics and retinal organization. In: The Visual System of Vertebrates (ed. F. Crescitelli) 615–97 (Springer, New York, 1977). 41. Allman, J.M. Evolving Brains (Freeman, New York, 2000). 42. Ravosa, M.J. & Savakova, D.G. Euprimate origins: the eyes have it. Journal of Human Evolution 46, 357–64 (2004). 43. Collins, C.E., Airey, D.C., Young, N.A., Leitch, D.B., & Kaas, J.H. Neuron densities vary across and within cortical areas in primates. Proceedings of the National Academy of Science USA 107, 15927–32 (2010). 44. Ross, C.F. The tarsier fovea: functionless vestige or nocturnal adaptation? In: Anthropoid Origins: New Visions (ed. C.F. Ross & R.F. Kay) 477–537 (Academic/Plenum, New York, 2004). 45. Wallace, J.M., Chung, S.T., & Tjan, B.S. Object crowding in age-related macular degeneration. Journal of Vision 17, 33 (2017). 46. Hoffmann, J.N., Montag, A.G., & Dominy, N.J. Meissner corpuscles and somatosensory acuity: the prehensile appendages of primates and elephants. Anatomical Record A: Discoveries in Molecular, Cellular, and Evolutionary Biology 281, 1138–47 (2004). 47. Heritage, S. Modeling olfactory bulb evolution through primate phylogeny. Public Library of Science One 9, e113904 (2014).

* In Physiological Psychology (Wadsworth, Belmont, California, 1990), Robert B. Graham (p. xvi) captured that attitude by writing that for many of his colleagues “the motor system is regarded as somewhat less interesting than a 1949 report on farm futures.”

6 Primate paleoecology Overview Primates originated in a world of persistent warmth and widespread rainforests. Plesiadapiform primates thrived during the Paleocene but died out when arboreal rodents won their niches. Euprimates radiated during the Eocene, which ended in a period of global cooling ~40–34 Ma. The resulting deforestation reduced primate habitats and increased competition for arboreal resources. The abrupt global cooling that initiated the Oligocene ~34 Ma exacerbated deforestation at temperate latitudes and caused food production to become highly seasonal and volatile. Afterward, anthropoids became larger animals that foraged diurnally over an extensive home range, initially as slow arboreal quadrupeds. Hominoids appeared ~26–23 Ma and diversified ~17–15 Ma, during a warm period. Global cooling resumed ~14–9 Ma, which caused more deforestation and the extinction of many ape species. As hominoid diversity waned, cercopithecoids and platyrrhines radiated. During the PlioPleistocene, both cercopithecoids and hominins exploited the open habitats that replaced forests. . . . [humans are] descended from a hairy quadruped, furnished with a tail and pointed ears, probably arboreal in its habits, and an inhabitant of the Old World. —Charles Darwin, The Descent of Man, Vol. 2, 1871

Introduction In this chapter, alligators bask in a balmy Upper Canada; rodents and primates fight to the death; and simians sail the southern sea. But I begin by extolling the virtues of paleoecology. To understand why the cortex changed in extinct primates, it helps to know something about their life and times. The reason is simple: It was in some of these animals that the cortex changed. The impediment to understanding extinct primates is also simple, at least to explain: The ecology of their time and place included interactions with species that no longer exist and the relationship of a bygone biological world to a nowvanished physical one. Paleocene plesiadapiforms Hot-house habitats Paleocene primates lived in physical conditions almost unimaginable today. The usual way to emphasize the strangeness of their ecosystem is to call it a hot-house world: an endless “summer” of oppressive humidity and rain more constant than in the movie Blade Runner. Paleocene temperatures exceeded those of modern times by ~10ºC (Figure 4.4).1,2 For perspective, much of the current debate about climate-change mitigation deals with the difference between global warming of 1.5ºC (very bad) and 2.0ºC (very very bad). As mentioned earlier, high atmospheric temperatures enable the atmosphere to adsorb about 7% more water for every degree Celsius. The Paleocene was both warm and wet. Angiosperm plants thrive in such climates, in part because of ample soil moisture, and Box 6.1 considers their evolution. Trees were well established by ~90 Ma, during the dinosaur-dominated Cretaceous. When primates emerged from their Euarchontan ancestors, they joined other mammals and birds in the arbors of angiosperm trees. During the Paleocene, a burst of evolutionary innovation occurred as survivors of the end-Cretaceous extinction exploited unoccupied niches in recovering ecosystems. Box 6.1 Angiosperm evolution Early angiosperm fossils, mainly herbs and small shrubs, date to ~125 Ma.3 Angiosperms underwent an adaptive radiation during the Cretaceous, with a large increase in diversity ~125–72 Ma.4 Trees evolved later than shrubs and began to populate terrestrial habitats in large numbers ~90 Ma.3 During the run-up to the end-Cretaceous extinction (~66 Ma), a diverse array of angiosperm trees came to dominate most of the earth’s forests. After recovery from the mass extinction and for the remainder of the Paleocene, tropical and subtropical rainforests extended to high latitudes, which are now devoid of trees. Fruit size tended in increase over time,3 and by the end of the Paleocene (~56 Ma) fleshy fruits and nutritious seeds attracted birds and mammals of various species, primates among them. Although mass extinction opened many opportunities, the Paleocene was a time of unusual ecosystems.5 For instance, a high concentration of atmospheric oxygen supported an increase in body size as mammals radiated early in that epoch.6, 7 Forests changed, too. At the onset of the Paleocene, the extinction had decreased both the diversity of tree species and the number of trees in surviving species. As botanical diversity recovered, forests then evolved from open- to closed canopies. In Central and South America, forests lost half of their plant species during the extinction, and it took about six million years to regain their previous level of diversity.8 The story was much the same in Asia, Europe, and North America: a world in which dense forests extended to near the North Pole. Paleocene forests—even at high latitudes—came to resemble the angiospermdominated, closed-canopy rainforests of Central and South America today.8 In the immediate aftermath of the endCretaceous extinction, the ensemble of competing species changed rapidly, as did available habitats. Individuals not only needed to compete successfully with other animals and adapt to swift changes in plant and animal populations, but successful lineages also benefited from a high speciation rate.6

The prime of plesiadapiforms Plesiadapiform primates quickly exploited the arboreal niches emptied by the end-Cretaceous extinction. They might have originated in Asia and migrated into North America,9 although some paleontologists favor a North American origin. Regardless, these species became diverse and plentiful throughout much of North America. Plesiadapiforms later dispersed into Europe via land bridges that connected North America, Greenland, and Eurasia (Figure 5.3). These terrestrial corridors created an integrated ecosystem in which plesiadapiforms thrived during the Paleocene.6 Unlike the ecological communities that primates encounter today, the Paleocene lacked large herbivores. The gigantic herbivorous dinosaurs (sauropods) had died out, and large ungulates had yet to evolve. As angiosperms regained diversity and flourished during the Paleocene, they co-evolved with animal species that consumed their fruits and dispersed their seeds. In this way, seedlings could sprout and grow without compromising their parent plant’s access to nutrients and sunlight. Plesiadapiforms, among other species, provided this “service” to angiosperms in their community. Like many modern primates, large plesiadapiforms subsisted mainly on fruits and leaves; small species extracted gums from trees and preyed on insects.10 Box 6.2 Rodents According to one revision of rodent phylogeny and taxonomy, more than 2,000 species of rodents scamper, burrow, or glide through the world.11 Of every eight mammalian species, somewhere between three and four are rodents (estimates differ). Molecular phylogenies have confirmed the three rodent clades long suspected on phyletic grounds: sciuriform rodents (squirrels); murine rodents (rats, mice, hamsters, and so forth); and caviomorph rodents (guinea pigs, porcupines, and capybaras, among others).11 The origins of these clades remain uncertain, but it’s clear that squirrels emerged early in rodent evolution and quickly adapted to an arboreal or semi-arboreal life. Like Euprimates, rodents of modern aspect don’t appear in the fossil record until the Eocene or very late Paleocene,12 although molecular-clock estimates suggest an earlier origin. The ecosystems of the Paleocene also lacked large predators. The end-Cretaceous extinction had eliminated dinosaurs and pterosaurs, and large eutherian carnivores hadn’t emerged yet. The lack of large ungulates and carnivores, alone, made ecosystems of the Paleocene much different from modern ones, but perhaps the most important absence was a group of placental mammals that eventually became its most speciose order: rodents (Box 6.2). Without competition from rodents, most Paleocene plesiadapiforms evolved into generalized, arboreal herbivores. Chapter 5 (“Gnawing away at primate evolution”) highlighted the similarities between rodent and plesiadapiform dentition, including large procumbent incisors and diastemas (gaps) between the incisors and premolars or molars.10 These similarities resulted from homoplasy, not homology. In fact, the same traits have arisen repeatedly in arboreal mammals that didn’t need to compete with rodents (Box 6.3). Box 6.3 Mammalian homoplasies for arboreal life A Mesozoic eutherian that lived as early as ~160 Ma had adaptations for climbing trees and leaping among their branches and trunks.13 In these animals, mobile and flexible ankle and wrist joints permitted movements with multiple degrees of freedom that supported arboreal, scansorial (climbing) locomotion. Likewise, arboreal marsupials have many convergent traits with primates, including grasping specializations and a form of the diagonal sequence, diagonally coupled (DS–DC) gait described in Chapter 5 (“Branch managers”).14 Some marsupials have so many similarities with primates that South Americans call one species monito del monte, which means “little monkey of the mountain.” They have forward-facing eyes (or nearly so) and several other homoplasies with primates, including relatively large brains (Chapter 10, “Mesozoic mammals, modern monotremes, marsupials”). When animals share traits via homoplasy, superficial similarities often conceal important differences. In this case, plesiadapiforms had a seed cruncher in their jaws, which resembled the one that rodents evolved independently. But rodents had a better version. Not only did rodent incisors grow ceaselessly, but they also had a self-sharpening mechanism that allowed them to gnaw through hard materials indefinitely, as rodents do today. Powerful masticatory muscles brought their top and bottom molars together (occlusion) to crush plant matter efficiently, and an enhanced ability of rodents to move the jaw laterally improved food processing further.10, 15 Because of the superior gnawing and food-processing apparatus of arboreal rodents, plesiadapiforms began to die out once rodents invaded North America in force. After their heyday during the Paleocene, plesiadapiforms decreased in diversity and abundance throughout the Eocene, with the most recent specimens dated to ~37 Ma. In essence, their extinction resulted from being deprived of their niches by arboreal rodents, a process called competitive exclusion.10 Before this happened, a plesiadapiform species probably gave rise to the founding Euprimate, but it’s possible that Euprimates descend from some other Euarchontan. Regardless, Eocene primates needed to compete successfully with arboreal rodents, and plesiadapiforms weren’t among the survivors. The battle of the birdfeeder Discussions that emphasize cortical specializations in primates bother some neuroscientists, especially those who study rats and mice. Perhaps they can take some solace from the fact that the ancestors of their favorite research subjects once drove stem primates to extinction simply by being better at doing what those primates had evolved to do. Anyone who has witnessed the battle of the birdfeeder can appreciate what happened. Once squirrels enter the arena, other animals don’t stand a chance. Even the most robust birdfeeder bird can’t compete with the speed, power, and agility of these acrobatic and

cunning rodents. Squirrel-like (sciuromorph) species evolved early in the history of rodents, and some lineages have survived little changed from the late Eocene.11, 16 The struggle for survival between plesiadapiform primates and arboreal rodents probably resembled the battle of the birdfeeder, and—because of their improved gnawing and grinding dentition—rodents prevailed decisively. Summary Plesiadapiforms thrived and radiated in a world dominated by rainforests, which extended from the equator to near the poles. The end-Cretaceous extinction opened many niches, and Paleocene plesiadapiforms exploited arboreal ones quickly, within 200,000 years or so. In the absence of competition from rodents, which had yet to enter these ecosystems, plesiadapiforms resembled rodents in many ways: in part via inheritance from ancestral Euarchontoglires, but mainly through the independent evolution of rodent-like traits, including procumbent incisors, strong masticatory muscles, and a gap behind the incisors called a diastema. These structures enabled plesiadapiforms to obtain nutrients from seeds and fruits while avoiding the ingestion of seed coats and husks, which have little nutritional value. In the warm world of the Paleocene, and in the absence of large predators, life in the trees provided many opportunities and fewer risks than did a terrestrial life. Plesiadapiforms prospered. Then, during the Eocene, arboreal rodents appeared in large numbers. They had a superior gnawing mechanism that won the niches in which both rodents and plesiadapiforms lived. In the end (their end), plesiadapiforms died out from competitive exclusion. Cortical considerations As Chapter 7 (“Eocene expansions”) explains, Paleocene plesiadapiforms had a cortex much smaller than any primate has today: in the size range of modern rodents (and many other small mammals). Cortical expansion occurred much later, in crown primates. Accordingly, the initial adaptations to an arboreal life didn’t trigger cortical enlargement in primates. Primates moved within the arbors of angiosperm trees, foraged in its terminal-branch niche, and consumed insects and the foods produce there—all with a rodent-size cortex. These were not the selective factors that transformed a cortex of ancient aspect into something new. Eocene Euprimates The Eocene began with an abrupt global warming, and it ended with an abrupt global cooling (Figure 4.4). What happened in between established primates as the successful order that we know today. Not only did Euprimates radiate, diversify, and disperse during the Eocene, but the neocortex expanded into the size range of modern prosimians then, probably toward the end of the epoch. Something happened to transform primates from typical Euarchontoglires into large-cortex mammals (Chapter 7), with a suite of new cortical areas (Chapter 12) that performed novel functions (Chapter 15). The paleoecology of this epoch provides clues about the selective factors at work. Forests, long winter nights, and Arctic alligators The Eocene resembled the Paleocene in several ways: (1) Compared to today’s climate, the Eocene was unusually warm, with high humidity and abundant soil moisture; (2) tropical and subtropical rainforests extended much farther north and south than in recent times; (3) land bridges connected North America, Greenland, Europe, and Asia (Figure 5.3); and (4) these connections established an integrated ecosystem across most of the Northern Hemisphere.10, 17 During winter, the combination of relative warmth and dim-light conditions was like nothing seen at northern latitudes today. Relative darkness extended to as much as 22 hours per day, as it does now, but with subtropical conditions, not the bitter cold of Nordic and Upper Canadian winters. Temperatures rarely decreased to freezing, much like South Florida today.* For animals living under a dense forest canopy that blocked 98% of the sunlight,18 the ability to forage in dim light was a necessity any time of year—at any latitude. But this capacity came to the fore during long winter nights. Chapter 12 (“Dining in the dark”) describes primate adaptations for dim-light vision. Forests covered regions that are nearly treeless now. West Texas, for example, is an arid desert, but ~40 Ma it was a subtropical rainforest. (The brain drawing in Figure 1.1, based on a Euprimate species that lived ~40–37 Ma, comes from there.) Montana is the Big Sky Country today, but if you were standing on the forest floor back then you probably couldn’t see the sky. Subtropical conditions extended as far north as Upper Canada, where year-round warmth supported alligators on Ellesmere Island, which straddles ~80° North latitude: more than three-quarters of the way from the Quebec–Vermont border to the North Pole. Despite the far-north locations of these forests, fossilized pollen shows that those ancient plant communities had much less variation in seasonal patterns than exist at temperate latitudes today.10 Hot times in the early Eocene Near the beginning of the Eocene, volcanism accompanied the birth of the Atlantic Ocean, which caused a large increase in atmospheric carbon.15 In addition, some experts think that sea floor instability caused sentiments to release entrapped methane in prodigious quantities.10 For either reason or both, an increase in greenhouse gases caused an abrupt global warming called the Paleocene–Eocene thermal maximum (PETM). Building on the relative warmth of the Paleocene, the global climate warmed rapidly by 5–8°C (Figure 4.4), a temperature spike that developed over 10,000–20,000 years and dissipated over a few hundred thousand years. Land masses near the Arctic averaged 25°C while those near the equator sweltered in 40°C weather—a level that ~55 million years later (the summer of 2022) caused an “unprecedented climate emergency” in Britain and continental Europe. The PETM changed habitats, led to new patterns of competition, and caused extensive extinctions among placental mammals.10 Despite such challenges, species poised to exploit dense rainforests had a significant advantage during and after

the PETM, which provided them with new territories and opportunities. Primates fared well in such conditions, as did arboreal rodents and ungulates (artiodactyls and perissodactyls). These groups appear abruptly in the early Eocene fossil record across the Northern Hemisphere. According to Brusatte15 (p. 215), they “materialize so rapidly—like a swarm of locusts—that it’s difficult to tell exactly how they migrated.” For our purposes, the most relevant fossils include the earliest Euprimate specimens, such as Archicebus achilles and Teilhardina brandti, both dated to ~55 Ma.19, 20 Among the traits that identify them as primates are relatively long digits that ended in nails instead of claws and longer hindlimbs than forelimbs. All fossil Euprimates have these traits, which supported an arboreal life that involved leaping across gaps among tree limbs and grasping limbs upon arrival,21 as described in Chapter 5 (“Branch managers”). The origin of Euprimates remains uncertain in many respects, but it occurred during a time of rapid morphological change in mammals generally.10, 22, 23 Early Euprimates were probably smaller than all but the smallest modern primates,24, 25 although some experts disagree.26 The oldest fossil Euprimate probably weighed ~30 grams, approximately the size of pygmy mouse lemurs.20 Other early Euprimates were even smaller: ~12 grams (Box 2.1).24, 25 Most mammalian lineages that spanned the PETM tended to decrease in size, something that often occurs in warming climates, but primates maintained a relatively consistent body mass.10 This trait is another reflection of primate prosperity, but exploitation of dense forests came at a cost. Like other animals in this ecosystem, primates thrived as rainforests thrived, but when forests suffered, primates suffered, too. Euprimates fan out Global temperatures remained high for most of the Eocene (Figure 4.4). After the PETM (~56–55 Ma), global temperature again increased, reaching a peak ~52 Ma. Then, a long-term cooling trend began ~50 Ma and continued, off and on, until the Holocene (11,700 years ago). A cooler atmosphere is also a drier one, so global cooling ultimately led to the retreat of rainforests toward middle latitudes. However, a warming trend from ~45–40 Ma—the middle Eocene climatic optimum (MECO)—interrupted a longer-term cooling period that otherwise lasted ~16 million years (~50–34 Ma). The MECO enabled tropical and subtropical rainforests to continue dominating the Northern Hemisphere throughout most of the Eocene. It was in these warm conditions that Euprimates diversified and radiated. As a result of their dispersion within the northern forests, paleontologists have discovered fossil Euprimates in Asia, North America, and Europe, all dated to the Eocene.10, 17, 19, 20 By the early Eocene, primates had already evolved into the main taxa of today—including anthropoids, tarsiers, lorisiforms, and lemuriforms—as well as two extinct lineages: adapiforms and omomyiforms (Figure 5.1).17, 21, 27, 28 Adapiforms were either stem Euprimates or early strepsirrhines; omomyiforms were either stem Euprimates or early haplorhines, although there are other possibilities.29 The snouts of omomyiforms became shorter and their nasal passages smaller during the Eocene, which reflects an overall de-emphasis on olfaction: a trait shared by modern haplorhines, including anthropoids (Figure 8.4C). In adapiforms, snouts, nasal morphology, and olfactory bulb size changed less. The same features distinguish modern strepsirrhines from haplorhines. Eocene Euprimates survived climate change, shifting habitats, and competition from arboreal rodents, along with the other challenges of their times, including the reappearance of large and medium-size predators (Box 12.1). Competition with arboreal rodents might have provided a selective force for some of the traits that characterize Euprimates. To outcompete rodents in the terminal-branch niche, or at least some partition within it, a combination of forward-facing eyes and four grasping limbs provided important advantages. As explained in the previous chapter, enhanced stereopsis improved the visual guidance of several kinds of movements: leaping during locomotion, including escape from predators; handholds that tested branches for support; grasping of valuable items; the initial phases of manipulating objects; and bending branches to bring valuable items to the mouth. In addition, the lower forces associated with hindlimb-dominated locomotion decreased noise and limb shaking that could attract predators.30 Taken together, these adaptations enabled early Euprimates to survive in the fine branches of trees, despite competition from rodents and other vertebrates (mostly birds). Chapter 12 addresses the cortical innovations that contributed to the success of Eocene primates. During their Eocene radiation, Euprimates dispersed widely and adapted to diverse habitats. European Euprimates, for example, became as diverse in dietary preferences and modes of locomotion as modern primates. From ~39–37 Ma, three of these species, including both adapiforms and omomyiforms, had dental microwear patterns consistent with insectivory, frugivory, and folivory, in various combinations that depended on their habitats.10 Earlier, I mentioned that primates suffered when rainforests suffered. This happened toward the end of the Eocene, beginning ~40 Ma (Figure 4.4). In North America, for example, the cooling of the atmosphere converted subtropical forests into arid ones, in which primates fared poorly. In the western United States, where omomyiforms and adapiforms had radiated and competed with each other,31 both groups became extinct by the end of the Eocene or shortly thereafter.32 Summary The warm, moist conditions that prevailed through most of the Eocene supported an adaptive radiation of crown primates and their dispersion within vast northern forests that extended toward the Arctic. All the major primate lineages—including lorisiforms, lemuriforms, anthropoids and tarsiers—have left fossils dated to the Eocene, and all of them have forward-facing eyes, as well as grasping hands and feet. These traits enabled primates to survive in an ecosystem that included large carnivores and agile, acrobatic, arboreal rodents. After a warming trend from ~45–40 Ma, Earth’s atmosphere cooled and dried for the last six million years of the Eocene. Global cooling caused habitat reduction as forests dried and retreated, which increased the competition for arboreal resources. Cortical considerations Chapter 7 (“Eocene expansions”) explains that the origin of Euprimates preceded an expansion of their brains and cortex by millions of years. Accordingly, the selective pressures that produced forward-facing eyes, grasping hands and feet, and finger- and toenails had their effects long before whatever led to the first pulses of cortical enlargement in primates. Primates of modern aspect emerged and thrived for a long time with a cortex of ancient aspect, at least in terms of size.

In addition, primate species faced a special set of competitors during the Eocene: each other. Later, Oligocene extinctions eliminated much of the direct competition among primates.10 The various species of Eocene primates had a cortex of similar size, presumably with a similar complement of cortical areas, which created selective pressures favoring neural representations that promoted fitness: like competing with like. Chapter 15 considers cortical representations as units of selection and the role of primate-on-primate competition in cortical evolution. Intermission This is a long chapter, so it has an intermission: a time to reset the stage. At the beginning of the Oligocene, all the major primate lineages had evolved, and they were established in North America, Africa, Arabia, and Eurasia. The earth’s atmosphere had begun to cool, but it remained warm by modern standards. If, by some magic, you could see the primate species alive ~34 Ma—all at once—you would notice something missing: large animals. There were no primates in the several-kilogram range, and not many anywhere near 1 kg. The Oligocene and Miocene transformed body size in primates— and much else besides. Oligocene openings and Miocene monkeys There’s a television series called Finding Your Roots, hosted by the historian Henry Louis Gates. Gates has professional genealogists investigate the ancestry of celebrities, and he records their reactions as he unveils their family tree. In almost every episode, the guest has ancestors who did something great and others who suffered badly. Slavery, sweatshop labor, devastating poverty, and holocaust victimization crop up repeatedly. Our ancestors had it rough over the past few centuries, but that’s nothing new. Some of our ancestors did remarkable things, but that’s nothing new either. Few times were tougher or lives more accomplished than those of our Oligocene ancestors. This epoch is named for the relatively few species that originated then, but it’s also noteworthy for many extinctions. The Oligocene extinctions resulted from a period of global cooling ~34 Ma (Figure 4.4). This event, called the Eocene– Oligocene climatic transition (EOCT), occurred quickly: within a few hundred thousand years. At high latitudes, mean surface temperatures dropped 5°C; on large middle-latitude landmasses, the plunge was yet more dramatic: 9°C.15 At latitudes of 45°–70° North and South, mean temperatures decreased from ∼20°C to ∼15°C.33 Three factors appear to have caused cooling: weathering of rock formations sequestered atmospheric carbon dioxide in carbonate deposits; fluctuations in Earth’s orbit took it farther from the Sun; and a frigid circumpolar circulation developed around the newly-separate Antarctica.15 Climate change stressed forest habitats over much of the world. Antarctica developed glaciers where forests once thrived; and both North America and Eurasia became inhospitable to rainforests, as they remain today. Woodlands, savannas, and grasslands expanded, and among the places this happened was Madagascar: the land of the lemurs. Large lemurs The founding species of lemurs rafted to Madagascar during the Eocene, ~50–40 Ma. They radiated into a wide range of habitats with little competition until rodents arrived ~26–19 Ma. No predators threatened them until carnivores made it to the island about the same time.10 Thus, lemurs were well established and little challenged when the EOCT arrived. In their island isolation, some lemurs reached the size of modern macaques and baboons, with a similar diet. Unlike most lemurs, these large-bodied species lived as terrestrial quadrupeds that exploited open habitats. One group of subfossil lemurs reached ~35 kg and had many other features in common with cercopithecoids.10 (Subfossil species became extinct relatively recently, so their skeletons haven’t yet fossilized.) As Cachel10 put it (p. 167), their “extinction . . . removed a group that, given more time, might have evolved even more anthropoid-like morphology.” Figure 6.1 illustrates a remarkable example of convergent evolution. The subfossil lemur Hadropithecus, which died out only ~1,500 years ago, had a craniofacial anatomy and diet similar to Pliocene hominins,10, 34 although the former were much smaller individuals.35

Figure 6.1 Convergent evolution in distantly related primates. The subfossil Hadropithecus was a strepsirrhine that lived well into the Holocene. Australopithecines were haplorhines that lived ~4–2 million years ago. Hadropithecus drawing reproduced, with permission, from T.M. Ryan et al., A reconstruction of the Vienna skull of Hadropithecus stenognathus, Proceedings of the National Academy of Sciences USA 105, 10699–702, 2008. Australopithecus drawing reproduced, with permission, from: www.sciencephoto.com/media/947182/view/australopithecus-afarensis-skull-illustration.

Anthropoids arrive From a small, haplorhine ancestor that led a mainly solitary life36 (Figure 15.3) and foraged locally in the relative safety of trees, anthropoids developed into large, highly social animals that foraged over long distances and risked predation to do so (Box 16.1). Most likely, a haplorhine acquired anthropoid characteristics sometime during the Paleocene. Early anthropoids were about the size of modern mouse lemurs, ~75 grams,29 and most remained small, arboreal animals throughout the early and middle Eocene. In a sample of middle Eocene anthropoids from two taxa, one group ranged from 17–120 grams and the other from 28–123 grams.37 For comparison, the smallest modern anthropoids, pygmy marmosets, weigh 115–130 grams. During the late Eocene (~37–34 Ma), anthropoids in the 750-gram range appeared, but crown anthropoids didn’t enter the several-kilogram range until the Oligocene (“Anthropoid adaptations”). The evolutionary trend toward forward-facing eyes, which began in stem Euprimates, continued during anthropoid evolution, and it produced a yet-further-forward eye orientation. Another Euprimate synapomorphy that became enhanced in anthropoids protects the eye from compression that would cause visual distortions. In anthropoids, a complete bony septum walls off the orbit laterally, and that shield protects the eye’s shape (and therefore its optics) from muscle contractions. Experts disagree about whether this trait is a haplorhine synapomorphy or evolved independently in tarsiers and anthropoids.38 Stem haplorhines also lost the tapetum lucidum, a reflective iridescent layer of tissue that parallels the retina and is common to many nocturnal animals, including strepsirrhines.

Figure 6.2 Chronogram of Euarchontans. The width (top-to-bottom) of the colored shapes represent an increase in the number of species, color-coded for their continental locations. The transitions from orange to purple show the estimated timing of two independent anthropoid migrations from Africa to South America. Parapithecus grangeri (also known as Simonsius grangeri) is labeled because Chapter 8 discusses this species. Abbreviations: Ma, million years ago; P-P, Pliocene-Pleistocene. Adapted from E.R. Seiffert et al., A parapithecid stem anthropoid of African origin in the Paleogene of South America, Science 368, 194–7, 2020.

The current consensus is that anthropoids evolved in Asia and migrated to Africa via the Arabian Peninsula. Later, an anthropoid species—at a minimum one pregnant female, but more likely a small group of perplexed primates—rafted from Africa to South America. It must have been quite an adventure! Seiffert et al.39 have recently discovered that at least two anthropoid species made the passage (Figure 6.2). These seafaring primates weighed much less than most modern anthropoids, ~400 grams (Figure 6.4A), which might explain a little about how they rafted across an ocean. In addition, the South Atlantic of the Eocene was somewhat narrower than it is today, sea levels were lower, and some of the mid-Atlantic Ocean ridge or other islands might have been above sea level. Even so, the South Atlantic posed a formidable barrier to migration. Africa and South America rifted and began drifting apart ~100 Ma and had reached an ocean gap of ~1,500 km by the time anthropoids made their move to South America.40 This kind of relocation is sometimes called sweepstakes migration, which refers to the low probability of making it to an ecosystem not yet exploited by a taxon, but with a huge payoff if successful. Migration across large bodies of water occurs more often than seems likely at first glance (Box 6.4). Box 6.4 Long-distance migration As unlikely as it might seem, there is evidence that long-distance, overseas migration occurred several times during primate evolution. Not only did lemurs migrate to Madagascar, but there’s some evidence that aye-ayes (Daubentonia) made the journey independently. After reaching South America, anthropoids rafted again to Central America, which was not yet connected by the isthmus of Panama, and they drifted to Caribbean islands at least twice.40 An alternative is that anthropoids reached South America via an Asia–North America route. However, as Figure 5.3 reveals, that also would have required a long rafting voyage. There are conflicting views about when the main migration to South America took place. Figure 6.2 implies that it might have occurred as recently as ~32 Ma, during the early Oligocene. However, other estimates date the split and migration to the middle Eocene, ~43 Ma.41

The big cut Regardless of how anthropoids originated or when they made it to South America, the lineages that survived into the Oligocene faced an abrupt change in climate in both the New- and Old Worlds. The Eocene–Oligocene climatic transition (EOCT) cooled the earth rapidly, and seasonal variation increased markedly. The landmass lost moisture, so rainforests accelerated a retreat that had begun during the late Eocene. Worldwide, the ecological effects of the EOCT decreased mammalian diversity by ~20%. Climate change hit herbivores especially hard; ~40% of such species became extinct.10 This extinction event goes by many names: the Oligocene bottleneck, the big cut (grande coupure), the Mongolian remodeling, and the Patagonian hinge, depending on location.42,43,44,45 In Africa and Arabia, the Oligocene bottleneck wiped out almost two-thirds of mammalian species by ~30 Ma.46

Figure 6.3 Anthropoid extinctions during the Oligocene bottleneck in North Africa. (A) The thick part of each line in the evolutionary tree shows the range of fossil dates for a given lineage. The vertical dashed line marks the Eocene–Oligocene climatic transition (EOCT). The strepsirrhines included fossil lorises and galagos. Two fossil anthropoids are labeled because Chapter 8 discusses these species. (B) The number of anthropoid (blue) and strepsirrhine (green) species as a function of time. Abbreviation: Ma, million years ago. Adapted from E.R. Seiffert, Evolution and extinction of Afro-Arabian primates near the Eocene–Oligocene boundary, Folia Primatology 78, 314–27, 2007.© European Federation for Primatology, Brill

Figure 6.3 illustrates one effect of the EOCT.47 It plots the number of anthropoid and strepsirrhine species found in the Fayum depression, southwest of Cairo, Egypt. The Oligocene bottleneck caused the extinction of most primates there. Some anthropoids squeezed through, and their diversity recovered after 4–5 million years. The strepsirrhines didn’t fare as well. The EOCT wiped them out at Fayum, and they never returned. Because climate change affected ecosystems worldwide, Old World and New World primates faced the “big cut” independently. Most primates disappeared from Europe fairly quickly after the EOCT, although some persisted for a few

million years. In North America, climate change eliminated primates by the end of the Oligocene.10 Figure 6.2 shows that many Asian and Afro-Arabian species also died out during the EOCT or shortly thereafter. The “big cut” resulted in the restriction of primates to the locations wild populations have today: the southern New World, Africa, and Asia. Anthropoid adaptations Where anthropoids survived, changes in climate and habitats provided strong selective pressure for modifications in body size, diet, foraging ranges, modes of locomotion, circadian patterns, and social systems.10 Eocene anthropoids probably had a mixed diet of insects and fruits,29 but as anthropoids became larger animals, insects no longer provided sufficient nutrition and frugivory became more important.

Figure 6.4 The evolution of body size in anthropoids. (A) Platyrrhines. (B) Catarrhines. Red symbols and bars signify species over 1 kg. In Part B, blue bars correspond to species weighing less that 1 kg; in Part A, blue and black symbols do so. The large, unfilled red circle in Part A highlights the last common ancestor of modern platyrrhines. The gray triangles between Parts A and B show the time range for the histograms in Part B. Abbreviations: Ma, million years ago; Plio, Pliocene. (A) Adapted from D. Silvestro et al., Early arrival and climatically linked geographic expansion of New World monkeys from tiny African ancestors, Systematic Biology 68, 78–92, 2018. (B) Adapted from J.G. Fleagle, Primate Adaptation and Evolution, Academic Press, San Diego, 1999.(A) © 2018, Oxford University Press (B) Elsevier Science & Technology Journals

Figure 6.4 illustrates the evolution of body size in platyrrhines and catarrhines.41, 48 Both entered the one-to-several kilogram range, independently, by the late Oligocene or early Miocene; and in both taxa the smaller species died out. There were no small-bodied anthropoids until this trait re-emerged in platyrrhines: mainly tamarins, marmosets, and owl monkeys (Box 2.2).*

It’s tempting to imagine that anthropoids increased in size as a direct effect of global cooling: to decrease surface-tovolume ratios and thus conserve heat. But size increases were not confined to periods of global cooling.31 Instead, the effects of global cooling were indirect. The effects of a cooler, drier atmosphere changed a complex web of selective factors that led to larger body size, among many other anthropoid adaptations.

Figure 6.5 Selective pressures and anthropoid adaptations. Orange shading highlights the influence of climate change; yellow shading marks two key morphological adaptations; and gray shading emphasizes behavioral changes. Entries in blue font are additions to the original flow chart. Adapted from S. Cachel, Fossil Primates, Cambridge University Press, Cambridge, 2015.© Cambridge University Press

Figure 6.5 presents Cachel’s10 synthesis of these intertwined causes and effects. Not only did cooling-induced deforestation change the resources available to anthropoids, it caused seasonal variation in temperature to become much greater than daily fluctuations. This development, in turn, led to dramatically increased seasonality and synchronization of flowering and fruiting by angiosperm plants. Along with seasonal volatility in other resources, these ecological changes required anthropoids to forage over a larger territory in daylight, a factor that favored foveal, trichromatic vision, quadrupedal locomotion, and larger bodies (which, in turn, needed more calories to maintain). I have changed Cachel’s flowchart a little, by adding predation risks and highlighting the behavioral adaptations, two morphological changes, and climate change. Her analysis doesn’t say much about the cortex, although it does indicate an expansion of visual cortex and a contraction of the olfactory bulbs. As Chapters 8 and 13 explain, these changes only scratch the surface of what happened to primate brains during anthropoid evolution. I added predation risks because it’s sometimes been overlooked as a selective factor in anthropoids (Box 16.1). Field studies of modern anthropoids have sometimes described predation on these species as rare, but most experts disagree.49, 50 The evolution of locomotion in anthropoids becomes important for a proposal advanced in Chapter 16. As mentioned earlier, anthropoids adopted quadrupedal locomotion as they began to move large distances via branches. Cachel’s chart shows that they did so initially as arboreal quadrupeds (Figure 6.5, lower right). This adaptation led to changes in skeletal morphology, including modification of the proximal femur.51 In part because independent cortical evolution in platyrrhines, cercopithecoids, and hominoids is a major theme of subsequent chapters, it’s worth examining the evolution of femur morphology in some detail because it reveals something surprisingly similar.

Figure 6.6 Evolutionary trajectories of femur morphology in anthropoids. (A) Chronogram of the anthropoids contributing to Part B. (B) Principal component analysis (PCA) of proximal-femur morphology and evolutionary trajectories (arrows). The squares plot the values of first two principal components for individual species; circles represent an inferred adaptive optimum for each group. A color code links the chronogram in Part A to the PCA in Part B. For example, the red squares in Part B show the data for extinct anthropoids, which correspond to the red lines in Part A. Abbreviations: Anc, the ancestral condition; Eo, Eocene; Ma, million years ago; Plio-Pli, Plio-Pleistocene. Adapted from S. Almécija et al., Early anthropoid femora reveal divergent adaptive trajectories in catarrhine hind-limb evolution, Nature Communications 10, 4778, 2019.Copyright © 2019, Sergio Almécija et al

Figure 6.6B presents a principal component analysis that illustrates how proximal femur morphology diverged from the ancestral anthropoid condition in Miocene platyrrhines, hominoids, and cercopithecoids. Of particular interest is the persistence of the primitive morphology into the Miocene, as indicated by red lines in Figure 6.6A. These traits supported a slow and cautious form of arboreal quadrupedal locomotion, which no modern species has retained. Both a fossil catarrhine, Victoriapithecus, and a fossil platyrrhine, Homunculus, had the primitive anthropoid morphology, and they lived ~16–15 Ma. Then, during the middle-to-late Miocene, femur morphology changed as anthropoids radiated and adopted new, faster forms of locomotion. These changes occurred independently in platyrrhines, cercopithecoids, and hominoids. In hominoids, the transition occurred a little earlier than in monkeys: by ~18 Ma, as indicated by the fossil hominoid Ekembo, and it sped up locomotion a different way than in monkeys. Chapter 8 establishes a similar chronology for cortical enlargement. Summary The EOCT led to a loss of habitats, increased seasonality, extinctions, and body-size increases in the surviving anthropoids. Most Oligocene and Miocene anthropoids foraged in daylight in social groups. They also: (1) adopted dietary preferences which satisfied the greater nutritional requirements that larger animals require; (2) engaged in long-distance foraging, first as arboreal quadrupeds and later in other ways, such as terrestrial quadrupedy or suspensory locomotion; and (3) risked predation while foraging (Box 16.1). The complex social systems that evolved in anthropoids helped individuals avoid predation, although foraging in groups intensified intragroup competition, among other aspects of primate societies. Cortical considerations Chapter 8 summarizes fossil evidence indicating that Oligocene anthropoids had a brain and cortex that was similar in size to what prosimians have today: a condition that persisted into the Miocene. Thus, the selective factors favoring a larger cortex influenced anthropoids millions of years after the development of larger bodies, long-distance diurnal foraging, complex social groups, and most of the other anthropoid traits depicted in Figure 6.5. Something about the Miocene led to both a larger cortex (Chapter 8) and new forms of locomotion (Figure 6.6). Chapters 13 and 16 explore what this “something” was.

Miocene modifications and Plio-Pleistocene primates Planet of the apes, then monkeys When the Miocene began ~23 Ma, the first hominoids had only recently emerged; when it ended ~5.3 Ma, hominins lived in Africa, orangutans thrived in Southeast Asia, and gorillas had knuckle-walked through Africa for millions of years. The Miocene was a time of cool and dry conditions, for the most part, with the continents more-or-less in their modern locations. But a warming trend called the middle Miocene climatic optimum (MMCO, Figure 4.4) led to rainforest expansion ~18–15 Ma. Hominoids, which became several-kilogram animals ~20 Ma, underwent an adaptive radiation, diversifying and dispersing widely. At the height of their Miocene radiation, hominoids had spread throughout Eurasia, including many places devoid of apes today.10 (Zoos don’t count.) The ascendency of apes waned after a global cooling trend began during the middle Miocene (~14 Ma). For a while, the cooling trend paused, then it resumed ~9.6 Ma in a phase of climate change called the Vallesian crisis.52 Figure 6.7C illustrates the drying of the Old World,53 which caused the contraction of dense rainforests and the extinction of many hominoid species that depended on such habitats.10 Neuroscientists often—and other scientists nearly always—equate a large cortex with intelligence and behavioral adaptability, and Miocene hominoids certainly had a large cortex by any measure (Chapter 8, “Catarrhines”). Unfortunately, it didn’t enable them to overcome their dependence on warm, well-watered forests and the relatively constant climates they afforded. Today, all ape species forage and nest in dense, moist forests. Miocene hominoids did so, as well, and when their forests dried out, most of these species died out. By the late Miocene, open woodlands and savannas replaced the retreating forests in Africa, which presented new opportunities for two groups of catarrhines living there: hominins and cercopithecoids. Hominins diverged from panins during the late Miocene (~6.3 Ma),54 although a period of interbreeding and hybridization might have extended into the Pliocene (~5–4 Ma)15; cercopithecoids radiated and diversified at about the same time.55 Cercopithecoids probably migrated out of Africa during the late Miocene,10 and they eventually spread throughout Europe, South and Southeast Asia, China, and Japan. Figure 6.7A presents a chronogram of a clade, the papionini, which includes macaque monkeys, baboons, and geladas.56 It illustrates the time course of diversification in this group, which was typical of cercopithecoids.

Figure 6.7 Climate change and adaptive radiations. (A) A chronogram of one group of cercopithecine monkeys. (B) Chronograms of three catarrhine clades, with global temperature change superimposed in red. The black circle marks the last common ancestor of humans and chimpanzees according to this molecular phylogeny. The green arrow indicates the onset of a glacial period (Ice Age). The blue arrow marks the radiation of plants that are C4 foods. (C) Estimates of humidity and aridity from dental data. (A) Adapted from C. Roos et al., The radiation of macaques out of Africa: evidence from mitogenome divergence times and the fossil record, Journal of Human Evolution 133, 114e132, 2019. (B) Global temperatures from Figure 4.4; Hominini chronogram from Figure 8.7; Papionini chronogram from Part A. (C) Adapted from F. Kaya et al., The rise and fall of the Old World savannah fauna and the origins of the African savannah biome, Nature Ecology and Evolution 2: 241–6, 2018. (A) © 2019 Elsevier Ltd. (C) © Copyright © 2018, Ferhat Kaya et al

The middle-to-late Miocene thus saw a turnover in the composition of catarrhines. During the global cooling of ~14–9 Ma, catarrhine ecosystems changed from hominoid dominance and cercopithecoid rarity to the opposite: a lot of monkeys but few apes, a condition that has persisted ever since then.10 The Plio-Pleistocene was thus something of a relic world for catarrhines, and in that world—otherwise a planet of monkeys—one hominoid lineage that survived to the Holocene was ours. The origin of hominins remains uncertain, but one idea is that a group of European hominoids adapted to shrinking habitats by evolving a more upright posture for rapid movement through contracting rainforests. Then, ~10 Ma, some of these hominoids migrated to Africa and one of their descendant species—a stem hominin—became fully bipedal.57 Patagonian platyrrhines Similar changes were afoot in the New World.41 By the MMCO, platyrrhines had radiated and dispersed as far south as Patagonia, near the southern tip of South America, where hot and humid conditions prevailed at the time. In addition, the main uplift of the Andean mountains had yet to occur, which also made Patagonia more hospitable to subtropical forests. Together, these geologic and climatic factors established conditions suitable for platyrrhines. After the cooling and drying of the atmosphere began ~14 Ma, both the forests and platyrrhines retreated toward the equator as their extinction rates increased.41

Hominin heartlands Ice began to dominate the upper Northern Hemisphere during the late Pliocene, beginning ~2.9 Ma with another global cooling trend (Figures 4.4 and 6.7B). Forests retreated yet again, and more hominoids died out. Hominins had diverged from the chimpanzee–human ancestral population ~3 million years before the steep Plio-Pleistocene cooling. The hominin chronogram in Figure 6.7B, adapted from Püschel et al.,58 places the hominin–panin split a little earlier than other estimates, at more than ~7 Ma. Despite some remaining uncertainty, nearly all estimates agree that by ~6 Ma humans and chimpanzees had gone their separate ways, but a degree of hybridization and gene exchange might have continued for a couple of million years thereafter. The rapid global cooling that began ~2.9 Ma kicked off an Ice Age, and the several Ice Ages that followed posed serious challenges to evolving hominins. However, australopithecines survived from ~4–2 Ma as ground-foraging, bipedal species in an ecological community that had abundant, large, and speedy carnivores. Because these Plio-Pleistocene hominins foraged in savannas and grasslands, rather than in the forests of their ancestors, they were especially vulnerable to predation.59, 60 Nevertheless, australopithecines survived during a time when many other hominoids became extinct, so they must have had some advantages that the others lacked. Cachel61 has proposed that cohesive social systems and sophisticated vocal signaling might have provided those advantages. If so, then australopithecine societies prefigured the human ones that came later, and an australopithecine lineage probably gave rise to the first Homo species ~3.5–3.0 Ma.62, 63 The late Pliocene cooling of ~2.9 Ma initiated the first of three glacial periods that extended into the Pleistocene: ~2.9–2.4 Ma, ~1.8–1.6 Ma, and ~1.2 Ma–800,000 years ago. The climate again underwent periods of instability ~700,000 years ago and 400,000–300,000 years ago.64 Ice Ages continued off and on as the global climate oscillated between warm and cool periods. The last glacial period began ~130,000 years ago and worsened until ~26,000 years ago.15 Then, 11,700 years ago, the onset of the Holocene marked the beginning of a warm period that continues to this day. (We are currently in an interglacial period. As the characters in Game of Thrones kept saying: Winter is coming.) All these climate changes affected human evolution: a story often told in both the popular and academic literature. Accordingly, I won’t labor the well-worn topic of human evolution here. Most likely, neuroscientists sufficiently interested to have read this chapter already know about that. Here’s a short version of the story, with an emphasis on climate change: •

•

• •

Hominins originated during gradual and mild cooling trend from ~8–6 Ma. It was followed by another phase of cooling and drying of the atmosphere. Savannas and grasslands expanded,65 and a group of plants that fix carbon in a novel way, called the C4 photosynthetic pathway, came to dominate many locales.66, 67 Figure 6.7B marks their rise to prominence, although a recent report70 indicates that they flourished ~10 million years earlier than previously thought. Most plants use another photosynthetic pathway, called C3, but C4 improves the efficiency of photosynthesis when the atmospheric concentration of carbon dioxide decreases, as it did in association with global cooling. C4 plants are relatively resistant to temperature and moisture volatility, so they flourished as the atmosphere cooled and the soil dried. These plants invaded open woodlands and savannas after originating in the understory of tropical and subtropical forests.66 Like C4 plants, hominins also originated in forests and moved into a more open, drier habitats. As they did, they turned to C4-rich foods. Apes don’t eat appreciable quantities of C4 plants, even though these foods grow in places where apes live. Enamel adsorbs plant material, so information about diet can be gleaned from fossil teeth. This dental evidence reveals that early hominins had a diet rich in C4 foods, such as sedges and C4 grasses, along with animals that consumed C4 plants. Homo species that lived during the early Pleistocene, ~2.4–2.0 Ma, exploited these open habitats, probably via improvements in tool manufacture and social cooperation, and they benefited from a period of climate stability during the early Pleistocene.64 From ~2 Ma, humans continued adapting to woodlands, grasslands, riversides, and lakesides.15 After ~700,000 years ago, and especially 400,000–300,000 years ago, global temperatures and atmospheric moisture went through another period of volatility. By these times, Homo species had developed highly mobile and sophisticated societies64 with hi-tech tool-making traditions, which endowed them with the cultural capacity to cope with climate change.68, 69 By the beginning of the last Ice Age, ~130,000 years ago, the human brain had reached its modern form, or nearly so. Roughly 125,000 years later, prehistory ended and history began.

Summary A middle-to-late Miocene cooling trend caused the extinction of many ape species, which depended on warm, moist forests that had little seasonal variation. Other anthropoid lineages did better, including hominins, cercopithecoids, and platyrrhines. Hominins and cercopithecoids of the Plio-Pleistocene exploited the savannas and open woodlands left behind by contracting rainforests, with hominins turning to C4 foods. At about the same time, platyrrhines moved northward as New World forests did. Cortical considerations Chapter 8 explains that hominoids had large brains with an expansive cortex before rainforests began their middle-to-lateMiocene retreat. Their large cortex didn’t suffice to save most of them from extinction as their forest habitats vanished, but a few hominoid species survived into the Plio-Pleistocene. Among them, hominins did better than most of the others because, like cercopithecoids, they adapted to climate change by foraging in savannas and open woodlands, despite a markedly increased threat of predation. Both hominins and cercopithecoids mitigated these threats by living in complex social groups, and the cortex enlarged independently in both lineages (Chapter 8). Chapters 16 and 17 consider the selective factors that favored cortical expansion in these two groups of Old World primates.

Chapter summary Global climate has exerted strong effects on the ecology of primates since their origin. Both plesiadapiforms and early Euprimates lived in a hot-house world. Wet and warm conditions were conducive to dense tropical and subtropical forests, which extended much farther north than they do today, and early primates adapted to arboreal habitats within those forests. Two major sets of ecological changes played a crucial role in primate evolution. First, in the early Eocene, arboreal rodents invaded the habitats that primates depended upon, which drove plesiadapiforms to extinction via competitive exclusion. The first Euprimates competed successfully with arboreal rodents, as well as with other mammals and birds that foraged in the terminal branches of angiosperm trees. Second, as the Eocene drew to a close, two periods of global cooling caused deforestation, which presented a severe challenge to primates (Figure 4.4). Especially difficult conditions developed during the abrupt and severe global cooling of the Eocene–Oligocene climatic transition ~34 Ma (the EOCT). Many lineages died out during this evolutionary bottleneck, and the survivors lived in an ecosystem characterized by less competition among primate species than during the Eocene. Once primate species no longer competed with other primates as much, the selective pressures operating on their cortex changed. Before the Oligocene, there was a premium on whatever provided an edge over fellow primates, which sometimes competed for similar niches; afterward, innovations that provided an advantage over other primate species became less important. Cooler conditions during the Oligocene contributed to an increase in anthropoid body size, a trait that interacted with a web of selective forces and adaptations. Figure 6.5 presents a flowchart of the changes in morphology, physiology, and behavior that occurred in anthropoids. Central to these developments was a markedly increased seasonality and volatility of resources during the Oligocene and afterward, especially at temperate latitudes. Anthropoids became larger animals, in the several-kilogram range, independently in the New- and Old Worlds. Among many consequences of this development, they needed to travel farther to obtain necessary nutrients. Because anthropoids descended from a diurnal haplorhine ancestor, most of them foraged in daylight and experienced serious predation risks when they did. The complex social systems that characterize modern anthropoids mitigated those risks, but also caused competition within foraging groups. The evolution of social hierarchies mitigated and managed those conflicts in many groups of primates. Among catarrhines, cercopithecoids and hominoids fared differently during the Miocene. After the EOCT of ~34 Ma, global temperatures stabilized for ~16 million years, culminating in a warming trend from ~18–15 Ma. Hominoids radiated and diversified in these conditions. Then, cooling resumed during the middle-to-late Miocene, ~14–9 Ma. Hominoids died out in large numbers as cercopithecoids radiated and diversified in the Old World, and platyrrhines did likewise in the New World. In the Old World, deforestation caused by global cooling led to shift from ecosystems in which hominoids were common and monkeys rare to the opposite. The two monkey lineages, cercopithecoids and platyrrhines, independently evolved new modes of locomotion, which enabled speedier (cursorial) quadrupedal movements (Figure 6.6). As Chapters 8 and 13 explain, their cortex expanded independently, too. Gradual cooling continued from ~8–6 Ma, when hominins originated, and then accelerated during the late Pliocene, ~2.9 Ma (Figure 6.7B). The loss of moisture in the atmosphere resulted in an expansion of savannas, open woodlands, and grasslands containing C4 foods. Hominins exploited the habitats that produced these foods, as did some cercopithecoids. This chapter’s epigraph quotes Darwin’s inference that a direct ancestor of humans was a hairy, arboreal quadruped that lived in the Old World and had a tail and pointed ears. He was right about most of that (I’m not sure about the pointed ears). An anthropoid fitting that description gave rise to hominoids, which later diversified into humans and several ape species. But Darwin didn’t know something that we know now: The arboreal primate he imagined had a cerebral cortex much smaller than any anthropoid has today. Darwin’s fellow Victorians were probably appalled by the image of a hairy quadruped as one of their direct ancestors, but perhaps they would have preferred that horror to the idea that they descended from animals that were—at least compared to modern anthropoids—pea-brains. The next two chapters examine the evolutionary transitions that made modern primates into the large-cortex animals that they (and we) are today. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

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Early anthropoid femora reveal divergent adaptive trajectories in catarrhine hindlimb evolution. Nature Communication 10, 4778 (2019). 52. Lewis, A.R., Marchant, D.R., Ashworth, A.C., Hedenäs, L., Hemming, S.R., Johnson, J.V., Leng, M.J., Machlus, M.L., Newton, A.E., Raine, J.I., Willenbring, J.K., Williams, M., & Wolfe, A.P. Mid-Miocene cooling and the extinction of tundra in continental Antarctica. Proceedings of the National Academy of Science USA 105, 10676–80 (2008). 53. Kaya, F., Bibi, F., Žliobaitė, I., Eronen, J.T., Hui, T., & Fortelius, M. The rise and fall of the Old World savannah fauna and the origins of the African savannah biome. Nature Ecology and Evolution 2, 241–6 (2018). 54. Patterson, N., Richter, D.J., Gnerre, S., Lander, E.S., & Reich, D. Genetic evidence for complex speciation of humans and chimpanzees. Nature 441, 1103–8 (2006). 55. Elton, S. Environmental correlates of the cercopithecoid radiations. Folia Primatologica 78, 344–63 (2007). 56. 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Primate and Human Evolution (Cambridge University Press, Cambridge, UK, 2006). 62. Villmoare, B. Early Homo and the role of the genus in paleoanthropology. American Journal of Physical Anthropology 165, 72–89 (2018). 63. Villmoare, B., Kimbel, W.H., Seyoum, C., Campisano, C.J., DiMaggio, E.N., Rowan, J., Braun, D.R., Arrowsmith, J.R., & Reed, K.E. Early Homo at 2.8 Ma from LediGeraru, Afar, Ethiopia. Science 347, 1352–5 (2015). 64. Timmermann, A., Yun, K.-S., Raia, P., Ruan, J., Mondanaro, A., Zeller, E., Zollikofer, C., Ponce de León, M., Lemmon, D., Willeit, M., & Ganopolski, A. Climate effects on archaic human habitats and species successions. Nature 604, 495–501 (2022). 65. Bobe, R. & Behrensmeyer, A.K. The expansion of grassland ecosystems in Africa in relation to mammalian evolution and the origin of the genus Homo. Palaeogeography, Palaeoclimatology, Palaeoecology 207, 399–420 (2004). 66. Edwards, E.J. & Smith, S.A. Phylogenetic analyses reveal the shady history of C4 grasses. Proceedings of the National Academy of Sciences USA 107, 2532–7 (2010). 67. Cerling, T., Harris, J.M., MacFadden, B.J., Leakey, M.G., Quade, J., Eisenmann, V., & Ehleringer, J.R. Global vegetation change through the Miocene/Pliocene boundary. Nature 389, 153–8 (1997). 68. Potts, R. Evolution and climate variability. Science 273, 922–3 (1996). 69. Potts, R. Environmental hypotheses of Pliocene human evolution. In: Hominid Environments in the East African Pliocene: An Assessment of the Faunal Evidence (ed. R. Bobe, Z. Alemseged, & A.K. Behrensmeyer) 25–49 (Kluwer, New York, 2007). 70. Peppe, D.J., Cote, S.M., Deino, A.L., Fox, D.L., Kingston, J.D., Kinyanjui, R.N., Lukens, W.E., MacLatchy, L.M., Novello, A., Strömberg, C.A.E., Driese, S.G., Garrett, N.D., Hillis, K.R., Jacobs, B.F., Jenkins, K.E.H., Kityo, R.M., Lehmann, T., Manthi, F.K., Mbua, E.N., Michel, L.A., Miller, E.R., Mugume, A.A.T., Muteti, S.N., Nengo, I.O., Oginga, K.O., Phelps, S.R., Polissar, P., Rossie, J.B., Stevens, N.J., Uno, K.T., & McNulty, K. Oldest evidence of abundant C4 grasses and habitat heterogeneity in eastern Africa. Science 380, 173–7 (2023).

* As a resident of South Florida for 15 years, I experienced exactly one instance of a temperature below 0°C. One night, thermometers “plunged,” as the front-page headline of The Miami Herald put it, reaching –1.5°C briefly. A few years later, it snowed a little one day. * Figure 8.11 summarizes a phylogenetic analysis of body size, which indicates that increases occurred independently in four platyrrhine lineages and four catarrhine lineages.

PART III WHAT PRIMATE CORTEX WAS

7 Great grades of gray Overview Fossil evidence shows that primates evolved forward-facing eyes, grasping digits, a divergent hallux, fingernails, toenails, and long hindlimbs by the early Eocene, no later than ~55 Ma and probably earlier. Endocasts of fossil crania reveal that the brains and cortex of primates remained about the same size as in rodents until much later. During the late Eocene, primate brains increased in volume relative to body mass (a measure called encephalization), and they became mostly neocortex for the first time. As the cortex expanded, primate brains adopted their characteristic shape, with a lateral sulcus and a protruding temporal lobe. The timing of cortical enlargement indicates that it occurred independently in at least four primate lineages: lemuriforms and lorisiforms among strepsirrhines; and tarsiers and anthropoids among haplorhines. It’s also possible that these initial pulses of brain expansion occurred independently in platyrrhines and catarrhines. There is nothing like geology; the pleasure of the first day’s partridge shooting or first day’s hunting cannot be compared to finding a fine group of fossil bones, which tell their story of former times with almost a living tongue. —Charles Darwin, Darwin Correspondence Project, University of Cambridge, letter of April 6, 1834, to his sister Catherine, www.darwinproject.ac.uk/letter/DCPLETT-242

Introduction In this chapter, grades get glorified; tyrannosaurs outsmart cats and dogs; and morons bellow in faculty meetings. But I begin with the epigraph. Darwin enjoyed hunting animals, but he liked hunting fossils more. As he told his sister, fossils speak for the dead, and I suspect that most neuroscientists will be surprised by what they have to say. Fossils tell us nearly nothing about cortical maps or connections, but they reveal the size and shape of brains, as well as the amount of neocortex. These findings augment comparisons among modern species in several ways. For this chapter, it’s the timing of cortical expansion that’s most important. Based on data from modern primates, which all have a large cortex, and their outgroups, which don’t, it’s tempting to conclude that this trait originated in basal primates. However, there’s another possibility. Several primate lineages might have evolved a large cortex independently and more recently. With “almost a living tongue,” the fossils speak, and this chapter translates their message into a language familiar to neuroscientists. Measures and misconceptions Paleoneurology—the study of fossil crania—relies mostly on two measures to compare brain and cortex size: encephalization and corticalization. Encephalization refers to brain size, usually in terms of volume and usually relative to body mass. The encephalization quotient1 (EQ) is a dimensionless ratio defined as follows: For a group of species, preferably a clade, a log–log regression of brain size on body mass generates a predicted brain size for a given body mass, and the EQ of a species equals the ratio of its observed brain size to its predicted size. Figure 7.1 presents three such regressions. Several factors can influence the EQ, including phylogenetic sampling biases and the slope of the regression. The phrase “relative brain size” usually refers to the EQ of a species or a group of species. Relative, in this sense, means relative to an animal’s body mass (often referred to as body size). Sometimes the word “encephalization” refers to an increase in relative brain size, and sometimes it refers to a high EQ value.

Figure 7.1 Brain–body relationships in primates and other mammals. Plot of brain size versus body size in mammals (blue), prosimians (red), and anthropoids (green). The plot omits very large and very small mammals, as well as humans, and there’s no correction for phylogenetic sampling biases. Database of A.M. Boddy et al., Comparative analysis of encephalization in mammals reveals relaxed constraints on anthropoid primate and cetacean brain scaling, Journal of Evolutionary Biology 25, 981–94, 2012.

Corticalization refers to the percentage of the brain occupied by neocortex. Like encephalization, the term “corticalization” can refer either to an increase in or a high level of this measure. Both terms are prone to misinterpretations. Because the cerebral cortex includes allocortex—three-layered cortex at the border of the cerebrum—the term corticalization has a narrower meaning than a literal reading suggests. Corticalization refers only to neocortex. That’s a minor quibble because experts understand that corticalization refers to neocortex and not allocortex. But sometimes it’s important to remember that measures of corticalization usually exclude two sizeable parts of the cerebral cortex: the hippocampus and piriform cortex. Discussions of EQs tend to have more serious problems. Many authorities view it as proxy for intelligence based on little, if any, evidence. For example, in an otherwise erudite book on dinosaur evolution, Brusatte2 (p. 219) used the EQ to draw inferences about animal intelligence. When discussing the brain of Tyrannosaurus rex, he wrote: The largest tyrannosaurs like T. rex had an EQ in the range of 2.0 to 2.4. By comparison, our EQ is about 7.5, dolphins come in around 4.0 to 4.5, chimps at about 2.2 to 2.5, dogs and cats are in the 1.0 to 1.2 range, and mice and rats languish around 0.5. Based on these numbers, we can say that Rex was roughly as smart as a chimp and more intelligent than dogs and cats.

That’s a romantic view of animal intelligence, but an interpretation of the EQ requires more nuance. Many factors contribute to the EQ of a species, and some have nothing to do with intelligence. Prosimians exemplify one problem in interpreting EQ values. It seems reasonable, at first glance, to ask whether prosimians have relatively large brains. As Figure 7.1 illustrates, brain-size values for nearly all prosimians fall above the brain–body regression for nonprimate mammals. Based on this comparison, it seems clear that prosimians have relatively large brains, and in this sense they do. However, a different view emerges from comparisons among primates. The figure also shows that prosimian brains fall below the brain–body regression for anthropoids. Therefore, among primates, prosimians have relatively small brains. The question—Do prosimians have large brains?—has a seductive simplicity that belies a more complex reality. The answer is a little messy. My choice is: “It depends”; but an alternative: “Yes and no,” is also attractive. Because EQ computations rely on a comparison group, each species has several EQ values, not just one. In the quotation from Brusatte,2 he says that the human EQ is ~7.5, but a wide range of values appear in the literature. One reason is that they depend on different comparisons: among hominoids; among primates; among mammals; or, worst of all, among a hodgepodge of species selected for no reason other than data availability. In the older literature, the species used for calculating the EQ did not always reflect phylogenetic relationships, which led to some faulty conclusions. Another misconception comes as a pair: A high EQ is said to reflect “extra neurons” and extra neurons are said to equate with higher intelligence. Jerison,1 who devised the EQ, recognized the close correlation between the size of the brain and the mass of the body, which arises partly because of the embodied nature of the brain: sensory inputs and motor outputs both involve neural mappings between the brain and the body. Any EQ above 1.0, according to Jerison, results from extra neurons, with the implication that extra neurons (and connections among them) lead to higher intelligence. Herculano-Houzel3 made

this conjecture explicit in her neuron-counting studies. An EQ below 1.0 implies that the species in question is moronic, not to put too fine a point on it. None of those assumptions can withstand critical scrutiny. As is evident from its definition, the EQ reflects changes in either brain volume, body mass, or both. In some species, such as Angolan talapoins (Miopithecus talapoin) and a species of galago (Galagoides demidoff), selection for small bodies while conserving brain size has inflated their EQs without increasing anything related to either intelligence or extra neurons. In other species, such as howler monkeys (Alouatta), their EQ has decreased in the recent evolutionary past, but it’s not clear that they are any more moronic than other anthropoids. (I acknowledge, however, that their incessant bellowing strikes me as undignified for a member of our order. Like assertive participants in departmental colloquia, journal clubs, and faculty meetings, the loudest and most vociferous primates typically have less to offer than they seem to believe.) Some species, such as baleen whales (Mysticeti), have such specialized body architectures that a comparison with other mammals is bound to be misleading. Their low EQs probably have more to do with blubber than with brainpower. A recent study of brain–body allometry across all mammalian orders used a much-improved database to distinguish brainsize and body-size contributions to the EQ.4 It showed that both factors drove changes in encephalization, as did the slope of the regression between them. A summary of this analysis, for primates, appears in the next chapter (Figure 8.11). For now, the important point is that the EQ combines many factors, including the number of neurons, as usually assumed, but also other things: white matter volume and the degree of myelination; the proportion of cortex occupied by neuropil, glia, and vascular tissue; the size of brain structures that contribute to intelligence; the size of brain structures that don’t; and body mass. The EQ is useful for tracking changes in brain size during evolution and for describing differences among species. But the fact is that no one knows whether extra neurons have anything to do with intelligence by any definition. Regardless of how many neuroscientists assume or assert that bigger brains or extra neurons correlate positively with intelligence, this proposition remains entirely speculative. So, too, is the impression that a low EQ equates with low intelligence. The reason for rejecting an uncritical linkage of EQ with IQ is that much of the brain—and extensive expanses of neocortex—have functions that have nothing to do with intelligence as usually understood. In primates, for example, large parts of the neocortex function in the visual guidance of movements. They improve the accuracy of reaching and grasping movements, but few neuroscientists would equate high levels of visual acuity or reaching accuracy with intelligence. The EQ has something to do with how a species has adapted to its ecological niche and social system, but inferences beyond that nearly useless generalization are difficult to come by. There have been attempts to address this problem with a battery of cognitive tasks,5 but sufficiently constrained tests of animal intelligence remain a long way off. There’s no IQ test for animals that has a well-established scientific basis, although the popular media never tire of such claims. A lot of the EQ literature strives to support or debunk various behavioral correlates of this measure, based on modern species. The social-brain hypothesis, for example, holds that the EQ increases as social systems grow in complexity. Evidence in support of this conclusion6 and against it7 breathes life into an otherwise dry exercise in statistics. A robust literature has developed on the social-brain hypothesis and competing ideas,7,8,9,10,11,12,13,14,15 and like many “robust literatures” in neuroscience, it’s full of contradictions. Chapters 15 and 16 address the social-brain hypothesis but don’t endorse it as the single, driving force behind the large cortex of primates. In contrast to the usual approach, which depends on data from modern species, those two chapters consider the lives of extinct primates in which the cortex enlarged, and they rely on the concept of evolutionary grades. Then, in Chapter 17, I offer a new version of the social-brain hypothesis, one that applies specifically to humans. Grades and clades In biology, the term grade refers to a level of size or complexity. For example, early vertebrates evolved a new grade of neural organization: a complex brain, a pair of camera-like eyes, a telencephalon, and an olfactory bulb, among many other innovative traits. Simply put, the founding vertebrates underwent an upward grade-shift in brain complexity. Importantly, this statement remains true even though similar evolutionary changes occurred in other lineages. For instance, cephalopods (squid and octopuses) evolved both complex brains and camera-like eyes convergently with vertebrates.16 To stress the importance of grades, I reproduce a lengthy quotation from Stephen Jay Gould.17 He introduced a symposium paper, Grades and Clades Revisited, as follows: I have a simple faith that the best people in any field are never moronic and are hardly ever truly misguided. Therefore, in the face of acrimonious debate, I generally assume that semantic difficulties inspire 90% of any argument and that, when these are sorted out, both sides are doing something right. The greatest difference in method constantly before us in this symposium pits paleontologists against students of comparative neurology and behavior. The paleontologists are engaged in constructing ever more finely tuned phylogenies; the simple ladders of an earlier century have yielded to bushes of the most luxurious and complex branching. In the face of this, it seems as though comparative neurologists remain rooted to Lamarck’s scala naturae—for they persist in studying a fish, a reptile, an insectivore, a tree shrew, a monkey, and a [hu]man and in drawing from such comparisons a set of conclusions about vertebrate evolution. The neuroanatomists speak of their sequence as though it represents phylogeny, which it manifestly does not. Paleontologists then seem to assert that because the series does not reflect true descent, it cannot designate anything of value. I propose (1) that this debate involves words and not things; (2) that it will be laid to rest when neuroanatomists cease implying that their procedure is related to true phylogeny; (3) that this procedure, nonetheless, is both valid and valuable; and (4) that it reflects an evolutionary concept of undoubted respectability, despite its inevitably subjective basis. In short, I assert that the linear sequences of neuroanatomy are successive grades rather than phyletic clades and that analysis by grades is both a neglected and an important mode of evolutionary comparison.

Much has changed in the ~55 years since Gould wrote that introduction. Nowadays, neuroscientists rarely imply that modern monkeys or chimpanzees are “precursors” of humans, although some did so relatively recently.18 Still, the neuroscience literature is full of conclusions about brain evolution drawn from comparisons of macaque monkeys, chimpanzees, and humans—almost as if one is ancestral to another.19,20,21,22,23,24,25 Quite often, the chimpanzees are omitted and grand theories about brain evolution depend on macaques and humans, alone. Some hominids find such publications very annoying. The last common ancestor of modern macaques and humans lived ~26–23 Ma, and fossil evidence indicates that their brains differed significantly from those of modern macaques. These differences have important implications for understanding

cortical evolution, as Chapters 8 and 14 explain and Figure 14.5D illustrates for a key issue: the evolution of prefrontal cortex. So, if comparing modern species, alone, can be misleading, what can neuroscientists do to understand cortical evolution in primates? The rest of this chapter and the next one let “a fine group of fossil bones . . . tell their story” in answer to this question. Eocene expansions Figure 7.2A presents virtual endocasts* for the brains of two fossil plesiadapiforms: Microsyops and Ignacius.26 Figure 5.1 situates these genera on a phylogenetic tree of primates. In both shape and size, plesiadapiform brains resemble those of modern rodents. For example, both have a convex curvature and lissencephalic neocortex (the rhinal sulcus aside), and their occipital cortex doesn’t extend caudally enough to cover the superior colliculus completely. The olfactory bulbs protrude from the frontal pole of the neocortex, with little or no overlap. The inset box in Figure 7.2 shows a rat brain for comparison with the two plesiadapiform brains in Figure 7.2A. The fact that early primate brains looked more like rodent brains than like modern primate brains indicates that most of what distinguishes the shape and size of primate brains evolved after the divergence of Euarchontans and Glires. In support of this conclusion, the inset box in Figure 7.2 also shows a mouse lemur (Microcebus) brain for comparison with those of fossil Euprimates (Figure 7.2B). The Euprimate endocasts come from Rooneyia and Microchoerus, both of which date to the late Eocene: ~37 Ma and ~36 Ma, respectively. Figure 1.1 is a drawing of Rooneyia’s brain, and Figure 5.1 designates it as an omomyiform primate or a stem haplorhine.27, 28 However, its precise classification remains uncertain (incertae sedis in the parlance of paleontology). Microchoerus was an omomyiform related to tarsiers. In terms of overall shape, Rooneyia and Microchoerus brains resembled those of modern primates in having a rostrolaterally bulging temporal lobe that creates the lateral sulcus or fissure (called the Sylvian sulcus or fissure in the older literature). These are the principal features of Euprimate brains that give them a recognizable shape.

Figure 7.2 Virtual cranial endocasts of fossil primates. (A) Brain endocast renderings for two extinct plesiadapiforms, Ignacius and Microsyops, with the neocortex shaded in green. (B) Brain endocast renderings for two extinct Euprimates: Rooneyia and Microchoerus. The inset box illustrates the brains of a laboratory rat and a mouse lemur (Microcebus) for comparison. Note that the scales differ among the different parts of the figure.

(A) Reproduced from A. Long, J.I. Bloch, and M.T. Silcox, Quantification of neocortical ratios in stem primates, American Journal of Physical Anthropology 157, 363–73, 2015. (B) Rooneyia reproduced from E.C. Kirk et al., Cranial anatomy of the Duchesnean primate Rooneyia viejaensis: new insights from high resolution computed tomography, Journal of Human Evolution 74, 82–95, 2014. Microchoerus reproduced from A. Ramdarshan and M. J. Orliac, Endocranial morphology of Microchoerus erinaceus (Euprimates, Tarsiiformes) and early evolution of the Euprimates brain, American Journal of Physical Anthropology 159, 5–16, 2016. The inset comes from R. E. Passingham, Understanding the Prefrontal Cortex: Selective Advantage, Connectivity, and Neural Operations, Oxford University Press, Oxford, 2021.(a) © 2015 Wiley Periodicals, Inc. (b) © 2014 Elsevier Ltd. (B) bottom - © 2015 Wiley Periodicals, Inc. Inset - Oxford University Press

The subject of size Figure 7.3A displays the range of relationships between brain volume and body mass for modern strepsirrhines, as well as two extinct plesiadapiform primates and four extinct Euprimates. The key data point comes from Rooneyia. Of the specimens plotted, only Rooneyia falls within the modern strepsirrhine range (green shading).29 This finding implies that Rooneyia had a larger brain, relative to body mass, than the other extinct primates included in the figure. For example, Ignacius and Rooneyia had a similar body mass, but Rooneyia had a much larger brain. Thus, Rooneyia, a late Eocene Euprimate, had attained a new grade of encephalization, comparable to that of modern prosimians.

Figure 7.3 Eocene grade-shifts in encephalization. (A) Brain volume–body mass relationships in six extinct species (black and colored letters) and in modern prosimians. The green shading bounds the values of modern prosimians, including tarsiers. Note that ‘T’ stands for Tetonius, not tarsier. The horizontal variance bar associated with the red ‘R’ arises from differing estimates of Rooneyia’s body mass. (B) The encephalization quotient (EQ) range for each taxon is bounded by green bars. Red and green circles show values for individual specimens. The two box-and-whisker plots at the right show the interquartile range (pink box), mean (thick green line), range (green bars), and outliers (green circles) for modern primates. Dashed horizontal lines mark the lower limit of EQs for modern haplorhines (top line) and modern strepsirrhines (bottom line) for reference. The haplorhine data are dominated by anthropoids. (A) Adapted from E.C. Kirk et al., Cranial anatomy of the Duchesnean primate Rooneyia viejaensis: new insights from high resolution computed tomography, Journal of Human Evolution 74, 82–95, 2014; drawing modified from S.P. Wise, The evolution of the prefrontal cortex in early primates and anthropoids, in The Evolution of Nervous Systems, (ed. L.A. Krubitzer and J.H. Kaas), 3, 387–422, Elsevier, New York, 2017. (B) Adapted from A.R. Harrington et al., First virtual endocasts of adapiform primates, Journal of Human Evolution 99, 52–78, 2016.(b) © 2014 Elsevier Ltd.

Plots like Figure 7.3A provide a good idea about EQ values, but Figure 7.3B presents them explicitly: for two early Eocene Euprimates (the adapiforms Smilodectes and Notharctus) and three late Eocene Euprimates (the adapiform Adapis, the omomyiform Microchoerus, and Rooneyia).30 Plesiadapiform primates fell below the modern prosimian range, as did the Euprimates Notharctus, Smilodectes, and almost all specimens of Adapis. Most values for Rooneyia’s EQ, which vary depending on the cranial or dental correlate used to estimate body mass, fall within the modern strepsirrhine range. Values for Microchoerus vary from within to outside that range. The dates of these fossils indicate that Euprimates underwent an upward grade-shift in encephalization during the Eocene, probably the late Eocene. Taking Figure 7.3A and B together, both omomyiforms (Tetonius) and adapiforms (Smilodectes and Notharctus) had a Euarchontoglires-grade EQ during the early Eocene (~53–48 Ma), but the omomyiform Microchoerus and Rooneyia had a prosimian-grade EQ during the late Eocene (~37–36 Ma). Similar conclusions apply to corticalization.26 Figure 7.4A presents corticalization values for plesiadapiforms and fossil Euprimates, each plotted against their geological age. Corticalization values of ~20–30% characterize many mammalian taxa, including most rodents, although some squirrels slightly exceed 40%.31, 32 Accordingly, the figure designates values of 20– 42% as the Euarchontoglires grade of corticalization. Plesiadapiforms had levels of corticalization toward the lower end of the Euarchontoglires range, usually less than 25% (see also Figure 10.5B). Fossil Euprimates present a more complex picture. In the early Eocene, the adapiform Euprimates Smilodectes and Notharctus, both dated to ~49 Ma, had corticalization

values within the Euarchontoglires range. Later in the Eocene, fossils of the omomyiform Euprimate Necrolemur, dated to ~38–34 Ma, had brains with more than 50% neocortex, as did an Adapis specimen. Most modern strepsirrhines have this level of corticalization. Although gaps in knowledge remain, it seems most likely that sometime during the late Eocene, primate brains became mostly neocortex for the first time.

Figure 7.4 Eocene grade-shifts in corticalization. (A) Percentage of the brain consisting of neocortex in extinct and extant mammals. A selection of values for modern mammals appears to the right (at 0 Ma), including the values for squirrels (range bars) and an outlier squirrel species (black circle above the top bar). Colored background shading indicates five grades of corticalization in primates; gray shading highlights the Euprimate species; and an oval surrounds the plesiadapiforms. The vertical line marks the onset of the late Eocene climatic transition. (B) Some values from cetaceans and artiodactyls (even-toed ungulates) for comparison with Part A. Cetaceans include toothed whales (Odontoceti), killer whales (Orcinus orca), and dolphins (delphinoids). Figure 10.2C presents a chronogram of cetaceans. Abbreviations: Ma, million years ago; Paleo, Paleocene; Oligo, Oligocene. (A) Adapted from A. Long, J.I. Bloch, and M.T. Silcox, Quantification of neocortical ratios in stem primates, American Journal of Physical Anthropology 157, 363–73, 2015. Drawing adapted from S.P. Wise, The evolution of the prefrontal cortex in early primates and anthropoids, in The Evolution of Nervous Systems (ed. L.A. Krubitzer and J.H. Kaas), 3, 387–422, Elsevier, New York, 2017.

Euprimates were not the only mammals to undergo an expansion of the brain and neocortex. Similar grade-shifts also occurred in other orders.33 Figure 7.4B displays corticalization values for selected ungulates (artiodactyls) and cetaceans, and Chapter 10 explores these comparisons in more detail. Two conclusions follow from the findings summarized so far: The Euprimate entry into the prosimian grade of encephalization occurred long after the diversification of primates into their major subordinal clades (lorisiforms, lemuriforms, tarsiforms, and anthropoids); and it occurred long after the evolution of forward-facing eyes, grasping hands and feet, and flat nails instead of pointy claws.34 These conclusions come with an important caveat. Here, as in Chapter 8, I treat the available fossil endocasts as representative of the major primate lineages. Accordingly, I assume that a particular lineage had a certain trait (e.g., a particular level of corticalization) at a given time because a fossil specimen related to that lineage, dated to that time, had this trait. That’s not a foolproof assumption because the fossils might not be part of the ancestral line that gave rise to modern primates. For example, the Euarchontoglires-size cortex of Smilodectes and Notharctus might reflect a secondary decrease in cortex size in offshoots of the modern Euprimate lineages. However, the consistent contrast among well-dated specimens— such as the smaller cortex of earlier Euprimates and the larger cortex of later ones—mitigates this problem, and there’s no reason to assume that the available specimens are unrepresentative of the main primate lineages. A legacy of longevity A recent study of modern mammals examined the relationship between relative brain size and longevity in several mammalian orders, including primates, bats, ungulates, carnivores, cetaceans, and rodents.11 Only the two Euarchontoglires studied, rodents and primates, had a significant correlation between these two variables (Figure 7.5A). A related analysis revealed that the duration of maternal investment accounted for relative brain size, rather than lifespan per se,13 so the correlation plotted in the figure might result from the covariation of two or more factors. Nevertheless, it remains instructive. For one thing, an aspect of the correlation differs in rodents and primates. In primates, selection for large brains appears to have led the way to longer lives, as illustrated by the flowchart in Figure 7.5B; in rodents, selection for longer lives seems to have preceded a modest increase in relative brain size.

Figure 7.5 Longevity and encephalization. (A) The correlation of longevity and relative brain size in rodents and primates. Ellipses show the bivariate normal distribution for both orders. Relative brain size is plotted as the residual of a log–log regression between brain volume and body mass for all mammals in a sample that included primates, bats, ungulates, carnivores, cetaceans, and rodents. (B) Flow chart of life-history factors related to longer lives in primates. Adapted from A.R. DeCasien et al., Encephalization and longevity evolved in a correlated fashion in Euarchontoglires but not in other mammals, Evolution 72, 2617– 31, 2018.

Chapter summary Euprimate brains underwent an upward grade-shift in both encephalization and corticalization, probably during the late Eocene. For the first time, primate brains became mostly neocortex, as primate brains are today. Primates with less neocortex died out during a time when primate species competed with other primate species regularly, something that happened less often after the Eocene. The characteristic shape of primate brains35—including the lateral sulcus and a prominent, protruding temporal lobe—probably emerged at about the same time as corticalization entered the prosimian grade.

Because the upward grade-shifts occurred before ~37–36 Ma and after ~49 Ma, they must have evolved independently in at least four of the five major primate lineages: lorisiforms, lemuriforms, tarsiforms, and anthropoids. Figure 7.6 illustrates the basis of this conclusion. The green font presents divergence estimates for the major Euprimate clades, from Steiper and Seiffert36 (Chapter 4, “Tick-tock molecular clock”). They indicate that all four of these subordinal lineages had emerged prior to ~50 Ma, before the upward grade-shifts of the later Eocene. It’s also possible that an upward grade-shift occurred independently in platyrrhines and catarrhines. Figure 7.6 adopts this possibility, but it might have occurred in or near their last common ancestor, instead.

Figure 7.6 Summary of Eocene grade-shifts. Based on fossil endocasts of crania from Paleocene, Eocene, and early Oligocene primates. Line color corresponds to a grade of encephalization and corticalization, according to the key in the upper left. The timeline is not to scale. Divergence times, in green font, come from the full range of corrected molecular-clock estimates in Figure 4.2. Abbreviation: Ma, million years ago.

Something about the late Eocene seems to have been conducive to evolving a larger cortex. Chapter 9 returns to this topic, with emphasis on a period of global cooling that began ~40 Ma and persisted throughout the late Eocene. First, however, Chapter 8 takes up more recent grade-shifts. As Darwin noted: “fossil bones . . . tell their story of former times.” As the Eocene ended, the story had only begun. References 1. 2. 3. 4.

Jerison, H. Evolution of the Brain and Intelligence (Academic Press, New York, 1973). Brusatte, S. The Rise and Fall of Dinosaurs: A New History of a Lost World (William Morrow, New York, 2018). Herculano-Houzel, S. The Human Advantage: How Our Brain Became Remarkable (MIT Press, Cambridge, MA, 2016). Smaers, J.B., Rothman, R.S., Hudson, D.R., Balanoff, A.M., Beatty, B., Dechmann, D.K.N., de Vries, D., Dunn, J.C., Fleagle, J.G., Gilbert, C.C., Goswami, A., Iwaniuk, A.N., Jungers, W.L., Kerney, M., Ksepka, D.T., Manger, P.R., Mongle, C.S., Rohlf, F.J., Smith, N.A., Soligo, C., Weisbecker, V., & Safi, K. The evolution of mammalian brain size. Science Advances 7, eabe2101 (2021). 5. Deaner, R.O., Isler, K., Burkart, J., & van Schaik, C. Overall brain size, and not encephalization quotient, best predicts cognitive ability across nonhuman primates. Brain, Behavior and Evolution 70, 115–24 (2007). 6. Dunbar, R.I.M. The social brain hypothesis and its implications for social evolution. Annals of Human Biology 36, 562–72 (2009). 7. DeCasien, A.R., Williams, S.A., & Higham, J.P. Primate brain size is predicted by diet but not sociality. Nature Ecology and Evolution 1, 0112 (2017). 8. Dunbar, R.I.M. & Shultz, S. Understanding primate brain evolution. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 362, 649–58 (2007). 9. Street, S.E., Navarrete, A.F., Reader, S.M., & Laland, K.N. Coevolution of cultural intelligence, extended life history, sociality, and brain size in primates. Proceedings of the National Academy of Science USA 114, 7908–14 (2017). 10. Schillaci, M.A. Sexual selection and the evolution of brain size in primates. Public Library of Science One 1, e62 (2006). 11. DeCasien, A.R., Thompson, N.A., Williams, S.A., & Shattuck, M.R. Encephalization and longevity evolved in a correlated fashion in Euarchontoglires but not in other mammals. Evolution 72, 2617–31 (2018).

12. Heldstab, S.A., Kosonen, Z.K., Koski, S.E., Burkart, J.M., van Schaik, C.P., & Isler, K. Manipulation complexity in primates coevolved with brain size and terrestriality. Science Reports 6, 24528 (2016). 13. Barton, R.A. & Capellini, I. Maternal investment, life histories, and the costs of brain growth in mammals. Proceedings of the National Academy of Science USA 108, 6169–74 (2011). 14. Powell, L.E., Isler, K., & Barton, R.A. Re-evaluating the link between brain size and behavioural ecology in primates. Proceedings in Biological Science 284, 20171765 (2017). 15. Dunbar, R.I.M. & Shultz, S. Why are there so many explanations for primate brain evolution? Philosophical Transactions of the Royal Society B: Biological Sciences 372, 20160244 (2017). 16. Striedter, G. & Northcutt, R.G. Brains Through Time: A Natural History of Vertebrates (Oxford University Press, New York, 2020). 17. Gould, S.J. Grades and clades revisited. In: Evolution, Brain, and Behavior: Persistent Problems (ed. R.B. Masterton, W. Hodos, & H.J. Jerison) 115–22 (Psychology Press, London, 1976). 18. Poremba, A., Malloy, M., Saunders, R.C., Carson, R.E., Herscovitch, P., & Mishkin, M. Species-specific calls evoke asymmetric activity in the monkey’s temporal poles. Nature 427, 448–51 (2004). 19. Pandya, D., Seltzer, B., Petrides, M., & Cipolloni, P.B. Cerebral Cortex: Architecture, Connections, and the Dual Origin Concept (Oxford University Press, Oxford, 2015). 20. Hill, J., Inder, T., Neil, J., Dierker, D., Harwell, J., & Van Essen, D. Similar patterns of cortical expansion during human development and evolution. Proceedings of the National Academy of Science USA 107, 13135–40 (2010). 21. Buckner, R.L. & Krienen, F.M. The evolution of distributed association networks in the human brain. Trends in Cognitive Science 17, 648–65 (2013). 22. Avants, B.B., Schoenemann, P.T., & Gee, J.C. Lagrangian frame diffeomorphic image registration: morphometric comparison of human and chimpanzee cortex. Medical Image Analysis 10, 397–412 (2006). 23. Mars, R.B., Sotiropoulos, S.N., Passingham, R.E., Sallet, J., Verhagen, L., Khrapitchev, A.A., Sibson, N., & Jbabdi, S. Whole brain comparative anatomy using connectivity blueprints. eLife pii: e35237 (2018). 24. Reardon, P.K., Seidlitz, J., Vandekar, S., Liu, S., Patel, R., Park, M.T.M., Alexander-Bloch, A., Clasen, L.S., Blumenthal, J.D., Lalonde, F.M., Giedd, J.N., Gur, R.C., Gur, R.E., Lerch, J.P., Chakravarty, M.M., Satterthwaite, T.D., Shinohara, R.T., & Raznahan, A. Normative brain size variation and brain shape diversity in humans. Science 360, 1222–7 (2018). 25. Petrides, M. & Pandya, D.N. Comparative cytoarchitectonic analysis of the human and the macaque ventrolateral prefrontal cortex and corticocortical connection patterns in the monkey. European Journal of Neuroscience 16, 291–310 (2002). 26. Long, A., Bloch, J.I., & Silcox, M.T. Quantification of neocortical ratios in stem primates. American Journal of Physical Anthropology 157, 363–73 (2015). 27. Ni, X., Li, Q., Li, K., & Beard, K.C. The oldest known primate skeleton and early haplorhine evolution. Nature 498, 60–4 (2013). 28. Ramdarshan, A. & Orliac, M.J. Endocranial morphology of Microchoerus erinaceus (Euprimates, Tarsiiformes) and early evolution of the Euprimates brain. American Journal of Physical Anthropology 159, 5–16 (2016). 29. Kirk, E.C., Daghighi, P., Macrini, T.E., Bhullar, B.A., & Rowe, T.B. Cranial anatomy of the Duchesnean primate Rooneyia viejaensis: new insights from high resolution computed tomography. Journal of Human Evolution 74, 82–95 (2014). 30. Harrington, A.R., Silcox, M.T., Yapuncich, G.S., Boyer, D.M., & Bloch, J.I. First virtual endocasts of adapiform primates. Journal of Human Evolution 99, 52–78 (2016). 31. Bertrand, O.C., Amador-Mughal, F., & Silcox, M.T. Virtual endocasts of Eocene Paramys (Paramyinae): oldest endocranial record for Rodentia and early brain evolution in Euarchontoglires. Proceedings in Biological Science 283, 20152316 (2016). 32. Bertrand, O.C., Amador-Mughal, F., & Silcox, M.T. Virtual endocast of the early Oligocene Cedromus wilsoni (Cedromurinae) and brain evolution in squirrels. Journal of Anatomy 230, 128–51 (2017). 33. Bertrand, O.C., Shelley, S.L., Williamson, T.E., Wible, J.R., Chester, S.G.B., Flynn, J.J., Holbrook, L.T., Lyson, T.R., Meng, J., Miller, I.M., Püschel, H.P., Smith, T., Spaulding, M., Tseng, Z.J., & Brusatte, S.L. Brawn before brains in placental mammals after the end-Cretaceous extinction. Science 376, 80–5 (2022). 34. Gurche, J.A. Early primate brain evolution. In: Primate Brain Evolution: Methods and Concepts (ed. E. Armstrong & D. Falk) 227–46 (Plenum, New York, 1982). 35. Sansalone, G., Allen, K., Ledogar, J.A., Ledogar, S., Mitchell, D.R., Profico, A., Castiglione, S., Melchionna, M., Serio, C., Mondanaro, A., Raia, P., & Wroe, S. Variation in the strength of allometry drives rates of evolution in primate brain shape. Proceedings in Biological Science 287, 20200807 (2020). 36. Steiper, M.E. & Seiffert, E.R. Evidence for a convergent slowdown in primate molecular rates and its implications for the timing of early primate evolution. Proceedings of the National Academy of Science USA 109, 6006–11 (2012).

* A virtual endocast contrasts with a physical endocast. In the latter, a fluid fills a skull cavity and later solidifies. Once removed from the skull, it reveals the shape and volume of the endocranial cavity. A virtual endocast depends on high-resolution computational imaging.

8 Greater grades of gray Overview Building on the Eocene grade-shifts into the modern prosimian size range, additional cortical expansion occurred later and independently in three anthropoid lineages: hominoids, platyrrhines, and cercopithecoids. In platyrrhines and cercopithecoids, the cortex, as a whole, and the frontal lobe, in particular, increased into the modern anthropoid size range during the middle-to-late Miocene, after ~16–15 Ma and after most of their other characteristic traits had evolved. Both groups of monkeys radiated and diversified during the late Miocene. The cortex expanded earlier in hominoids than in monkeys, sometime before ~18 Ma and before hominoids evolved most of their characteristic traits, while they still resembled cercopithecoids in most other respects. Hominins emerged ~7–6 Ma, but the major upward grade-shifts in cortex size occurred much later: after 3 Ma, with considerable enlargement occurring after 2 Ma. Expansion of the frontal, posterior parietal, and anterior temporal cortex, including widening of the frontal lobes, continued until ~130,000 years ago, when the human cortex reached its current size and shape. Surely the men were stupid and ridiculous and cowardly. Even Manu, the monkey, was more intelligent than they. If these were creatures of his own kind he was doubtful if his past pride in blood was warranted. —Edgar Rice Burroughs, Tarzan of the Apes, 1914

Introduction In this chapter, a Miocene monkey makes a mark; brains shape up as bulbs slim down; and foreheads inflate. But I begin with the epigraph. Tarzan appreciated two undoubted principles of primate cognition: human intelligence sometimes leaves a lot to be desired; and other anthropoids are also smart. Chapter 7 explained that several upward grade-shifts in brain and cortex size occurred in Euprimates during the Eocene, probably the late Eocene. This initial phase of cortical expansion drove primate brains into the modern prosimian size range. Subsequent grade-shifts made anthropoids into the brainy species that they (and we) are today. Three pulses of corticalization occurred independently in Miocene hominoids, cercopithecoids, and platyrrhines; another occurred later, in Plio-Pleistocene hominins. The next section, “Miocene monkeys and apes,” addresses the first set of grade-shifts; “Plio-Pleistocene hominins” deals with the more recent ones. Miocene monkeys and apes Gray-matter growth The Oligocene began with an abrupt global cooling that altered the ecosystems of both catarrhines and platyrrhines. At the time, both groups faced severe challenges, which intensified during the Miocene. As Simons et al.1 put it (p. 8735), the fossil record shows that: crown catarrhines likely faced new demands . . . after the influx of relatively large-brained . . . competitors and predators into Afro-Arabia during the early Miocene, whereas platyrrhines likely experienced similarly strong selection pressures after their arrival on a landmass (South America) with new competitors, predators, and phenological patterns.

It was in the challenging ecological contexts of the Miocene that additional pulses of cortical expansion occurred. Figure 7.4A presents the relevant data for corticalization. The neocortex exceeds 65% of the brain’s volume in modern anthropoids and reaches ~80% in humans. This increase in corticalization was accompanied by an increase in EQs, which is unsurprising for brains like those of anthropoids that are mostly neocortex. Although comparative neuroanatomists reached this conclusion long ago,2,3,4,5 Boddy et al.6 assembled an improved database to refine the analysis. All the data in Figure 8.1 come from that source (as do the data in Figure 7.1). A more recent study led to similar conclusions.7

Figure 8.1 Encephalization quotients in mammals. (A) The diameter of each circle is proportional to the mean encephalization quotient (EQ) for each lineage of Euarchontoglires (colugos omitted). (B) Mean (thick green lines), interquartile range (boxes), and range (green bars) of EQ values for each lineage. For reference, gray shading shows the interquartile range for rodents. (C) Circular evolutionary tree showing EQ values for mammals. Each segment comprises a stack of curves for a given mammalian order, with EQ values sorted for individual species from low (outer segments) to high (inner segments). The angle subtended by each segment is proportional to the number of species in each order. Blue indicates low EQs; red and yellow indicate high EQs, relative to all mammals; and the thickness of the red and yellow zones indicates how many species in each taxon have relatively high EQs. (B, C) Adapted from A.M. Boddy et al., Comparative analysis of encephalization in mammals reveals relaxed constraints on anthropoid primate and cetacean brain scaling, Journal of Evolutionary Biology 25, 981–94, 2012.© 2012 A. M. BODDY, M. R. McGOWEN, C. C. SHERWOOD, et al. Journal of Evolutionary Biology. European Society For Evolutionary Biology

As expected from their phylogenetic relationships, tree shrews have EQs mostly between Glires and strepsirrhines; strepsirrhines have EQs mostly between tree shrews and haplorhines; and tarsiers have EQs between anthropoids and strepsirrhines. Figure 8.1C presents a comprehensive analysis of EQs across mammalian orders, and it shows that primates have the most species with high values. Among primates, the bulk of the high-EQ species are anthropoids, a fact that Figure 7.1 depicts in a more traditional way. Figure 8.1 comes from modern species, so it provides little information about when the brain and the cortex enlarged. Because all modern anthropoids have high levels of encephalization and corticalization, it might seem likely that their last common ancestor also had these traits. However, the fossil evidence tells a different story. An upward grade-shift in the size of the cortex—into the modern anthropoid range—occurred independently in at least three anthropoid lineages: platyrrhines, cercopithecoids, and hominoids.

To explain this conclusion, it helps to begin with Figure 8.2A. It shows the values and ranges of brain–body relationships in modern strepsirrhines and anthropoids (including hominoids). For a primate of a given body size, an anthropoid will almost always have a larger brain than a strepsirrhine. There’s some overlap, which has two main causes: (1) Relative brain size has decreased in some anthropoids, such as howler monkeys (Alouatta)8; and (2) some strepsirrhine brains have expanded in parallel with those in anthropoids.6, 9

Figure 8.2 Upward grade-shifts during anthropoid evolution. (A) Brain size–body size relationships for modern primates. Like Figure 7.3A, green shading bounds data from modern strepsirrhines. In addition, pink shading bounds data from modern anthropoids, which include hominoids (blue-shaded region). (B) Brain size–body size relationship for extinct anthropoids (colored letters). The shading is the same as in Part A. Red letters indicate fossil anthropoids, excluding hominoids; blue letters mark two specimens of a fossil hominoid. For the fossil catarrhines, Aegyptopithecus and Parapithecus, error bars depict variation in estimated body size. (C) A principal component analysis of brain shape in primates. For cercopithecoids, only the data points that bound the data appear. To the right, the most-deformed areas appear on the brain renderings in red; the least-deformed areas in green. (A, B) Adapted from E.C. Bush, E.L. Simons, and J.M. Allman, The scaling of frontal cortex in primates and carnivores, Proceedings of the National Academy of Sciences USA 101, 3962–6, 2004); error bars from E.L. Simons, E.R. Seiffert, T.M. Ryan, and Y. Attia, A remarkable female cranium of the early Oligocene anthropoid Aegyptopithecus zeuxis (Catarrhini, Propliopithecidae), Proceedings of the National Academy of Sciences USA 104, 8731–6, 2007. (C) Adapted from G. Sansalone et al., Variation in the strength of allometry drives rates of evolution in primate brain shape, Proceedings of the Royal Society of London, Biological Sciences 287, 20200807, 2020.(c) Royal Society (Great Britain)

Figure 8.2B illustrates the brain–body relationship of four fossil anthropoids from the Oligocene and Miocene: Homunculus, Chilecebus, Aegyptopithecus, and Victoriapithecus. Another extinct anthropoid, Parapithecus, might have lived a little before the Oligocene, but not much. They all had brains smaller than those of modern anthropoids with a similar body size. Thus, Oligocene anthropoids had prosimian-size brains or smaller, and so did many Miocene anthropoids. The alternative interpretation of these data is that the available fossil specimens represent side-branches of the main primate lineages, in which the brain and cortex contracted. However, because all the Oligocene and many Miocene specimens have low EQs, that’s not likely. Note that Figure 8.2B includes both fossil platyrrhines (Homunculus and Chilecebus) and fossil catarrhines (Parapithecus, Aegyptopithecus, and Victoriapithecus). That’s important because these two lineages split during the late Eocene, ~40–34

Ma. The small-cortex anthropoids plotted in Figure 8.2B lived after the Eocene, during the Oligocene or Miocene, so cortical expansion must have occurred independently in catarrhines and platyrrhines. Accordingly, the next two sections address them separately. Catarrhines Figure 8.2B plots data for three specimens of Aegyptopithecus, a catarrhine that dates to the early Oligocene (~30 Ma), prior to the cercopithecoid–hominoid split. The size of its brain clearly falls below the range of modern anthropoid brains and barely enters the modern prosimian range, if it does so at all.10 Likewise, Parapithecus, which lived ~34.5 Ma, fell below the anthropoid range of brain size–body size relationships.11 It has an uncertain affinity with other catarrhines,12 but it was probably a stem catarrhine of the late Eocene that persisted into the Oligocene. (Bush et al.11 referred to this specimen as Parapithecus grangeri, but Beard et al.12 later reclassified it as a new genus, Simonsius grangeri, based on morphological differences from the Parapithecus type specimen: Parapithecus fraasi. Here I retain the name used by Bush et al. to maintain consistency with that source.) A more recent catarrhine, Victoriapithecus, lived during the middle Miocene, ~15 Ma.13, 14 It was a cercopithecoid that lived ~8–11 million years after the cercopithecoid–hominoid split. Given its date and prosimian-size brain, it seems likely that an upward grade-shift in brain size occurred in cercopithecoids during the middle-to-late Miocene. (The middle Miocene was ~16–12 Ma.) Another way to look at the same data recognizes that Victoriapithecus brains were near the lower limit of the modern anthropoid range, which suggests that brain expansion was underway ~15 Ma but had yet to reach a modern level. This chapter began by saying that “a Miocene monkey makes a mark,” and these are the monkeys in question. Given the importance of Victoriapithecus, it’s worth considering what’s known about them. Benefit and McCrossin14 discovered their fossils near Lake Victoria in 1997, hence the name, and paleontologists have unearthed ~2,500 specimens.15 Their abundance was an exception during a time when cercopithecoids were rare. Victoriapithecus had closely set eyes and a generalized dental morphology, which indicates a generalist ecological niche. According to Cachel15 (p. 249): The cranium of Victoriapithecus resembled those of basal catarrhines, such as Aegyptopithecus and Early Miocene hominoids in Africa. . . . Among living cercopithecoids, the craniofacial morphology of Victoriapithecus most resembled the modern genus Macaca, although scaled down considerably in size. Victoriapithecus also appears to be a miniaturized version of a macaque in terms of generalized adaptations. It was about 3–5 kg, approximating that of living vervet monkeys (Cercopithecus aethiops). Postcranial anatomy also suggests that Victoriapithecus engaged in terrestrial locomotion as often as living vervet monkeys do, and these animals are often referred to as savanna guenons because of their wide-spread presence in open-country environments.

The last point’s important because it implies that even though Victoriapithecus had adapted to life in open woodlands or savannas, its brain remained at the prosimian grade of encephalization and corticalization. As explained elsewhere, its femur morphology (Figure 6.6) and olfactory-bulb size (Figure 8.4A) also remained outside the modern anthropoid range.16 The fossil evidence therefore indicates that cercopithecoid brains expanded into the modern anthropoid range after ~15 Ma, but the exact time remains uncertain. According to the fossil record, cercopithecoids first appeared ~26–23 Ma, but they remained rare until the late Miocene, ~7–6 Ma.15 This is about the time that the last common ancestor of macaques lived (Figure 6.7A).17 Something important seems to have happened during the late Miocene, when species-level morphological changes in cercopithecoids “exploded.”15 Later, during the Plio-Pleistocene, cercopithecoids underwent another phase of rapid speciation. Chapter 9 explores the timing of these adaptive radiations in relation to climate change. Turning to hominoids, Figure 8.2B plots two specimens of Proconsul, which date to ~18 Ma.15 Given this date, it lived 5– 8 million years after the hominoid–cercopithecoid split. Although its five-cusp (Y-5) molar morphology identifies Proconsul as a hominoid, it was an early, basal member of that clade. Otherwise, Proconsul closely resembled cercopithecoids in most aspects of its anatomy.15 Nevertheless, this anthropoid species had a brain in the upper part of the modern anthropoid size range, well above prosimians and about the same relative size as in modern hominoids (other than humans, of course).13 A later fossil ape, Hispanopithecus from ~10 Ma (not illustrated), had a similarly large brain.18 These findings indicate than an upward grade-shift in brain and cortex size had occurred in hominoids by ~18 Ma, a time when cercopithecoid brains were still in the prosimian range or smaller. Gonzales et al.13 have suggested that the brain-size estimates for Proconsul might be overestimates, but even if that’s the case, they had brains at least in the modern anthropoid range at a time when other anthropoid brains remained well below that grade. The Proconsul specimens also indicate that a relatively large cortex predates most skeletal traits that characterize modern hominoids, especially hominids (the great ape–human clade). Compared to cercopithecoids, extant hominids have a more dorsally located scapula (shoulder blade), a less rounded thoracic cavity (broad chests), broad sternums, a large body mass, and vertebrae that protrude into the chest cavity.15 The finding that cortical enlargement occurred before most hominid skeletal traits evolved contrasts with the other grade-shifts discussed in this chapter and in Chapter 7. For instance, grasping hands and convergent eyes evolved before the Euprimate cortex expanded into the modern prosimian range. Likewise, for both platyrrhines and catarrhines, their diagnostic skeletal and dental traits evolved before the cortex expanded into the modern anthropoid range. In hominoids, cortical expansion led the way. Perhaps their larger cortex and modified molars provided early Miocene hominoids with an advantage in exploiting forest niches near ground level while coping with the “relatively large-brained . . . competitors and predators” mentioned earlier. Box 16.1 describes some of the predators that Miocene primates faced. Large animals have difficulty climbing high into trees, so hominoids eventually developed either suspensory brachiation to re-enter this niche (as gibbons did), or they became larger, more terrestrial with robust skeletons (as gorillas, orangutans, hominins, and panins did). But hominoids of the early Miocene were smaller and more agile than either hominoids of the late Miocene15 or modern great apes. It was in the more cercopithecoid-like hominoids of the early Miocene that the cortex enlarged into the modern anthropoid and hominoid ranges.

Platyrrhines Like their contemporaries in the Old World, the New World anthropoids Chilecebus and Homunculus had small brains relative to body size (Figure 8.2B).19 The former lived ~20 Ma, weighed ~500 grams, and resembled marmosets; the latter lived ~16 Ma, weighed 2.0–3.5 kg, and was a diurnal frugivore–folivore.15, 20 Two other platyrrhines, Tremacebus and Dolichocebus, dated to ~20–17.5 Ma (not illustrated), have also been studied.8 The relative brain size of all four fossil platyrrhines fell within the modern prosimian range.8, 20 Later, probably during the middle-to-late Miocene, the brain and cortex expanded in platyrrhines, as it did independently in cercopithecoids. Anthropoid brains shape up The shape of brains also changed during anthropoid evolution. Figure 8.2C presents the results of a principal component analysis of brain shape in modern primates.21 As the right part of the figure illustrates, the first two principal components capture variation in the dorsoventral axis, rostral extension and rounding of the frontal lobe, flexing at the cranial base, and expansion toward the occipital pole. Both platyrrhines and cercopithecoids evolved a taller brain independently. Hominoid brains departed from the shape of basal Euprimate brains in a different way than did cercopithecoid brains, especially along the second principal-component axis, which captures more rounded, bulging frontal and occipital lobes. Gray-matter grooves In addition to information about the size and shape of the cortex, fossil evidence also reveals the pattern of sulci and the size of each lobe.13, 22 Figure 8.3A presents a phylogenetic analysis by Ni et al.,22 which emphasized platyrrhines. I begin, however, by considering sulci and lobes in catarrhines.

Figure 8.3 The emergence and loss of sulci in anthropoids. (A) The photographs in the lower left and upper right illustrate the arcuate and principal sulci (arrows) in a New World monkey (spider monkeys, Ateles) and an Old World monkey (mangabeys, Cercocebus). (B) Expansion of the frontal lobe in catarrhines. The brain drawing comes from a modern macaque, and it shows the calculation that produced the bar chart to its left. Abbreviations: AP, anterior-posterior; AS, arcuate sulcus; CS, central sulcus; IPS, intraparietal sulcus; ITS, inferior temporal sulcus; LatS, lateral (Sylvian) sulcus; LunS, lunate sulcus; Ma, million years ago; OB, olfactory bulb; PS, principal sulcus; SPS, superior precentral sulcus; STS, superior temporal sulcus. (A) Adapted from X. Ni, J.J. Flynn, A.R. Wyss, and C. Zhang, Cranial endocast of a stem platyrrhine primate and ancestral brain conditions in anthropoids, Science Advances 5:eaav7913, 2019. The brain photographs reproduced from the Wisconsin comparative mammalian brain collections, http://neurosciencelibrary.org/index.html. (B) Data from supplementary material in L.A. Gonzales et al., Cerebral complexity preceded enlarged brain size and reduced olfactory bulbs in Old World monkeys, Nature Communications 6, 7580, 2015.Reproduced with permission from http://brainmuseum.org. Specimens used are from the Defense Health Agency Neuroanatomical Collections Division of the National Museum of Health and Medicine, the University of Wisconsin and Michigan State comparative mammalian brain collections supported by the US National Science Foundation.

Among catarrhines, both Aegyptopithecus and Parapithecus had a small frontal lobe bounded caudally by the central sulcus.23 These early catarrhines lacked both an arcuate sulcus and a principal sulcus, which characterize the brains of modern cercopithecoids. Thus, the central sulcus probably evolved before the arcuate and principal sulci. In contrast to earlier catarrhines, the middle Miocene catarrhine Victoriapithecus had both arcuate and a principal sulci in addition to a central sulcus.13 According to the paleontologists who have studied these specimens, all the extinct anthropoids highlighted thus far— Chilecebus, Homunculus, Aegyptopithecus, and Victoriapithecus—had small frontal lobes compared to modern anthropoids. Because this list includes two platyrrhines and two catarrhines, at least some expansion of the frontal lobes must have occurred independently in Old World and New World anthropoids.9, 24 Figure 8.3B illustrates data for the two catarrhines, contrasted with a selection of modern cercopithecoids. Because Victoriapithecus had a relatively small frontal lobe and the sulcal pattern characteristic of modern cercopithecoids, the sulci must have predated at least one phase of frontal-lobe expansion.13 In contrast to the frontal lobe, the temporal lobe seems to have reached a roughly modern size by the early Eocene,25 long before the early Oligocene catarrhine Aegyptopithecus (~30 Ma).1, 23, 25 Because the anthropoid grade-shifts and frontal-lobe expansions occurred during the Miocene, an expansion of extrastriate visual cortex, which occupies the inferior temporal lobe, probably preceded grade-shifts of the brain and cortex into the modern anthropoid size range. The fossil and comparative data summarized in Figure 8.3 also overturn some prevalent ideas about sulcal evolution. Several platyrrhines, including spider monkeys (Ateles) and capuchin monkeys (Cebus), have central, arcuate, and principal sulci that closely resemble the like-named sulci in macaques and other cercopithecoids. The brain photographs in Figure 8.3 come from a cercopithecoid and a platyrrhine, and both have arcuate and principal sulci. Many neuroscientists have assumed that these sulci are homologous, but the fossil evidence shows otherwise.22 Fossil endocasts combined with a phylogenetic analysis of modern species show that the last common ancestor of capuchin (cebus) monkeys and macaques lacked both an arcuate sulcus and a principal sulcus, although it might have had a central sulcus. Thus, the arcuate and principal sulci evolved independently in platyrrhines and catarrhines, which means that they are homoplasies, not homologies. Figure 8.3 also reveals the loss of sulci in several lineages. For example, marmosets and tamarins (collectively the callitrichids) have lost several sulci—including the arcuate, central, intraparietal, inferior temporal, and lunate—as their brains became smaller in absolute size. In general, EQ values in most callitrichids are comparable to those in other anthropoids, although they are higher in the smallest species: pygmy marmosets (Cebuella pygmaea) and Geoffroy’s tamarin (Saguinus geoffroyi).9 EQ values in these instances probably reflect changes in body size more than brain size (Figure 8.11). Brains versus bulbs As relative brain size increased in anthropoids, the olfactory bulbs decreased in relative size. The timing of these changes provides clues about the trade-offs involved. The traditional view, based on modern species, has emphasized a trade-off between vision and olfaction during primate evolution. Primates are, after all, “visual animals.” There’s truth enough in this idea, but fossil evidence, combined with comparative neuroanatomy, points to some important nuances. First, I address the truth-enough part, then the nuances.

Figure 8.4 Olfactory bulb contraction. (A) Pink shading shows the range of olfactory bulb–brain size relations in modern anthropoids; green shading shows the range for modern strepsirrhines. Red letters indicate values for three extinct catarrhines, which range in date from the very late Eocene through the middle Miocene and had olfactory bulbs in or near the modern prosimian range. (B) Letters indicate the olfactory bulb–brain size values for two extinct plesiadapiforms (orange), two adapiforms (green), and three omomyiforms (blue). Note that the shading and scales differ from Part A. (C) Evolutionary trajectory of relative olfactory-bulb size in primates and related lineages. The vertical axis is in arbitrary units, with time running from bottom to top. Angles to the left indicate a decrease in relative olfactory-bulb size. (D) An evolutionary tree highlighting the fossil primates included in the figure. Colors correspond in Parts A–D. The question mark indicates an uncertain classification. (A) Adapted from L.A. Gonzales et al., Cerebral complexity preceded enlarged brain size and reduced olfactory bulbs in Old World monkeys, Nature Communications 6, 7580, 2015. (B) Adapted from E.C. Kirk et al., Cranial anatomy of the Duchesnean primate Rooneyia viejaensis: new insights from high resolution computed tomography, Journal of Human Evolution 74, 82–95, 2014. (C) Adapted from S. Heritage, Modeling olfactory bulb evolution through primate phylogeny, Public Library of Science One 9, e113904, 2014.(A) CC 4.0 (B) Copyright © 2014 Elsevier Ltd. © PLOS applies the Creative Commons Attribution 4.0 International (CC BY) license

Figure 8.4B shows that two plesiadapiform primates, Ignacius and Microsyops, had larger olfactory bulbs, in relative terms, than modern strepsirrhines have. Two adapiform Euprimates, Adapis and Notharctus, had olfactory bulbs at the upper extreme of the modern strepsirrhine range. These data support the idea that Euprimates underwent olfactory-bulb contraction during their early evolution. Now for the nuances: A phylogenetic analysis suggests that the decrease in relative olfactory-bulb size was modest in basal Euprimates and in both major strepsirrhine lineages: lorisiforms and lemuriforms.26 Both groups have olfactory bulbs within the overall Euarchontan range (Figure 8.4C), with somewhat larger absolute size.25,26,27 Taken together, these analyses indicate that relatively small olfactory bulbs are mostly a haplorhine and anthropoid trait, rather than a primate trait more generally.26 Figure 8.4A shows that the middle Miocene cercopithecoid, Victoriapithecus, had olfactory bulbs in the modern strepsirrhine size range, albeit at its lower extreme.13 So did two earlier catarrhines: Parapithecus and Aegyptopithecus. These findings imply that the olfactory bulbs contracted after ~15 Ma, the date of Victoriapithecus. Because platyrrhines, hominoids, and cercopithecoids diverged long before then, it’s clear that, like cortical expansion, olfactory-bulb contraction occurred independently in these three anthropoid lineages (Figure 8.4C). The same data indicate that most olfactory-bulb contraction occurred long after the visual cortex had reached its modern size, or nearly so. As mentioned earlier, (“Gray-matter grooves”), the temporal lobe, which includes the bulk of the extrastriate visual cortex, attained a roughly modern size by the early-to-middle Eocene.25 This finding raises an obvious question: If most of the olfactory-bulb contraction didn’t occur when the visual cortex expanded—indicating a trade-off

between vision and olfaction in early Euprimate evolution—what were the trade-offs and when did they occur? The fossil record suggests an answer: An expansion of the frontal lobe accompanied olfactory-bulb contraction during the Miocene. Significantly, it wasn’t just any part of the frontal lobe that expanded, it was a specific component: the granular prefrontal cortex. Chapter 13 discusses the new prefrontal areas that emerged during anthropoid evolution, an idea first advanced by Preuss and Goldman-Rakic.28 There I suggest that the “visual cortex” did expand as the olfactory bulbs contracted in primates, as usually supposed, but with two modifications of the traditional view: (1) For the most part, it happened during the evolution of anthropoids during the Miocene, not during the evolution of earlier primates; and (2) “visual cortex” is in quotation marks because the areas that emerged and expanded are not usually classified as such. In contemporary neuroscience, the visual cortex—even broadly construed—is usually restricted to areas in the posterior parietal, occipital, and inferior temporal lobes. Comparative neuroanatomy suggests that the trade-off associated with olfactory-bulb contraction involved new prefrontal areas that, because they receive visual information from the temporal and parietal cortex, might be considered “visual” in a functional sense. (Auditory inputs also contribute to novel functions of anthropoid prefrontal areas, so these frontal areas are much more than visual. But I think that visual inputs are more fundamental to their functions.) Chapter 13 (“Sights and sounds on the side”) proposes that a trade-off occurred as the prefrontal cortex gained more importance in making foraging choices, especially when anthropoids needed to make such choices at a large distance from food sources. In such circumstances, olfaction provided little guidance, and long-distance visual cues (along with acoustic cues and spatial memory) indicated food availability. The prefrontal cortex played a major role in such choices, which could explain why the frontal lobe expanded at roughly the same time as the olfactory bulbs contracted. Summary Cortical expansion occurred independently in platyrrhines and cercopithecoids of the middle-to-late Miocene, after ~16–15 Ma; and it happened earlier in hominoids, before ~18 Ma, also independently. When the cortex expanded, it didn’t do so uniformly. The temporal visual cortex had reached a modern size by the early-to-middle Eocene, long before a Miocene phase of frontal-lobe expansion. The olfactory bulbs contracted as the frontal lobe expanded, indicating a trade-off between olfaction and visual functions of the prefrontal cortex, not between olfaction and vision more generally. Intermission Like Chapter 6, the length of this chapter merits an intermission. Just about everything up to this point has been introduction (Chapters 1–6) or aspects of cortical evolution rarely considered by neuroscientists. Nerdiness on such a grand scale ends here, at least for a while. The topic of the next section is so popular that discoveries appear in The New York Times years before they diffuse into neuroscience monographs. So, finally—and at long last—the time has come to address what fossil evidence tells us about cortical enlargement in our own species. Many discussions of cortical evolution begin here, and that’s understandable. However, I hope that readers who have reached this point appreciate that the upward grade-shifts of human cortex depended upon a foundation established by earlier primates, especially Miocene hominoids. Plio-Pleistocene hominins Humongous hemispheres The last common ancestor of panins and hominins lived ~6.3 Ma, although, as mentioned earlier, a period of hybridization might have persisted for as much as two million years afterward.29 Figure 8.5 shows that until ~3–2 Ma, early hominin brains differed only modestly in absolute size from those of modern chimpanzees and gorillas. The figure also illustrates periods of global cooling, which occurred 2.9–2.4 Ma, 1.8–1.6 Ma, and 1.2–0.8 Ma. During cooling trends, drier and cooler conditions (Figure 6.7C) led to the contraction and drying of forests and the expansion of grasslands, open woodlands, and savannas, and they caused significant changes in the make-up and distribution of hominid species, including hominins.15 Chapter 9 returns to this point.

Figure 8.5 Cortical expansion in hominins. Absolute brain volume for fossil hominins and three modern hominids: modern humans, chimpanzees, and gorillas. The horizontal dashed line approximates the average for the latter two taxa. Purple and blue shading bound values for two groups of human species; red and green shading bound values for two australopithecine species. Yellow rectangles mark periods of global cooling and enhanced glaciation (Ice Ages). Abbreviation: Ma, million years ago. Adapted from I.T. Fiddes et al., Human-specific NOTCH2NL genes affect notch signalling and cortical neurogenesis, Cell 173, 1356–69, e1322, 2018.© 2018 Elsevier Inc.

In addition to an enlargement in absolute terms, hominin brains also expanded relative to body size. Figure 8.6 shows that ~3.8–3.1 Ma, Australopithecus afarensis—popularized as Lucy, the lucky lady of fossil fame and fortune—had a relative brain size that remained well below the level of subsequent human (Homo) species. The first hominin EQ values that clearly exceeded those of australopithecines occurred in Homo habilis ~2.2 Ma, but the phylogenetic reconstruction of Püschel et al.30 points to ancestral species that lived ~4.0–2.8 Ma (between the red circles) as the origin of accelerated brain expansion. There’s a rough correlation between EQ increases and three tool making traditions, a topic that Chapter 17 (“Types of tools”) explores.

Figure 8.6 Encephalization in hominins. Phylogram of encephalization quotient (EQ) values in three hominid lineages: chimpanzees, gorillas, and hominins. The red arrow points to the first Homo species with a dramatically encephalized brain; the red circles mark the divergence of lineages characterized by accelerated encephalization, as reconstructed in this analysis. The date ranges for three Paleolithic tool-making traditions are marked by colored shading at the top, as is the onset of Mesolithic technology. Abbreviations: A., Australopithecus; P., Paranthropus. Adapted from H.P. Püschel et al., Divergence-time estimates for hominins provide insight into encephalization and body mass trends in human evolution, Nature Ecology and Evolution, 5, 808–19, 2021. Dates for the earliest Oldowan artifacts come from S. Harmand et al., 3.3-million-year-old stone tools from Lomekwi 3, West Turkana, Kenya, Nature 521, 310–15, 2015. Dates for the Acheulean tool-making traditions come from R.G. Klein, The Human Career, Chicago University Press, Chicago, 2009.

Figure 8.7 uses the same data as Figure 8.6, but it plots EQ values differently.30 Because many details of the relationships among fossil hominins remain unresolved, it’s important not to take the phylogeny shown in this figure as the final word on the subject. Indeed, the authors present three possibilities, of which Figure 8.7 is only one. To add some perspective, this version of the figure includes divergence times among panins.31 Bonobos (Pan paniscus) diverged from chimpanzees (Pan troglodytes, comprising three subspecies) ~1 Ma (in other studies, ~2 Ma), at roughly the time that at least four human species lived.

Figure 8.7 Encephalization quotients and estimated divergence times in hominins and panins. Encephalization quotient (EQ) values for fossil hominins, modern humans, and modern panins, plotted by line color. The estimated EQ for each lineage ranges from a low value in dark purple through a range of higher values in lighter purple, orange, and yellow. Emergent traits in hominins (red bars and font) include small, diamond-shaped canine teeth, which are characteristic of all known hominins and contrast with the longer, sharper canines of chimpanzees and gorillas. Note that the estimated divergence of panins and hominins is more than 7 Ma, which differs from the current consensus, which dates the human–chimpanzee split to ~6.3 Ma (with a subsequent period of hybridization). Adapted from H.P. Püschel et al., Divergence-time estimates for hominins provide insight into encephalization and body mass trends in human evolution, Nature Ecology and Evolution, 5, 808–19, 2021. The divergence times for Pan species and subspecies are from J. Hey, The divergence of chimpanzee species and subspecies as revealed in multipopulation isolation-with-migration analyses, Molecular Biology and Evolution 27, 921–33, 2010. Dates for the tool-making traditions are the same as in Figure 8.6, with Ice Ages indicated by yellow rectangles.© 2021, Hans P. Püschel et al, under exclusive licence to Springer Nature Limited

Brain bulges As mentioned earlier, Australopithecus afarensis (~3.8–3.1 Ma) had brains that differed little from chimpanzees in EQ, and this conclusion also applies to sulcal patterns. Gunz et al.32 re-analyzed fossil endocasts for several specimens and concluded, contrary to some prior reports, that sulci in the frontal and occipital lobes of this extinct hominin closely resembled those of modern chimpanzees and not modern humans. Figures 8.8 and 8.9 address changes in brain shape and sulcal patterns during hominin evolution. Figure 8.8A focuses on expansion of the frontal lobe, as reflected in the relative locations of the inferior precentral sulcus and the coronal suture: a cranial landmark. Ponce de Leon et al.33 grouped cranial endocasts into three categories: (1) those showing nearly complete overlap of these two structures; (2) those showing a retreat of the inferior precentral sulcus to a location caudal to and not overlapping with the coronal suture; and (3) intermediate cases. It’s difficult to pinpoint a break in the data, but after ~1.6 Ma brains with an inferior precentral sulcus caudal to the coronal suture emerged, and ~1.0–0.6 Ma these brains became larger.

Figure 8.8 Changes in human brains and brain shape in hominids. (A) Expansion of the frontal lobe as reflected in a caudal shift of the inferior precentral sulcus (IPS) relative to the coronal suture (CS), which is the abutment of the frontal and parietal plates of the cranium. Orange circles, complete overlap of the IPS and CS; blue circles, retreat of the IPS caudal to the CS; gray, an intermediate condition. (B) Principal component analysis of brain shape in modern hominoids (95% confidence ellipses). Neanderthals (unfilled black squares) and middle-to-late Pleistocene humans (magenta squares) had brains closer in shape to those of modern humans (blue shading) than did earlier humans (early Homo, blue squares). Abbreviation: Ma, million years ago; PC, principal component. Adapted from M.S. Ponce de Leon et al., The primitive brain of early Homo, Science 372, 165–71, 2021.

Figure 8.8B presents a principal component analysis (PCA) of brain shape in the same specimens. The first principal component captures a widening and bulging of the frontal lobe, another indication of its expansion. The PCA documents the impression gained from qualitative observation of hominin endocasts: A major difference between modern humans and other hominins is a wider, rounded frontal lobe, the continuation of a phylogenetic trend that began in earlier hominoids (Figure 8.2C). The message from Figure 8.8B is that the brains of early Homo species were shaped differently than modern human brains, an idea supported by the analyses in Figure 8.9.

In hominins, brain shape has a close relationship with the shape of the skull. So, rather than working with brain endocasts, Bruner et al.34 and White et al.35 have performed principal component analyses on skull shape in hominins. Figure 8.9A shows the analysis of Bruner et al., which indicates that the frontal lobe bulged into its current shape during the evolution of early and modern Homo sapiens. These changes occurred after ~750,000–550,000 years ago, when Homo sapiens diverged from both Neanderthals and Denisovans (a species closely related to Neanderthals).36 Figure 8.9B presents an analysis of skull shape by White et al.35 They made more measurements than Bruner et al. but came to similar conclusions. Two of the early Homo sapiens fossils, one from ~315,000 years ago37 and the other from ~195,000 years ago, had frontal shapes overlapping with Neanderthals; more recent specimens had skulls shaped like those of modern humans, with a notably high forehead (the frontal squama). The ~315,000 year old Homo sapiens specimen had a fully modern face and an EQ comparable to modern humans, but with a more elongated, less globular braincase (Figure 8.9C).38 These findings indicate that significant changes occurred in the shape of the cortex after the emergence of the first Homo sapiens.38, 39

Figure 8.9 Skull shape in Homo species. (A) Negative values of the first principal component reflect an upward bulging of the forehead, the frontal squama. Dark blue circles plot values from early Homo sapiens; light blue circles do so for recent specimens; and blue shading bounds these data. Orange shading bounds data from Homo heidelbergensis; gray shading from Neanderthal humans; and the green line from Homo erectus. (B) An analysis like that in Part A, but based on more skull measurements, some of which are illustrated to the right and bottom of the plot. Bounding as in Part A, with the addition of purple for a nonHomo species: Paranthropus boisei. (C) Principal component analysis based on 31 landmarks and semi-landmarks of the midsagittal profile for fossil endocasts. The more globular skill of modern Homo sapiens, to the lower left, dates to ~95,000 years ago; the skull to the right reflects the elongated brain of early Homo sapiens, based on Moroccan fossils from 315,000 years ago. Abbreviations: H., Homo; ka, thousand years ago; P., Paranthropus.

(A) Adapted from E. Bruner et al., Geometric variation of the frontal squama in the genus Homo: frontal bulging and the origin of modern human morphology, American Journal of Physical Anthropology 150, 313–23, 2013. (B) Adapted from S. White et al., Taxonomic variation in the supraorbital region of catarrhine primates, American Journal of Physical Anthropology 171, 198–218, 2020. (C) Skull photographs from E.M.L. Scerri et al., Did our species evolve in subdivided populations across Africa, and why does it matter? Trends in Ecology and Evolution, 33, 582–94, 2018. Principal component analysis adapted from J.-J. Hublin et al., New fossils from Jebel Irhoud, Morocco and the pan-African origin of Homo sapiens, Nature 546, 289–92, 2017.(C ) Copyright © 1969, Elsevier

Figure 8.10 shows data from Bastir et al.,40 which indicate that similar changes occurred in the temporal lobe.41 Specifically, the anterior temporal lobe expanded in modern humans42 and continued changing in shape until ~130,000 years ago.40

Figure 8.10 Anterior temporal bulging in four human species and chimpanzees. (A) The first principal component of anterior temporal lobe shape clearly delineates data from modern humans (blue) and those from Homo heidelbergensis (orange) and Homo neanderthalensis (gray). The blue shading bounds data for early humans (dark blue circles) and recent humans (light blue circles). (B) Left: a modern human skull with landmarks in the middle cranial fossa (1–5) and optic chiasm (6). Right: the key for Part A. (C) A display of the differences in landmarks, with emphasis on landmarks 4–6. Left: The brown triangle connects landmarks 4–6 in Homo heidelbergensis. Right: The blue triangle, superimposed on the Homo heidelbergensis triangle, connects the same landmarks in modern humans. Note the anterior and dorsal shift in landmark 5, the anterior extreme of the temporal pole. Adapted from M. Bastir et al., Middle cranial fossa anatomy and the origin of modern humans, Anatomical Record 291, 130–40, 2008.Copyright © 2008 Wiley-Liss, Inc

In another set of studies (not illustrated), Bruner et al.43, 44 demonstrated an additional enlargement of the medial parietal lobe in modern humans. Likewise, Pereira-Pedro et al.45 found that the parietal plate (of the skull) has bulged significantly

more in modern humans than in Neanderthal humans. In summary, from the endocast and skull-shape analyses summarized here, it seems most likely that the human cortex reached its current shape ~130,000 years ago.36, 46, 47 Body or brain? Higher EQ values can result from either an increase in the size of the brain or a decrease in the size of the body, and a detailed examination of body–brain allometry can distinguish these two factors. In addition, variations in the slope of brain– body regressions influence the EQ, and primates include both high-slope clades and low-slope clades. For example, to achieve a given EQ value, a high slope requires a larger augmentation of brain size when body size increases than a low slope does.

Figure 8.11 Grade-shifts in brain size–body size allometry in primates. White arrows indicate statistically significant changes in body size; colored arrows show changes in brain volume. Line color indicates high-slope clades (red and green) and low-slope grades (blue and purple). Two arrows of the same type indicate that the degree of change for that variable exceeds the other. The dashed-line arrows depict several independent instances of a given type of grade-shift. Adapted from J.B. Smaers et al., The evolution of mammalian brain size, Science Advances 7: eabe2101, 2021.

Figure 8.11 summarizes results for primates taken from a mammal-wide analysis. Smaers et al.48 examined changes in the slopes and intercepts of the brain volume–body mass relationship, taking into account phylogenetic sampling. The figure shows that changes in encephalization depend on evolutionary changes in both brain size and body size. This distinction is important because these two variables respond to ecological factors in different ways. Of particular interest for humans, the hominin grade-shift in encephalization depended on both a decrease in body size and an increase in brain size. Because the cortex makes up ~80% of human brains, this conclusion certainly applies to the cortex as well as to the brain as a whole. One pattern of evolutionary change has been especially common during primate evolution: an increase in brain size that exceeds an increase in body size (noted by one white arrow and two green arrows in Figure 8.11). Smaers et al. identified nine independent instances of this kind of grade-shift: in basal strepsirrhines; in hominids; in stem catarrhines; in cercopithecines; in colobines; and independently in four platyrrhine lineages. Chapter summary Several independent cortical expansions occurred during primate evolution (Figure 8.12): in Eocene crown Euprimates; in Miocene platyrrhines, cercopithecoids, and hominoids; and in Pleistocene hominins. Except for the hominoid grade-shift, most characteristics of each clade were in place long before upward grade-shifts of the neocortex.

Figure 8.12 Summary of Eocene and Miocene grade-shifts. Line color indicates the grade of corticalization and encephalization. Vertical black bars mark upward gradeshifts; a vertical gray bar highlights an earlier temporal-lobe expansion. In most instances, the grade-shift could have occurred later than indicated on the plot. For example, the Victoriapithecus and Homunculus specimens indicate that primates in those lineages remained at the prosimian grade of cortex size until ~16–15 Ma, but it’s uncertain how long afterward the cortex entered the modern anthropoid range. For illustrative purposes, the Proconsul line is not to scale in the time domain; they were early Miocene primates. Abbreviations: Ma, million years ago; P-P, Pliocene-Pleistocene.

In hominins, the cortex enlarged modestly during the first 3 million years or so after the hominin–panin split. Then, sometime around ~3 Ma, cortical expansion accelerated, accompanied by enlargement and widening of the frontal lobe, probably ~1.6–0.6 Ma. The parietal and anterior temporal lobes also enlarged, with the human cortex reaching its current size and shape ~130,000 years ago. This chapter’s epigraph features Tarzan. According to the story, after being abandoned in the African jungle as a young child, he grew up in the company of apes. Eventually, Tarzan taught himself to read, using a children’s primer. As an autodidact:* his reason told him that he was of a different race from his wild and hairy companions. He was a [H-U-]M-A-N, they were A-P-E-S, and the little apes which scurried through the forest top were M-O-N-K-E-Y-S.

Too bad there wasn’t a cladist around to explain to Tarzan why there’s no such thing as a monkey; and even if there is, they aren’t apes. Regardless, he had no human companions until, one day, a group of shipwrecked English-speakers arrived. In his rainforest isolation, he had held his species in high regard—until he observed human behavior. As he watched the group from afar, he lamented their stupidity, at least of the men in the party. “But the girl, ah—that was a different matter.” Long before Tinder, Tarzan knew how to sweep right, which he did promptly. Despite his disappointment in human intelligence, Tarzan must have noticed that they had large brains. However, as the next chapter shows, that’s not enough to survive in a forest, especially one in flux from climate change. References 1. 2. 3. 4. 5. 6.

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* The quotations come from https://www.gutenberg.org/files/78/78-h/78-h.htm, Edgar Rice Burroughs, Tarzan of the Apes, 1914

9 Tempo and temperature Overview Three generalizations emerge from primate paleoneurology: (1) Several pulses of cortical expansion seem to have occurred during global cooling trends: in crown Euprimates of the late Eocene; in middle-to-late Miocene platyrrhines and cercopithecoids; and in Plio-Pleistocene hominins; (2) upward grade-shifts in cortex size preceded adaptive radiations in several lineages; and (3) skeletal changes usually occurred millions of years before the cortex enlarged. However, only the second of these generalizations applies to hominoids, the clade that includes apes and humans. The hominoid cortex expanded during the early Miocene, a time of stable and warming temperatures, and most of the characteristic skeletal traits of hominoids evolved after the cortex reached a size typical of modern species. Except for dental morphology and relative brain size, these early, large-cortex hominoids closely resembled cercopithecoids, their sister group. Thus, a hominoid-grade cortex emerged in ancestral hominoids that were smaller and more agile than modern great apes.

Introduction In this chapter, arid air taxes trees, ice increases intermittently, and a current carries us into the past. But I begin with a science fiction movie entitled The Time Machine. On New Year’s Eve 1899, a group of Victorian gentlemen discuss a recent invention by one of their friends, who created a device that can send a traveler through time. Naturally, they ask how it works. As his answer, the inventor demonstrates time travel with a miniature version of his machine, using a cigar as a surrogate human. Just push the lever back to journey into the past, he says; push it forward to explore the future. I hope the cigar had a good time. Regardless, the inventor’s less-than-satisfying account of time travel has parallels in discussions of cortical evolution. There are many levels of explanation, some more rewarding than others. Most ideas about primate success emphasize whole-cortex measures: a larger volume of cortex (or neocortex), more neurons, more or denser dendritic spines, more synaptic connections, or some combination of all that. There’s truth enough in these ideas, but they have a significant shortcoming: They aren’t very useful for understanding the lives of our primate ancestors or how an altered cerebral cortex gave them specific survival advantages. Take hominoids, for example. As explained in Chapter 6, most hominoid species became extinct during the late Miocene. Before ~10 Ma, hominoids lived in tropical or subtropical forests with moist soil and fruit-bearing trees, such as figs. Then, a cooling and drying of the atmosphere favored deciduous trees, which don’t produce nutritious fruit. As species such as oaks, elms, and maples came to dominate their now-dry forests, most hominoids died out. The demise of so many ape species shows that primates have always needed more than a large cortex to cope with changing ecosystems. One hominoid species that survived was the ancestor of hominins and therefore of humans, and this species had a cortex about the same size as that of many now-extinct hominoids, with a similar number of dendritic spines, synapses, and neurons, and all the same cell types. Time travel “So we beat on, boats against the current, borne back ceaselessly into the past.”

—F. Scott Fitzgerald, The Great Gatsby (Scribner, New York, 1925, p. 180)

To learn which selective factors favored cortical expansion, we need to do more than measure sizes, shapes, and sulci, and we need to do more than count neurons, synapses, and dendritic spines. Those studies are important, but it’s also essential to engage in some time travel: into the past, as Fitzgerald says in one of the most quoted endings in American fiction (it’s also his epitaph). Fields of biology that look back in time—paleontology (Chapter 5), paleoecology (Chapter 6), and paleoneurology (Chapters 7 and 8)—can help neuroscientists enrich the inferences that arise from comparative and functional neuroanatomy. A synthesis of these fields has the capacity to build a new vision of cortical evolution, but the realization of that ambition requires delving into the functional specializations of myriad cortical areas, inferring when these areas first appeared or expanded, and recognizing the kind of primates and ecological circumstances in which it happened. That’s a tall order, and I can’t do it in a single chapter. Accordingly, this chapter begins the implementation of an ambitious project that requires the remainder of the book, not to mention future research and future books (if books still exist so far into the future). To explain why so much needs to wait, it’s helpful to restate the two “why” questions posed at the beginning of Chapter 1: 1. 2.

Why did the cortex expand during primate evolution? Why does the cerebral cortex have the areas observed in primates today?

To answer these questions, we need to consider the ecological drivers that favored a large cortex, and one reason for deferring a general discussion of this topic is that the second question changes the nature of the first. Two things happened to the cortex during primate evolution: it expanded several times (Chapters 7 and 8) and new areas emerged (Chapters 11–13). So, in addition to asking what selected for a larger cortex, considered as a whole, we can also ask what advantages new or enlarged areas provided to ancestral primates—in their time and place. If we can answer the second question, we might explain cortical enlargement without assuming that something selected for a larger cortex, more neurons, denser dendritic spines, or additional synaptic connections per se. The same goes for the evolution of cortical cell types and layers. The first question poses another problem, as well. It could be read as implying a single answer, which is unlikely. Whatever ecological pressures favored cortical expansion in Eocene primates, they were unlikely to exist or play the same role tens of millions of years later, when subsequent grade-shifts occurred in cercopithecoids, platyrrhines, hominoids, and

humans. Primates of each epoch and geographical location faced different climatic conditions, competitors, and predators. Available foods and social systems changed, too. Among many ecological transformations, landmasses split and combined: the former leading to the isolation of populations; the latter establishing access to new territories, plants, and animals. Because the earth and its ecosystems changed so radically during the Cenozoic, the first question probably has several answers, not just one. The literature on correlates of brain size sometimes leaves the impression that only one or two factors caused cortical expansion. That’s possible, but it’s more likely that there were many influences, which operated at different times and in different places and, most importantly, on different kinds of cortical representations (see Proposal 1, p. 283). Obviously, an analysis that focuses on new and expanded cortical areas requires some specificity about the areas involved, which comes later (Chapters 11–14). So, instead of considering why the cortex changed as it did, this chapter establishes a foundation for such discussions by restating and reorganizing some of the material from Chapters 4 to 8, with emphasis on the tempo of cortical grade-shifts in relation to three sets of data: global climate change; adaptive radiations; and skeletal evolution. In this chapter, I limit references to the literature and previous chapters, but all the key points come from earlier in the book. The downside of this reorganization is a fair amount of repetition, but I think that the benefits, which include consolidating the insights gleaned from fossil data and considering them from various perspectives, outweigh the costs. Because this chapter begins a consideration of the ecological factors that accompanied cortical expansion, and because this discussion continues for the remainder of the book, a little time travel into future chapters might be useful: • • •

•

This chapter examines the tempo of cortical enlargement in relation to changes in global temperature, the diversification of primates, and the emergence of postcranial skeletal traits. Chapter 10 looks at cortical expansion in other mammalian orders. Fossil evidence shows that convergence is the rule, not the exception. Part IV. After Chapter 11 dispels some misconceptions, Chapter 12 identifies the cortical specializations of primates and explores which areas expanded as primate brains enlarged into the modern prosimian size range; Chapter 13 explores the same subjects for entry into the modern anthropoid range; and Chapter 14 examines cortical expansion in humans. Chapters 12 to 14 incorporate ideas about the life and times of extinct primates from Part II. Part V. Chapter 15 advances several proposals about the advantages provided by areas involved in the Eocene gradeshifts; Chapter 16 does so for the areas that contributed to the Miocene grade-shifts; and Chapter 17 discusses the Pleistocene grade-shifts in hominins. All three chapters incorporate knowledge from Part III, mainly Chapters 7 and 8, about the timing of cortical expansion. Chapter 18 compiles some conclusions.

Cooling and crisis Degrees of difficulty Stem primates and basal Euprimates evolved in a hot, humid world that supported dense rainforests as far north as Alaska and Upper Canada. Later, global cooling dried both the atmosphere and soil, which placed these forests under stress. Periods of warming or stable temperatures sometimes persisted for several million years, but the overall trend was toward the cooler, drier atmosphere that characterizes Earth today (Figure 9.1A). Figure 9.1 places cortical expansion in the context of climate change. Cooling episodes that began ~40 Ma, ~14 Ma, and ~2.9 Ma correspond roughly with upward grade-shifts in cortex size in specific primate lineages. Throughout much of the Eocene, primate brains remained similar in size to those of rodents and tree shrews. Then, most likely during the late Eocene, an upward grade-shift occurred independently in the major primate lineages. Additional grade-shifts followed. The next section, “Cool times and a cortex of modern aspect,” revisits the relationship between global cooling and the Eocene gradeshifts; “Cooler times and a cortex of modern size” does so for the Miocene grade-shifts; and “Coolest times and the cortex of modern humans” addresses the timing of cortical expansion in relation to Plio-Pleistocene Ice Ages.

Figure 9.1 Upward grade-shifts in cortex size and periods of global cooling. (A) Global temperatures during the Cenozoic, from Figure 4.4. Yellow shading highlights five periods of global cooling. Green arrows indicate the onsets of three global-cooling episodes that might have been associated with cortical grade-shifts. (B) On the same timescale as Part A, background shading indicates several grades of encephalization and corticalization, as indicated for three of the grades by the key at the bottom. Triangles plot fossil primates with Euarchontoglires-grade brains (black), prosimian-grade brains (red), and a hominoid-grade brain (blue). They constrain the boundaries among the background colors. Bottom: brains of three grades, with the neocortex in tan. The figure excludes tarsiers. Abbreviations: EOCT, Eocene–Oligocene climatic transition; Ma, million years ago; P-P, Pliocene-Pleistocene. (A) Adapted from T. Westerhold et al., An astronomically dated record of Earth’s climate and its predictability over the last 66 million years, Science 369, 1383–7, 2020. (B) Chronogram based on Figure 4.2; triangle placement from Chapters 7 and 8. Primate brain drawings in Part B by Mary K.L. Baldwin. Rodent drawing in Part B courtesy of R.E. Passingham, Understanding the Prefrontal Cortex: Selective Advantage, Connectivity, and Neural Operations, Oxford University Press, Oxford, 2021.

Cool times and a cortex of modern aspect Crown Euprimates are sometimes called “primates of modern aspect,” which contrasts with plesiadapiforms (stem primates). Throughout most of the Eocene, the skeletal and visual adaptations that give the impression of modernity—grasping feet and hands, long legs, fingernails instead of claws, and forward-facing eyes—were in place, but primates couldn’t be fully modern until they had a relatively large cortex.

The upward grade-shifts into the modern prosimian range had already happened in at least some primate lineages by ~37– 36 Ma, the dates of the late Eocene Euprimates Rooneyia and Necrolemur. These fossil primates lived a few million years after global cooling began ~40 Ma, during a 6-million-year-long period of climate change that ended with the temperature plunge of the Eocene–Oligocene climatic transition (EOCT) ~34 Ma. Episodes of global cooling placed significant demands on species that depended on arboreal resources, as Eocene primates did. In addition to the retreat and contraction of rainforest habitats, fruit production changed as the climate cooled and the atmosphere became more arid. A loss of rainforests at higher latitudes exacerbated the competition for resources. During the Eocene, primate species competed with each other, as well as with other animals that exploited arboreal habitats. Chapter 12 explores these points while considering cortical specializations in primates. Cooler times and a cortex of modern size After the Eocene, anthropoids became the several-kilogram animals typical of modern monkeys and gibbons. As they did, smaller anthropoids died out. It’s not clear why all these small (less than 1 kg) primates became extinct,* but cooling-induced stress on forest resources and the retreat of forests toward the tropics probably contributed to their demise. The EOCT triggered deforestation and an increase in the size of primates. As they became larger, Oligocene and Miocene anthropoids foraged diurnally over an extensive and predator-rich home range (Box 16.1), initially as slow and cautious arboreal quadrupeds, and they required more food than their smaller ancestors. Small anthropoids probably couldn’t cover the distances needed to get enough food. As anthropoids grew in body mass, brain size initially remained within or below the modern prosimian range. This condition persisted until at least the middle-to-late Miocene in monkeys, as indicated by the prosimian-size brains of Victoriapithecus among cercopithecoids and Homunculus among platyrrhines, which date to ~16–15 Ma (Figure 9.1B). Then something favored an expansion of their cortex, especially the frontal lobe. One possible ecological driver was a ~5 millionyear-long episode of global cooling that began ~14 Ma (Figure 9.1A). Resource disruptions caused by this middle-to-late Miocene cooling trend ushered in periods of dearth and the failure of previously productive food sources, so anthropoids needed to make more foraging journeys over larger distances to obtain necessary resources, and they risked predation to do so (Box 16.1). Exploitation of fall-back foods provided an important alternative source of nutrients, but predation risks remained high. During periods of increased extinctions, anything that reduced predation risks would have been under strong selection, and there’s reason to believe that anthropoids evolved new cortical mechanisms that mitigated predation risks. Chapters 13 and 16 consider the cortical areas that emerged and expanded during anthropoid evolution, along with their contribution to predator avoidance. In hominoids, a grade-shift into the modern hominoid range had already occurred by ~18 Ma, as indicated by Proconsul (Figure 9.1B). Hominoids emerged ~26–23 Ma from their catarrhine ancestors, became larger ~20 Ma, and diversified ~17– 15 Ma. By “larger,” I mean that they weighed one-to-several kilograms, like modern monkeys and gibbons (but not the 10s or 100s of kg typical of humans and great apes). The entire span of hominoid existence had, up to this point, taken place during a time of relatively stable temperatures, much warmer than today (Figure 9.1A). As mentioned earlier, the global cooling of the middle-to-late Miocene triggered a wave of extinctions among hominoids, as their forest habitats became increasingly arid. Fruit-bearing trees died out, and so did many hominoids. Coolest times and the cortex of modern humans Expansion of the human cortex was also associated with global cooling: episodes of glaciation known as Ice Ages. The major upward grade-shifts of human cortex occurred after ~3 Ma, and for the first two million of those three million years, Ice Ages came and went at least three times: once during the late Pliocene (~2.9–2.6 Ma) and twice during the Pleistocene (~1.8–1.6 Ma and 1.2–0.8 Ma). They altered ecological conditions in many ways. Among them, savannas expanded at the expense of forest habitats, as previously occupied territories became uninhabitably cold. Other things changed, too. For instance, new traditions of tool manufacture developed during this period, so there’s more to the story than global cooling, and there’s reason to think that human societies changed at about the same time. Still, the cooling-induced expansion of savannas and open woodlands played a major role in hominin evolution, during which our ancestors increasingly relied on C4 food sources that thrived in such conditions. Chapter 17 considers these selective factors, among others that may have contributed to cortical expansion in humans. The Pleistocene began ~2.6 Ma, but its first Ice Age didn’t start until ~1.8 Ma. A recent climate-simulation model confirmed the importance of this early-Pleistocene period of climate stability for human evolution. Later in the Pleistocene, episodes of glaciation had a major influence on evolving Homo species. Then, from 750,000–550,000 years ago, climate change in Africa contributed to the emergence of early Homo sapiens.1

Figure 9.2 Brain size, body size, land productivity, and rainfall. Brain volume and body size in Homo species from ~1 Ma. (A) Body mass. (B) Brain volume as measured from cranial endocasts. (C) Brain volume versus two other factors. Human brains of the past million years were largest in relatively unproductive lands (left) that had consistent amounts of precipitation (right).

Adapted from M. Will et al., Different environmental variables predict body and brain size evolution in Homo, Nature Communications 12, 4116, 2021.

Will et al.2 analyzed the relationship between a number of environmental variables and both brain size and body size in three groups of humans: middle Pleistocene Homo species (but not Homo sapiens); Neanderthal humans; and Homo sapiens (Figure 9.2). Their analysis included mean temperature and temperature variation, among other environmental factors. Using a database of Homo crania dated to ~1 Ma or less, they found a statistically significant relationship between body size and global temperature, but not between brain size and any temperature variable. The ~1 Ma date is important because their analysis doesn’t rule out an association between global cooling and earlier cortical expansions, which had been in progress for 1–2 million years by that time. They also found a small but statistically significant association between a larger brain and life in a relatively unproductive habitat, as savannas and grasslands tend to be, and a similarly small relationship between a larger brain and a consistent degree of precipitation, which might have permitted successful exploitation of such habitats (Figure 9.2C). Corticalization and speciation Cortical expansion seems to be related to adaptive radiations in primates. For hominoids, an upward grade-shift of the brain and cortex into the modern hominoid range had occurred by ~18 Ma, before they diversified and radiated ~17–15 Ma as they migrated out of Africa. According to one reading of the fossil evidence, the hominoid ancestors of hominins migrated back into Africa ~10 Ma3 with a hominoid-grade cortex. In cercopithecoids, the cortex increased in size after ~15 Ma, when Victoriapithecus thrived but monkeys were rare in the Old World, relative to modern times. The cortex of middle Miocene cercopithecoids remained in the modern prosimian sizerange, if that. It seems likely that cortical expansion occurred during the Miocene cooling trend of ~14–9 Ma. If so, then cercopithecoids had an anthropoid-grade cortex when they diversified during the late Miocene (~7–6 Ma) and migrated out of Africa during the very late Miocene (~5.3 Ma).3 Platyrrhines radiated at roughly the same time as cercopithecoids,4 and fossil endocasts indicate that their brain, cortex, and frontal lobe remained at the prosimian grade until after ~16 Ma. Therefore, cortical expansion probably preceded a late Miocene diversification and dispersal of platyrrhines, as it did in cercopithecoids. Cortex and corpus Another implication of Figure 9.1 is that the skeletal synapomorphies of most primate groups evolved at different times than upward grade-shifts in the size of the cortex. In summary: •

•

•

The characteristic traits of Euprimates—such as forward-facing eyes, grasping hands and feet, and fingernails—had all evolved by the beginning of the Eocene (~55 Ma), and probably earlier. Thus, the major synapomorphies of Euprimates were in place long before an expansion of the cortex into the modern prosimian range, which probably occurred during the late Eocene (after ~40 Ma). By the late Oligocene (~28–23 Ma), platyrrhines and cercopithecoids had evolved into larger animals with skeletal adaptations for quadrupedal locomotion, a bony post-orbital septum, and modified anterior dentition.3 These traits evolved long before an expansion of their cortex into the modern anthropoid range, which probably occurred during the middle-to-late Miocene (after ~16–15 Ma). Hominins developed bipedal locomotion, reduced (diamond-shaped) canine teeth, and an in-line hallux by the early Pliocene (~5–4 Ma), long before the major expansions of human cortex, which mostly occurred during the Pleistocene (~2.6–0.6 Ma).

Because skeletal specializations evolved millions of years before cortical expansion in these three primate lineages, it’s likely that different selective factors were at work for body and brain. A similar conclusion applies to hominoids, but with the opposite relative timing. Their cortical expansion occurred before the emergence of their characteristic skeletal traits, especially for hominids. Early hominoids are sometimes called “dental apes” in recognition of their similarity with cercopithecoids, their sister group, except for their characteristic molar morphology. Like cercopithecoids and platyrrhines, the observation that hominid skeletal specializations evolved millions of years apart from cortical expansion also points to different ecological drivers for the cortex and the postcranial skeleton. Chapter summary Several times during the Cenozoic, global cooling caused the contraction and drying of dense tropical and subtropical forests, which primates needed for nutritional resources and protection from predators. • • •

A late Eocene cooling trend, from ~40–34 Ma, seems to have accompanied an expansion of the cortex in crown Euprimates: from the Euarchontoglires grade to the prosimian grade. Most strepsirrhines and tarsiers remained at this grade, which—to state an astoundingly obvious point explicitly—is why it’s called prosimian. Much later, during the middle-to-late Miocene, a global cooling trend from ~14–9 Ma seems to have been associated with the cortex enlarging into the modern anthropoid range: independently in cercopithecoids and platyrrhines. Among hominins, the cortex entered the modern human range during a period characterized by repeated Ice Ages.

The climatic circumstances of cortical enlargement were different in hominoids. An upward grade-shift in cortex size had occurred by ~18 Ma. Warm and stable temperatures prevailed from the origin of hominoids, ~26–23 Ma, until that time. Cortical expansion also appears to have a relationship with adaptive radiations. Hominoids, cercopithecoids, and platyrrhines had a modern-size cortex before they diversified and radiated. This pattern might reflect advantages that new and

expanded cortical areas provided to Miocene anthropoids, which empowered them to exploit available habitats in more effective ways (Chapter 16). Finally, the characteristic skeletal traits of crown Euprimates, cercopithecoids, platyrrhines, and hominins evolved millions of years before cortical expansion in these lineages. In hominoids, the cortex and body changed in the opposite order; the cortex enlarged while hominoids were smaller and more agile than modern great apes and humans: more monkey-like than great-ape-like. A gap of millions of years between cortical and skeletal changes suggests that different selective factors affected the body and the brain. This chapter began with a comment on The Time Machine, a tale of time travel. If we had such a device and set its controls to explore the early Eocene, we would see some utterly unfamiliar mammals (as we sweltered in tropical temperatures). It’s doubtful that most of us would recognize the ancestors of cetaceans, ungulates, and carnivores for what they were, but some rodents would have been familiar. The next chapter explores what fossils have to say about cortical expansion in these mammals and earlier ones. References 1. 2. 3. 4.

Timmermann, A., Yun, K.-S., Raia, P., Ruan, J., Mondanaro, A., Zeller, E., Zollikofer, C., Ponce de León, M., Lemmon, D., Willeit, M., & Ganopolski, A. Climate effects on archaic human habitats and species successions. Nature 604, 495–501 (2022). Will, M., Krapp, M., Stock, J.T., & Manica, A. Different environmental variables predict body and brain size evolution in Homo. Nature Communications 12, 4116 (2021). Cachel, S. Fossil Primates (Cambridge University Press, Cambridge, 2015). Silvestro, D., Tejedor, M.F., Serrano-Serrano, M.L., Loiseau, O., Rossier, V., Rolland, J., Zizka, A., Höhna, S., Antonelli, A., & Salamin, N. Early arrival and climatically linked geographic expansion of New World monkeys from tiny African ancestors. Systematic Biology 68, 78–92 (2018).

* The smallest modern anthropoids, pygmy marmosets (Cebuella pygmaea), weigh ~115 grams, and other platyrrhines, including tamarins and owl monkeys, are only a little larger. However, as explained in Chapter 2 (Box 2.2), small-bodied platyrrhines have descended from larger ancestors. The smallest modern catarrhines, talapoin monkeys (Miopithecus talapoin), sometimes weigh less than 1 kg, but they, too, have undergone a relatively recent reduction in body size.

10 Other orders Overview Midway through the Mesozoic, ~196–190 Ma, mammalian brains reached a size typical of Paleocene eutherians, which lived at least 120 million years later. During the Paleocene, body size increased without much, if any, increase in relative brain or cortex size, as mammals radiated into niches vacated by the end-Cretaceous extinction. Later, the cortex expanded independently in several mammalian lineages after they saturated the available habitats and competition among mammals intensified. Cortical enlargement in cetaceans most closely resembled what happened in primates: an upward grade-shift in toothed whales by the late Eocene (in parallel with crown Euprimates); another in oceanic dolphins during the middle-to-late Miocene (in parallel with cercopithecoids and platyrrhines). In contrast, cortical expansion in other large-cortex mammals, such as ungulates and carnivores, had a different, mostly later time course. . . . on the planet Earth, [humans] had always assumed that [they were] more intelligent than dolphins because [they] had achieved so much—the wheel, New York, wars and so on—whilst all the dolphins had ever done was muck about in the water having a good time. But conversely, the dolphins had always believed that they were far more intelligent than [humans]—for precisely the same reasons. —Douglas Adams, The Hitchhiker’s Guide to the Galaxy

Introduction In this chapter, a marsupial masquerades as a monkey; cetaceans develop a whale of a cortex; and primates get a new name. But I begin with the epigraph. One day, the story goes, all the dolphins left Earth moments before its destruction. According to The Hitchhiker’s Guide to the Galaxy: The last ever dolphin message was misinterpreted as a surprisingly sophisticated attempt to do a double backwards somersault through a hoop, whilst whistling the ‘Star-Spangled Banner’. But, in fact, the message was this: ‘So long and thanks for all the fish’. —Douglas Adams

In this chapter, I explore what the fossil record reveals about cortical evolution in nonprimate mammals, including dolphins. No one knows, for sure, why dolphins have such a large cortex, but it could have something to do with finding the fish that the Hitchhiker dolphins appreciated so much. Pride of place Among the traits that characterize primates, a big brain takes pride of place for neuroscientists, as does a large expanse of cortex. But primates aren’t the only mammals with a capacious cortex. The cortical expansion that also occurred in other mammals is an impressive example of homoplasy, as a recent comprehensive phylogenetic analysis confirms.1 In previous chapters, I’ve mentioned my impression that neuroscientists tend to downplay convergent evolution and other forms of homoplasy. When a structure or function of interest occurs in two distantly related species, the authors conclude (or, at least, imply) that those traits descend from a common ancestor. The authors note, in passing, that convergent evolution might be an alternative account, but they dismiss it as an exotic, unlikely prospect. The life and ecological conditions of the last common ancestor receive scarcely a mention, if any at all. The fossil record reveals a pattern of evidence completely contrary to that intuition. It shows that the evolution of a large cortex has occurred many times during mammalian evolution, independently. (The opposite trend, cortical contraction, has also happened repeatedly.) The neocortex almost always makes the largest contribution to brain enlargement, but sometimes it doesn’t.

The origin of mammals

Figure 10.1 EQ values in fossil and modern mammals. (A) Evolutionary tree of mammals and their closest relatives. (B) Encephalization quotient (EQ) values for cynodonts and mammals. The gray shading shows the EQ range for the Paleocene eutherians (magenta bars) in the figure. The box-and-whisker plot at the bottom shows the total EQ range (bars) and interquartile range (box) for tenrecs. Colors match in Parts A and B. Adapted from T.B. Rowe, T.E. Macrini, and Z.X. Luo, Fossil evidence on origin of the mammalian brain, Science 332, 955–7, 2011. Synapsid synapomorphies in Part A come from S. Brusatte, The Rise and Reign of Mammals: A New History from the Shadow of the Dinosaurs to Us, Mariner Books, New York, 2022. Tenrec data from T. Stankowich and C. Stensrud, Small but spiny: the evolution of antipredator defenses in Madagascar tenrecs, Journal of Mammalogy 100, 13–20, 2019.

Hot blood on cool nights Mammals emerged from cynodont ancestors ~205 Ma, on the synapsid branch of the amniote evolutionary tree (Figure 10.1A). Cynodonts, in turn, had evolved from therapsids. Early mammals and their ancestors evolved a high-energy life, beginning ~250 Ma with early therapsids.2 At first, the selective factors for a high-energy life probably involved enhancements in aerobic metabolism,3 most likely during the Triassic (~252–201 Ma).2,4 An increased aerobic capacity

improved stamina in pursuit of prey, extended an animal’s foraging range, and promoted escape from predators. This evolutionary trend eventually led to increases in oxygen consumption that sufficed to maintain body temperature within a consistent, elevated range, a trait called endothermy or homeothermy.3 Therapod dinosaurs,2 a clade that includes the ancestor of birds, evolved the same capacity independently: one among many homoplasies in birds and mammals (Box 10.1). Box 10.1 A host of homoplasies Birds and mammals have many homoplasies in addition to homeothermy, including three-gene (routine) trichromacy (birds sometimes have four or more dimensions of color vision); forward-facing eyes with a fovea (birds sometimes more than one fovea); enlarged orbits; heat-conserving behaviors; relatively small olfactory bulbs; social pair-bonds; vocalizations with syllables; tool use; and a four-chambered heart. Some birds remember where and when they cached specific food items, which has been mistakenly equated with the episodic memories that humans have. In addition, humans and some birds can repeat a heard word, a fact that’s been promoted (relentlessly) as somehow related to speech and language, which it isn’t. Because of homeothermy, mammals consume much more energy than vertebrates that don’t thermoregulate, collectively called poikilotherms. During locomotion, poikilotherms rely mostly on anaerobic metabolism and become exhausted after 2– 5 minutes of moderate activity. Accordingly, they rely on brief bursts of high-velocity movement, with much lower sustained rates. Lizards, for example, have burst speeds 10–30 times higher than their sustainable rates of movement.3 In mammals, peak and sustained speeds are more similar. When mammals move at high rate, they use aerobic metabolism to continue such movements. Accordingly, homeothermy enables mammals to reach sustainable speeds 6–7 times higher than poikilotherms of comparable size.3 Evolutionary changes in muscle types—from fast-fatigable fibers to fast, fatigue-resistant ones— provide additional support for speedy, long-duration locomotion. Basal (resting) metabolic rates also changed dramatically during mammalian evolution. In modern reptiles, basal metabolic rates are ~10–20% of the mammalian mean, corrected for body size and ambient temperature.3 This means that early mammals needed 5–10 times more energy at rest than their nonmammalian ancestors of similar body size. Because the neocortex consumes a lot of energy, homeothermy was an important exaptation for its emergence and expansion in Mesozoic mammals. Homeothermy also enabled early mammals and their ancestors to forage at night when cool conditions made it difficult for poikilotherms to move efficiently or for prolonged periods. Before the end-Cretaceous extinction, few mammals foraged in daylight or with either crepuscular (dawn and dusk) or cathemeral (mixed diurnal–nocturnal) patterns.5 The ability to forage nocturnally gave mammals a survival edge during their long co-existence with dinosaurs and other predatory reptiles (~205– 66 Ma). Homeothermy also had advantages during the prolonged “winter” that followed the asteroid impact of ~66 Ma, when particulate matter filled the atmosphere and caused several years of global cooling. Chapter 4 mentioned parenthetically that the term Cenozoic means “new animals.” However, the animals that survived the calamity of ~66 Ma were a selection of the species that had populated the Mesozoic, and mammals were among them. The origin of fur coats In accord with their high-energy life, many mammalian synapomorphies involve the extraction, distribution, or conservation of energy. Molars and a new jaw hinge (the dentary–squamosal joint) help mammals break down foods for efficient digestion;4 a four-chambered heart and small erythrocytes improve oxygen delivery to cells; and hair provides insulation to conserve body heat. Hair had evolved by the Jurassic, ~160 Ma, as indicated by Castorocauda, an extinct mammal with a dense coat of fur that covered most of its body.6 And, as reflected in the name “mammal,” mammary glands provide a way to transfer energy from mothers to newborns: via milk, an innovative food for toothless progeny.4 Earlier evolutionary developments also helped our mammalian ancestors conserve heat and consume large quantities of oxygen and food. As illustrated in Figure 10.1A, cynodonts evolved turbinal bones in the nose, which warm and moisten inhaled air4 (along with improving olfaction).7 They also developed a secondary, hard palate, which serves as a partition between pathways for respiration and food consumption, so breathing and eating can occur simultaneously. (That’s the reason you can breathe through both your nose and your mouth.) In addition, cynodonts developed an upright quadrupedal posture, which improves locomotion and lung capacity. A restructuring of the jaw musculature enhances both bite power and food processing.4 Additional synapomorphies reflect the nocturnal foraging life of ancestral mammals, including a decreased reliance on vision and an enhanced role of audition, olfaction, and somatic sensation (see Chapter 5, “The mammalian foundation”). Improvements in hearing resulted from several innovations: middle-ear ossicles, which amplify high-frequency sounds; improvements in inner-ear architecture; and external ears (pinnae), which focus sound energy. Bird brains In addition, of course, there’s a particularly significant mammalian synapomorphy that’s the topic of this book: the neocortex. There’s been some confusion in the literature about whether other vertebrates, especially birds, “have a neocortex.” They don’t. This fact raises a simple question: If birds don’t have neocortex, what do they have? The answer is that both birds and mammals have their own kind of pallium, a telencephalic structure inherited from a common ancestor. The last common ancestor of birds and mammals was an amniote that lived ~325 Ma (Figure 10.1A). Birds and modern reptiles derive from one branch of the amniote tree (diapsids, sometimes called sauropsids); mammals descend from the other (synapsids). These two groups have been evolving independently ever since (Box 10.2), and the pallium has changed dramatically in both. Because they descended from a common ancestor, the pallia of birds and mammals draw on similar developmental mechanisms, albeit modified considerably during hundreds of millions of years of evolution. For readers

interested in this point, there are similarities between birds and mammals in pallial projections to other brain structures,8 physiological properties of visual neurons,9,10 and laminar-specific gene expression.11,12 And because the neocortex is a derivative of the pallium, it has similarities with the pallium in other vertebrates, in addition to birds. Studies of lamprey provide the best examples.13,14 These are fascinating observations, but they don’t mean that birds or other vertebrates have a neocortex. Instead, such findings indicate that some of the cell types in both the avian pallium and the mammalian neocortex have descended—in modified form—from cell types present in their last common ancestor. The neocortex is unique to mammals, but it didn’t evolve de novo. It derived from the pallium, which is common to all vertebrates.* Box 10.2 Hundreds of millions of years in perspective Timescales on the order of hundreds of millions of years can be difficult to fathom. The main text says that the ancestors of birds and mammals diverged ~325 Ma, but what does that really mean? To answer this question, it might help to consider what happened during the 325 million years before that, which would take us back to near the time that the first animals evolved. Fossils dated to a period called the Ediacaran (~635–539 Ma) include sponge-like animals and others so foreign to biologists that they defy classification. Some experts have even cast doubt on whether these fossils reflect the external shape of animals, as previously thought, or instead reflect an internal support structure that reveals little about an animal’s outward appearance.15 So, for approximately the same amount of time that it took for these mysterious, unclassifiable animals to give way to land animals (amniotes) that any kindergartner could recognize as a vertebrate, the ancestors of modern birds and mammals have been evolving separately. In this context, it should come as little surprise that the pallium of birds bears so little resemblance to that of mammals. Brain expansion Mesozoic mammals, modern monotremes, marsupials The previous section mentioned cynodonts but didn’t explain what they were, which was an extinct paraphyletic group of therapsids that included the direct ancestors of mammals.6 During their evolution, many cynodonts became small animals, which altered their diet and metabolism.4 Figure 10.1B presents cynodont encephalization quotients (EQs) relative to modern mammals. The plot shows that cynodonts had EQs well below the modern mammalian mean. An important fossil specimen, Hadrocodium, is dated to ~196–190 Ma. It was a mammal, as indicated by its middle-ear structure, and it had an EQ that was much larger than cynodonts, within the range of eutherians that lived during the Paleocene, which began more than 120 million years later. Figure 10.1B, adapted from Rowe et al.,6 presents the relevant data, including the EQ range of some Paleocene eutherians (gray shading). It also shows that the nonmammalian mammaliaform, Morganucodon, had an intermediate EQ: less than Hadrocodium but more than cynodonts. Unfortunately, the virtual endocast of Hadrocodium doesn’t have a visible rhinal sulcus, which rarely appears on endocasts of small mammals. Nevertheless, based on its endocranial capacity, Rowe et al. surmised that it had a neocortex, which contributed to brain enlargement. The EQ of Hadrocodium comes with a cautionary note, however. Based on the size of its skull, it weighed 2 grams or less, which makes it one of the smallest mammals ever (Box 2.1). Sometimes, selection for a small body size inflates EQ values, for reasons explained in Chapter 7 (“Measures and misconceptions”). Modern monotremes have a larger EQ than Hadrocodium, but most of these species have endocranial volumes within the range of Paleocene eutherians or just above it. Monotremes and therian mammals diverged ~166 Ma,16 and monotremes are rare today. Only a few species of platypuses and echidnas survive, and they have EQ values below the modern mammalian mean. Modern marsupials descended from an ancestral species that lived ~130 Ma and invaded Australia via land bridges connecting South America, Antarctica, and Australia. The founding Australian marsupial was closely related to a modern South American species, Dromiciops gliroides, known to locals as the monito del monte, which means “little monkey of the mountains” (Box 6.3). Most modern marsupials, like monotremes, have EQ values below the mammalian mean, but monito del monte is an exception (Figure 10.1B, Box 10.3). Box 10.3 A marsupial “monkey” The monito del monte, introduced in Box 6.3, serves as an instructive example of convergent evolution. These small (15– 50 gram) arboreal marsupials compete with primates in South America and exploit some of the same niches as small platyrrhines. Accordingly, they evolved several homoplasies with primates. Their name alone shows that South Americans have noticed the resemblance between these arboreal marsupials and small New World monkeys. The last common ancestor of modern platyrrhines and monito del monte lived ~160–148 Ma (Figure 10.1A),16 so they are very distantly related. Nevertheless, Figure 10.1B shows that monito del monte shares an important feature that characterizes platyrrhines: relatively large brains. They have EQ values greater than 1.5, which falls within the modern platyrrhine range. Marsupial and monotreme brains are intriguing, but they don’t resemble those of early mammals as closely as once hoped. Modern species in these groups have a host of specializations—of both body and brain—and marsupials have radiated into many Australian niches occupied elsewhere by eutherians. Kangaroos, for example, exploit some of habitats that deer occupy outside Australia; wombats resemble pigs in certain ways; and there are, or were, marsupial “lions,” “wolves,” “moles,” “bears,” “anteaters,” “groundhogs,” “flying squirrels,” and “tigers” of both the standard and saber-tooth varieties.17 So, crown marsupials and monotremes probably don’t resemble ancestral mammals very closely. Nevertheless, it’s been possible

to reconstruct the cortex of early mammals by comparing several extant mammals, including the more generalized marsupials, the duckbilled platypus (a monotreme), and some primitive eutherians such as tenrecs. Most early mammals probably had a brain in the size range illustrated for Paleocene eutherians in Figure 10.1B, much like modern tenrecs.18 The next four sections discuss cortical evolution in selected eutherian groups, mostly based on data from cranial endocasts. A whale of a brain Figure 10.2 illustrates the time course of brain expansion in two major groups of cetaceans: toothed whales (Odontoceti) and oceanic dolphins (Delphinoidea). Toothed whales diverged from baleen whales (Mysticeti) ~34 Ma, and oceanic dolphins split from other toothed whales ~16 Ma.19 Stem cetaceans (Archaeoceti) date to the early Eocene, ~55 Ma, with the most recent specimens coming from late Eocene formations ~38 million years old.20,21 As Figure 10.2B illustrates, cetaceans are either the sister group of artiodactyls or embedded within the cetartiodactyl clade as the sister group of hippopotamuses. A relatively recent analysis supports the latter option.22 Figure 10.2C presents a chronogram on the same timescale as Figure 10.2A.19 Stem cetaceans (Archaeoceti) had brains with EQ values below 0.6, averaging ~0.4 or less (Figure 10.2A), which is not unusual for the ungulates that they were. During subsequent evolution, the brains of toothed whales probably became larger during the late Eocene. Much later, in oceanic dolphins, cetacean brains underwent another pulse of expansion during the middle Miocene, ~14 Ma.23

Figure 10.2 Cortical grade-shifts in cetaceans. (A) Encephalization quotient (EQ) values for cetaceans. Ancient whales (Archaeoceti) are a paraphyletic group of stem cetaceans, which resembled artiodactyls (even-toed ungulates). Toothed whales (Odontoceti) include oceanic dolphins (Delphinoidea). (B) Two possibilities for phylogenetic relationships among cetaceans and artiodactyls. (C) A chronogram of cetaceans. Abbreviations: Hippos, hippopotamus species; Ma, million years ago; O., Orcinus; P-P, PlioPleistocene. (A) Adapted from L. Marino, D.W. McShea, and M.D. Uhen, Origin and evolution of large brains in toothed whales, Anatomical Record A: Discoveries in Molecular, Cellular, and Evolutionary Biology 281 1247–55, 2004. (C) Adapted from Z. Chen, S. Xu, K. Zhou, and G. Yang, Whale phylogeny and rapid radiation events revealed using novel retroposed elements and their flanking sequences, BMC Evolutionary Biology 11, 314, 2011.

These pulses of brain expansion match developments in primates reasonably closely. Chapter 7 reviewed evidence that primate brains probably underwent a grade-shift into the modern prosimian range during the late Eocene, at about the same time as the cortex enlarged in toothed whales. Chapter 8 explained that cercopithecoid and platyrrhine brains probably expanded during the middle-to-late Miocene (after ~16–15 Ma), probably near the time that a similar upward grade-shift occurred in the cortex of oceanic dolphins. Modern cetaceans match the encephalization and corticalization levels observed in hominoids, including humans. As in apes and humans, the neocortex takes up ~75–80% of cetacean brains (Figure 7.4B). Likewise, cetacean EQ values roughly match those of hominoids,24 although the specialized body architecture of cetaceans makes such comparisons less meaningful than in other cases. All the same, cetaceans have an extraordinarily expansive cortex by any measure. It’s worth asking whether something common to the ecology of the late Eocene and middle-to-late Miocene contributed to cortical grade-shifts in both cetaceans and primates. Chapter 9 pointed to two episodes of climate change as indirect driving forces for cortical enlargement in primates: a global-cooling period that began during the late Eocene, ~40 Ma; and another that began during the middle Miocene, ~14 Ma. Could climate change have affected cetaceans in a way that has any correspondence with climate-related driving forces on primates? It doesn’t seem likely in view of their very different habitats. Recently, Bertrand et al.1 have explained such concurrent but independent cortical expansion in a different way. They analyzed EQ values across the mammalian orders and found that an increase in encephalization was a general property of Eocene mammals. After the end-Cretaceous extinction, the surviving eutherians radiated and filled most of the vacant ecological niches during the Paleocene. Body size tended to increase in those Paleocene eutherians, but relative brain size didn’t. Then, once eutherians saturated the available habitats, the intensified competition generated a selective pressure for larger brains during the Eocene. In mammals, an increase in EQ values almost invariably results from a larger neocortex [although a later section (“Squirrelly cortex”) discusses an exception]. Thus, according to Bertrand et al.,1 parallel Eocene grade-shifts in cortex size resulted from habitat saturation and the increased competition it caused. The cortical enlargements of the late Eocene—in both crown Euprimates and toothed whales—might reflect a continuation of that trend. The middle-tolate Miocene grade-shifts in both monkeys and dolphins require a different explanation. Like toothed whales more generally, dolphins rely on echolocation to detect prey and for navigation, so it’s possible that augmented cortical representations of reflected sounds could account for the cortical expansion in dolphins, or at least some of it. Cortex on the hoof Figure 10.3 shows the mean EQ values for fossil and modern artiodactyls. Also known as even-toed ungulates, this order includes pigs, sheep, camels, cows, hippopotamuses, and deer, among other taxa. Together with cetaceans, they compose the cetartiodactyl clade (Figure 3.3).

Figure 10.3 Cortical grade-shifts in artiodactyls. Box plots show the interquartile range (boxes), means (thick green lines), and ranges (green bars) of encephalization quotient (EQ) values for artiodactyls. Diacodexis was a ground dwelling herbivorous artiodactyl of the Eocene, which lived ~50–46 Ma and was probably related to cetaceans. Adapted from M.J. Orliac and E. Gilissen, Virtual endocranial cast of earliest Eocene Diacodexis (Artiodactyla, Mammalia) and morphological diversity of early artiodactyl brains, Proceedings of the Royal Society of London B: Biological Sciences 279, 3670–7, 2012.(c) Royal Society (Great Britain)

Brain endocasts from fossil artiodactyls show that they retained a small cortex long after the cortex of cetaceans and primates expanded. Two examples are instructive. First, during the Eocene, a large-bodied North American genus of ungulates called Coryphodon had the smallest EQ ever recorded for a placental mammal.7 Second, an extinct group of ungulates called notoungulates retained a consistently small relative brain size from the Oligocene until their extinction

during either the Pliocene or the Pleistocene.25 Throughout the Miocene, the mean and range of artiodactyl EQ values remained little changed from their small-cortex ancestors of the Eocene and Oligocene. Then, sometime during the PlioPleistocene, artiodactyl cortex expanded, producing the large sheep, pig, and cow brains of today, among many others. This timing means that ungulate brains increased in relative size roughly in parallel with the cortical expansion in hominins (Figure 8.6). Fossil endocasts also reveal aspects of sulcal evolution in ungulates. Complex sulcal patterns evolved in artiodactyls during the Oligocene, and these developments occurred convergently in several lineages.26 The relatively early gyrification with a later expansion resembles what happened in cercopithecoid evolution. Chapter 8 noted that a modern cercopithecoid pattern of sulci was well developed in the frontal lobe of Victoriapithecus, ~15 Ma, with frontal-lobe expansion occurring later.27 Cats go their own way Figure 10.4 presents a summary of data from Finarelli and Flynn for carnivores.28 Like the work of DeCasien et al.5 in primates, they tested the social-brain hypothesis championed by Dunbar and his colleagues.29,30,31 It’s not illustrated in the figure, but Finarelli and Flynn found that previous analyses, which had seemed to support a significant correlation between social complexity and relative brain size in carnivores, had been dominated by members of the dog family. When they increased the sample to include a broader selection of carnivore lineages, including fossil carnivores, they found no relationship between EQ values and social complexity.

Figure 10.4 Cortical grade-shifts in carnivores. The red and green rectangles indicate independent upward grade-shifts in relative brain size in five carnivore lineages. Orange lines represent the primitive brain–body size allometry between brain size and body size; purple lines indicate the archaic musteloid allometry. Another grade-shift occurred in an extinct linage of carnivores commonly known as bear dogs (Amphicyonidae). Most (but not all) of the illustrated upward grade-shifts involved a change in the y-intercept of regressions between brain and body size. Four parallel shifts occurred during carnivore evolution (red arrows). Changes in slope were important in felids (green arrows), in the ancestors of modern musteloids (purple arrow), and in amphicyonids (not illustrated). Adapted from J.A. Finarelli and J.J. Flynn, Brain-size evolution and sociality in Carnivora, Proceedings of the National Academy of Sciences USA 106, 9345–9, 2009.

Figure 10.4 illustrates evolutionary changes in the relationship between brain and body size in carnivores. For crown canids (dogs), the upward grade-shift occurred near the end of the Miocene, as it did for crown musteloids (skunks, weasels, and racoons, among other carnivores). The grade-shift in ursids (bears) might have begun a little earlier.32,33 The relative brain expansion followed an increase in body size, as seems to have happened in platyrrhines and cercopithecoids of the

Miocene (Chapter 8) and in many eutherian lineages during the Eocene.1 In four carnivore lineages—dogs, bears, and two groups of mustelids—the y-intercept of the brain–body relationship increased, but the slope remained more-or-less constant. Other forms of encephalization occurred in different carnivore lineages. Felids (cats), as usual, did things differently (Figure 10.4). Small-bodied felids underwent an increase in relative brain size, thus decreasing the slope of the brain–body regression. Recently, Lynch and Allen34 examined several correlates of large brain size in modern carnivores. Two musteloid groups— mustelids (weasels, wolverines, badgers, otters, ferrets, and minks) and procyonids (racoons and their relatives)—showed a correlation between high-quality diets and relative brain size. Another group, mongooses, showed a correlation between brain size and the complexity of their habitats. Chapters 15 and 16 discuss similar correlations in primates. Squirrelly cortex The cortex has expanded much less in rodents than in primates, cetartiodactyls, and carnivores. Figure 7.5A illustrates the relationship between EQ and longevity in rodents. Several rodents, including rats and mice, have corticalization values in or near the 20–30% range common to many modern mammals. However, modern squirrels exceed that value, possibly because of the demands of diurnal, arboreal foraging. Figure 10.5A shows that many early mammals, labeled “archaic” in the figure, had EQ values within the range of plesiadapiform primates, as indicated by yellow shading. Chapter 6 explained that plesiadapiforms lived mostly during the Paleocene. Paramys was among the oldest fossil rodents, dating to the early Eocene or very late Paleocene;35 Ischyromys was a rodent that lived ~34–31 Ma, during the early Oligocene. These fossil rodents had EQ values higher than both “archaic” mammals and plesiadapiforms, and similar developments occurred in other mammalian lineages.1

Figure 10.5 Cortical grade-shifts in rodents. (A) Encephalization quotient (EQ) values for rodents and other selected mammals. (B) Corticalization values. Archaic mammals range from the late Cretaceous though the late Eocene (~100–34 Ma). Circles plot values for outliers. The outlier among modern squirrels is the American red squirrel (Tamiasciurus hudsonicus). The selection of Euprimates in Part B comes from anthropoids. Oreopithecus was a hominoid dated to ~9–6.5 Ma; Chapter 8 discussed Proconsul. The yellow shading shows the plesiadapiform range for comparison with the other data. Adapted from O. C. Bertrand, F. Amador-Mughal, and M. T. Silcox, Virtual endocast of the early Oligocene Cedromus wilsoni (Cedromurinae) and brain evolution in squirrels, Journal of Anatomy 230, 128–51, 2017 and O.C. Bertrand, F. Amador-Mughal, and M.T. Silcox, Virtual endocasts of Eocene Paramys (Paramyinae): oldest endocranial record for Rodentia and early brain evolution in Euarchontoglires, Proceedings of the Royal Society of London, Series B: Biological Sciences 283, 20152316, 2016.(c) Royal Society (Great Britain)

Cedromus was a rodent that lived throughout the Oligocene, and Figure 10.5B shows that its brain had a higher percentage of neocortex than did plesiadapiform brains. Although Paramys had a higher EQ than plesiadapiforms (Figure 10.5A), it had a lower percentage of neocortex (Figure 10.5B).36,37 The finding that EQ increased in Paramys without a concomitant increase in neocortical extent36 contradicts the impression that encephalization and corticalization are invariably coupled. In most mammals, an increase in relative brain size depends on the neocortex, but not in Paramys. Chapter summary This chapter brings Part III to a close. In it, Chapters 7 and 8 emphasized the idea that the neocortex expanded several times during primate evolution, independently in its major lineages (Figure 8.12). Chapter 9 discussed the timing of cortical gradeshifts in relation to global climate change, adaptive radiations, and the evolution of characteristic skeletal traits. This chapter summarized parallel and convergent evolution in other mammals, with emphasis on groups with a well-studied fossil record.* The fossil record for cetaceans, artiodactyls, carnivores, and rodents reveals many independent instances of cortical expansion, often several times in each order. Cortical enlargement in cetaceans most closely resembled the timing of upward

grade-shifts in primates; other mammals developed a relatively large cortex more recently. Overall, the impression garnered from the paleoneurology literature is this: Convergent evolution is the rule, not the exception. Parietofrontia The Preface noted that most mammalian orders have names that highlight a distinctive feature, such as “hand-wing” (Chiroptera) for bats. In contrast, the word “primate” derives from the Latin for first, which implies some sort of supremacy. Contemporary biology rejects primacy, so perhaps we should consider a different name for our order. No one will ever call primates by any other name, but what if we could choose something else? For neuroscientists, the most obvious trait of primates is a large neocortex. However, other mammals have also evolved a gargantuan neocortex, in both relative and absolute terms. Accordingly, gigantonoodyla is unacceptable—for many reasons. A new name could refer to forward-facing eyes, but carnivores have evolved that form of convergence convergently. So, frontoptera is out, too. Hindlimb-dominated locomotion evolved in kangaroos and kangaroo rats, among other mammals, which rules out kinetolegia or bipedalia. Another possibility involves fingernails. But unguiculata, from the Latin for fingernail, is already taken for a polyphyletic group of mammals with hooves. There is, however, something else that distinguishes primates from other mammals, which suggests another name for our order: parietofrontia. Part IV explains why. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37.

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Relationships of Cetacea (Artiodactyla) among mammals: increased taxon sampling alters interpretations of key fossils and character evolution. Public Library of Science One 4, e7062 (2009). Marino, L., McShea, D.W., & Uhen, M.D. Origin and evolution of large brains in toothed whales. Anatomical Record A: Discoveries in Molecular, Cellular, and Evolutionary Biology 281, 1247–55 (2004). Wright, A., Scadeng, M., Stec, D., Dubowitz, R., Ridgway, S., & Leger, J.S. Neuroanatomy of the killer whale (Orcinus orca): a magnetic resonance imaging investigation of structure with insights on function and evolution. Brain Structure and Function 222, 417–36 (2017). Fernández-Monescillo, M., Antoine, P.-O., Pujos, F., Gomes Rodrigues, H., Mamani Quispe, B., & Orliac, M. Virtual endocast morphology of Mesotheriidae (Mammalia, Notoungulata, Typotheria): new insights and implications on notoungulate encephalization and brain evolution. Journal of Mammalian Evolution 26, 85–100 (2019). Orliac, M.J. & Gilissen, E. Virtual endocranial cast of earliest Eocene Diacodexis (Artiodactyla, Mammalia) and morphological diversity of early artiodactyl brains. Proceedings in Biological Science 279, 3670–7 (2012). Gonzales, L.A., Benefit, B.R., McCrossin, M.L., & Spoor, F. Cerebral complexity preceded enlarged brain size and reduced olfactory bulbs in Old World monkeys. Nature Communications 6, 7580 (2015). Finarelli, J.A. & Flynn, J.J. Brain-size evolution and sociality in Carnivora. Proceedings of the National Academy of Science USA 106, 9345–9 (2009). Dunbar, R.I.M. The social brain hypothesis and its implications for social evolution. Annals of Human Biology 36, 562–72 (2009). Dunbar, R.I.M. Neocortex size as a constraint on group size in primates. Journal of Human Evolution 20, 469–93 (1992). Dunbar, R.I.M. & Shultz, S. Understanding primate brain evolution. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 362, 649–58 (2007). Finarelli, J.A. & Flynn, J.J. The evolution of encephalization in caniform carnivorans. Evolution 61, 1758–72 (2007). Finarelli, J.A. Testing hypotheses of the evolution of encephalization in the Canidae (Carnivora, Mammalia). Paleobiology 34, 35–45 (2008). Lynch, L.M. & Allen, K.L. Relative brain volume of carnivorans has evolved in correlation with environmental and dietary variables differentially among clades. Brain, Behavior and Evolution 97, 284–97 (2022). Anderson, D. Ischyromyidae. In: Evolution of Tertiary Mammals of North America, Small Mammals, Xenarthrans, and Marine Mammals (ed. C.M. Janis, G.F. Gunnell, & M.D. Uhen) 311–25 (Cambridge University Press, Cambridge, 2008). Bertrand, O.C., Amador-Mughal, F., & Silcox, M.T. Virtual endocasts of Eocene Paramys (Paramyinae): oldest endocranial record for Rodentia and early brain evolution in Euarchontoglires. Proceedings in Biological Science 283, 20152316, doi: 10.1098/rspb.2015.2316 (2016). 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* It’s also useful to distinguish between traits and trait states. For example, pectoral appendages are a homologous trait in gnathostomes. This trait has various character states: forelimbs in quadrupeds; wings in birds; flippers in cetaceans; and pectoral fins in jawed fishes, for example. The claim that birds have a neocortex is analogous to

saying that tuna (and other neoteleosts) have wings or forelimbs. * Among other large-cortex mammals, Afrotherians such as elephants and perissodactyls (horses and their relatives) have been less studied by paleoneurologists (or perhaps I have missed the relevant reports).

PART IV WHAT PRIMATE CORTEX IS

11 Cortical comparisons Overview Fossils speak to the timing of cortical expansion but reveal nearly nothing about the evolution of cortical maps. To gain such knowledge, a comparison of maps from an extensive selection of mammals would be ideal. For the time being, however, the field relies on data from a few, well-studied species. Tree shrews and galagos, because of their relationships to other Euarchontans, are especially important for understanding cortical evolution in primates. It’s also important to dispel some misconceptions. For instance, neuroscientists sometimes treat broad regions of cortex, such as the prefrontal cortex or the posterior parietal cortex, as a single thing. Consequently, small and relatively simple cortical regions in rodents are wrongly equated with large, complex suites of areas in primates. Other discredited ideas include replica-inminiature-, amalgam-, triune-brain-, and dual-origin theories of cortical evolution. However, the ring-and-core structure of the latter is a valid and valuable view of cortical organization. In my younger and more vulnerable years my father gave me some advice that I’ve been turning over in my mind ever since. ‘Whenever you feel like criticizing any one,’ he told me, ‘just remember that all the people in this world haven’t had the advantages that you’ve had.’ —F. Scott Fitzgerald, The Great Gatsby (Scribner, New York, 1925)

Introduction In this chapter, tree shrews find their tree; metaphors foul the literature; and the Declaration of Independence gets a makeover. But I begin with the epigraph. Elsewhere, I quoted the last lines of The Great Gatsby; here I quote the first ones. They’re apt because this chapter contains a lot of criticism, some of it directed toward ideas about cortical evolution that well-established neuroscientists have championed for decades. In considering their misconceptions, it’s worth bearing in mind that—like Fitzgerald’s narrator—they haven’t had the advantages that you, a reader of this book, now have. Specifically, they didn’t have the advantage of knowing—with confidence—that tree shrews are the neuroanatomically studied mammals most closely related to primates; of knowing the cortical maps of the duckbill platypus and tenrecs; of knowing how prevalent homoplasy has been during the evolution of primates and other mammals; of knowing when and in which kind of primates the cortex expanded; or of knowing how the frontal lobes of modern monkeys differ in size from those of ancestral anthropoids. Without these advantages, well-regarded neuroscientists have circulated discredited ideas about cortical evolution and continue to do so. This chapter dispels several of these misconceptions. One equates small areas in the cortex of rodents with large, complex suites of areas in primates (“To have and have not”). Two others hold that cortical areas in rodents have the combined properties of several areas in primates, or that rodents have all the areas that primates have, but in miniature form. The section entitled “Mixtures and miniatures” deals with these ideas. “Lamentation of the lizard” addresses an idea of astounding popularity outside the field of neuroscience but nearly none within it: the triune-brain theory. And the last one is a long-discredited (but often-cited) doctrine about dual origins and evolutionary trends (“Rings for right and wrong reasons”). A Declaration of Independence To secure the lessons of evolution to ourselves and our posterity, I propose a new Declaration of Independence, one tailored to contemporary neuroscience: We hold these truths to be self-evident: that any meaningful comparison among species requires an understanding of their evolutionary relationships; that it’s useful to consider the lives led by ancestral species, not just those of modern ones; that each living species, including humans, has an equally long evolutionary history; that no modern species serves as a precursor to any other modern species; that it’s impossible to infer anything specific about the evolution of the human brain by comparing macaque monkeys and humans; that the last common ancestor of macaques and humans didn’t resemble modern macaques as closely as usually assumed (marmosets even less so); and, most importantly, that convergent and parallel evolution are much more common than most neuroscientists believe.

Like the truths that Thomas Jefferson penned in the original, these principles are far from universally accepted. They should be. I doubt that any of them would seem remotely controversial to evolutionary biologists, who might wonder why I bother to state them at all. Unfortunately, the neuroscience literature contains countless violations of every clause in the new Declaration. I call it a Declaration of Independence because neglect of independent evolution often obscures the selective factors that have yielded an evolutionary innovation, and it does so for a simple reason. Every incorrect assumption about a trait being conserved, when it evolved independently, situates the time of an evolutionary development too distantly in the past and usually in different ecological circumstances. This chapter begins Part IV, which addresses a specific aspect of comparative neuroanatomy: cortical maps. Most of the relevant literature compares the human cortex with that of four animal models that neuroscientists favor for biomedical research: macaques, marmosets, mice, and rats. (The new Declaration mentions the two primates in this group.) These animals suffice for most neuroscience research, but comparative neuroanatomy benefits from knowledge about a wider variety of species. Specifically, two taxa that have a closer relationship with humans than rodents do—tree shrews and galagos—lack the fame of the favored four, but what they reveal about cortical evolution more than compensates for their poor PR. Chapters 12 and 13 summarize these revelations; this chapter provides background material and dispels some misconceptions.

Flying primates, feathered apes For some reason, when a nonprimate species shares a primate trait via convergent evolution, it’s fashionable to call that species a “primate.” In the 1980s, for example, when neuroanatomists observed primate-like retinotectal projections in bats,1 they touted these nocturnal insectivores as “flying primates.” At the time, the idea that bats, tree shrews, colugos, and primates might form a natural taxon seemed plausible (Figures 3.1A and 3.2), but molecular phylogenies demonstrated otherwise by the turn of the century. Even so, some neuroscientists persisted in treating bats as the sister group of primates until as recently as 2008,2 by which time it was clear that the traits in question had evolved convergently. In the same vein, when researchers discovered that crows and jays (corvids) are, like primates, capable of one-trial learning about events (called episodic memory in humans) and can recognize faces, the metaphors “avian primates” and “feathered apes” fouled (and fowled) the literature for a while. The champions of every species, it seems, want their favorite research subjects to have the exalted status that the scala naturae confers on primates. (Or maybe it has something to do with the exalted status that funding agencies confer on primates.) To some, an emphasis on primates smacks of chauvinism. Why shouldn’t corvids and bats receive as much attention as primates? One good answer, I think, draws an analogy with genealogy. It would seem odd if someone investigated a randomly selected person’s ancestry, but no one bats an eye when they try to learn about their own great great grandparents. Similarly, many neuroscientists choose to focus on primates, rather than to eye bats or corvids in the same way. We are neither bats nor birds, so it’s perfectly natural to study what we are: primates. Crucial comparisons Greater galagos Understanding cortical evolution in primates depends on appreciating the diversity among primates and other Euarchontoglires. The neuroscience literature is replete with primate–nonprimate comparisons, but primates are too diverse for such a simple approach, and the nonprimates used for comparison need careful consideration. Figure 11.1 illustrates the brains of selected Euarchontoglires, scaled to a fixed distance between the frontal and occipital poles. By any measure, the amount of variation is impressive.

Figure 11.1 Diversity of Euarchontoglires cortex. (A) An evolutionary tree of Euarchontoglires. Red font highlights the lineages most useful for understanding cortical evolution in primates, which reflects both evolutionary relationships and practical considerations such as previous neuroscience research and availability for future research. (B) Photographs of selected Euarchontoglires brains. (B) Reproduced from the Wisconsin comparative mammalian brain collection, http://neurosciencelibrary.org/index.html, with permission.

Figure 11.1 also illustrates why galagos and tree shrews provide such pivotal information. Galagos are also known as bushbabies, and the two best-studied species are the brown greater galago (Otolemur crassicaudatus) and the northern greater galago (Otolemur garnetti). Despite the name of this genus, they are not lemurs but are instead members of the lorisiform branch of the strepsirrhine tree (Figure 3.4). As strepsirrhines, galagos serve as an outgroup for both anthropoids and tree shrews. Galago–tree shrew comparisons are useful for understanding primate specializations; galago–anthropoid comparisons tell us about anthropoid specializations. (The sister group of anthropoids, tarsiers, usually get short shrift because so little is known about their cortex.) Because of these evolutionary relationships, comparisons among tree shrews, galagos, and anthropoids reveal more about primate brain evolution than do comparisons with more distantly related mammals. That said, all comparisons contribute to understanding cortical evolution. Tree shrews find their tree Chapter 1 mentioned an impediment to understanding how primate brains differ from those of other mammals: knowing with confidence which groups of mammals are most closely related to primates. In this regard, tree shrews have historically presented a problem in classification. For most of the 20th century, they seemed to be closely related to elephant shrews and tenrecs, which are generally considered to be among the more primitive mammals, sometimes called insectivores (Insectivora) or basal insectivores in the older literature.

In contrast to taxonomies that lump tree shrews with insectivores, they have sometimes been classified as primates.3,4 By the 1970s, however, an extensive analysis of tree shrew affinities led to the opposite conclusion, which prevails today: tree shrews are not primates. Molecular phylogenies have confirmed this conclusion (Figure 3.3). Outgroups and outliers Armed with an accurate understanding of the evolutionary relationships among mammals, and especially the place of tree shrews and galagos in the Euarchontan tree, comparative neuroanatomy has much more power than in the past. However, rich phylogenetic reconstructions remain beyond the reach of cortical mapping studies for the time being. Chapter 14 presents two such analyses: Figure 14.3 for the size of the prefrontal cortex; and Figure 14.7 for the size of the hippocampus. Such studies are demanding enough, but it’s much easier to measure the size of a brain structure than to map the locations and boundaries of dozens of cortical fields. Accordingly, there aren’t enough cortical mapping data from a sufficient diversity of species to perform a formal phylogenetic analysis.* Instead, ideas about the evolution of cortical maps depend on comparisons among a few reasonably well-studied species. Earlier, I said that galagos serve as an outgroup for both anthropoids and tree shrews. What this means in practice is that cortical maps in tree shrews inform inferences about primate specializations, especially when a cortical field found in primates is absent in both tree shrews and in other outgroups of primates, such as rodents. Likewise, when an anthropoid cortical field is absent in galagos, it’s a likely anthropoid specialization. The assumptions behind these inferences are reasonable, but they have some pitfalls, which a consideration of tree shrew phylogeny illustrates. Figure 11.2 presents a chronogram of tree shrews. Among the ~20 species in this clade, only two have received meaningful attention from contemporary neuroanatomists: the northern tree shrew, also known as Belanger’s tree shrew, Tupaia belangeri; and the common tree shrew, Tupaia glis. A little work has been reported for the Chinese tree shrew, which is probably a subspecies of Tupaia belangeri.

Figure 11.2 Tree shrew phylogeny. A chronogram for tree shrews and related taxa based on uncorrected molecular-clock dates. Asterisks mark the species that neuroanatomists have studied most. Abbreviation: Ma, million years ago. Adapted from T.E. Roberts et al., Molecular phylogeny of treeshrews (Mammalia: Scandentia) and the timescale of diversification in Southeast Asia, Molecular Phylogenetics and Evolution 60, 358–72, 2011. Artwork by Mary K.L. Baldwin.

Comparative neuroanatomists treat cortical maps in common and northern tree shrews as characteristic of tree shrews as a group. That’s likely, but it’s conceivable that these closely related species5 are outliers among tree shrews. Perhaps they lack

a given area and all other tree shrews have it. Given the sparse sampling of tree shrews, it’s impossible to rule out this possibility, and similar cautions apply to galagos as an outgroup for anthropoids. A physicist or mathematician would probably stop reading at this point, but biologists learn to live with such uncertainty. As good Bayesians, it’s important to recognize that an absence of certainty doesn’t imply an absence of knowledge. Unless something about the anatomy, lifestyle, or ecology of common and northern tree shrews differs in some important way from other tree shrews, it’s reasonable to accept the assumption that their cortical maps are characteristic of tree shrews generally, and mutatis mutandis the same goes for galagos. Chapters 12 and 13 describe the emergence of new cortical areas in Euprimates and anthropoids, respectively, inferences that arise from comparisons of cortical maps, connections, and topography in modern species. These studies provide irreplaceable insights, but several misconceptions need to be dispelled in order to reap their benefits, and the remainder of this chapter addresses them. Misconceptions: minor and massive To have and have not A persistent problem in comparative neuroanatomy is what I call the “have it” fallacy. Neuroscientists sometimes pose questions such as: Do rats have a posterior parietal cortex; a ventral visual stream; a prefrontal cortex? Then, when similarities come to light between a minuscule patch of cortex in a rodent and a large, complex suite of cortical fields in anthropoids, champions of rodent research proclaim that their favorite research animals “have it”: a posterior parietal cortex, for example. At one level, the conclusion that rodents “have it” is legitimate, and the posterior parietal cortex serves as a useful case-inpoint. There’s a small cortical area in rats and mice between the primary somatosensory cortex (S1) and the second visual area (V2).6 Neuroanatomists have placed several labels on this area, including PP and PM (not to be confused with the premotor cortex, which shares this abbreviation). This region of cortex has connections with the medial posterior nucleus of the thalamus, which is thought to be homologous with a rostral, somatosensory part of the pulvinar in primates.7 The pulvinar provides the main thalamic input to the posterior parietal cortex in primates, so this inferred homology supports the idea that rodents “have it”: a posterior parietal cortex, in this case. Is this small area in rodents homologous with part of the posterior parietal cortex of primates? It could be, and evidence for attentional impairments (hemineglect) after inactivation or lesions of it provides support for this idea.8 Krubitzer et al.9 (p. 81) concluded that PM “may be a primitive homolog to portions of posterior parietal cortex . . . described in other mammals such as primates . . . ” On the other hand, Remple et al.10 (p. 145) decided that the “relative location, topography, and spinal cord connections of PM are more similar to the SC [caudal somatosensory] region of tree shrews,” which, in turn, is homologous with area 1 and/or area 2 of anthropoids. These areas are part of the somatosensory cortex (the anterior parietal cortex), not the posterior parietal cortex. In support of this conclusion, area PM of rats has its orofacial representation lateral to its forelimb representation, in parallel with somatotopy in the primary somatosensory cortex (S1), just as areas 1 and 2 do in anthropoid and prosimian primates. Kaas has reviewed this topic in detail,6 and a succinct summary comes from O’Conner et al. (p. 9):11 The difference in size, number of cortical fields, and the proposed function of [the posterior parietal cortex] in mice and rats versus macaque monkeys (and primates in general) is notable . . . [and it] is not clear if any of the cortex termed [posterior parietal cortex] in mice and rats is homologous or even analogous to [the posterior parietal cortex] in monkeys.

Given these findings and conclusions, the idea that rats and mice “have it”—meaning that they have a posterior parietal cortex that’s homologous, in part, with the like-named cortical region of anthropoids—is plausible in certain ways, but it’s also a misleading oversimplification. Figure 11.3 illustrates the posterior parietal cortex of mice and macaque monkeys. Whatever area PM in mice corresponds to in macaques, it’s much smaller and more simply organized in mice and other rodents.6,12,13 The contention that rodents “have it” lacks the nuance necessary for understanding what occurred during cortical evolution in primates (and, for that matter, in rodents and other mammals). The inset in Figure 11.3B illustrates the size of area PM in mice at the same scale as a flattened cortical map of macaque monkeys. Now imagine dividing the tiny blue patch in the inset, less than 2 mm2 of cortex, into a dozen separate areas, each ~0.15 mm2. That’s the size of cortical columns, not cortical areas.

Figure 11.3 Grade difference in posterior parietal cortex. Comparison between cortical maps in mice and macaque monkeys for selected cortical areas. (A) Mice. (B) Macaques. Inset: mouse brain scaled to the dimensions of Part B. In Part B, the macaque cortex is flattened into a single plane. Blue shading marks the posterior parietal cortex. Note that S1 in mice is homologous with area 3b in macaques. Abbreviations: 1, 2, 3a, and 3b, somatosensory areas; A1, primary auditory cortex; AIP, anterior intraparietal area; Ig, granular insular cortex; LIP, lateral intraparietal area; M1, primary motor cortex; MIP, medial intraparietal area; PM, posteromedial cortex in rodents; PR, rostral parietal somatosensory cortex; PV, ventral parietal somatosensory cortex; Ri, retroinsular somatosensory cortex; S1, primary somatosensory cortex; S2, second somatosensory cortex; V1, primary visual cortex; V2, second visual cortex; VIP, ventral intraparietal area; VS, ventral somatosensory cortex. Adapted from D.H. O’Connor, L.A. Krubitzer, and S. Bensmaia, Of mice and monkeys: somatosensory processing in two prominent animal models, Progress in Neurobiology 201, 102008, 2021.© 2021 Daniel H. O’Connor,Leah Krubitzer,Sliman Bensmaia. Published by Elsevier Ltd.

In summary, there’s no simple answer to the question: Do rodents have a posterior parietal cortex? The best answer, I think, is that they both have it and don’t: to have and have not, as the title of this section puts it. They might have a homolog of one of the many posterior parietal areas of primates, but they don’t have anything remotely resembling the large and complex suite of areas that composes the posterior parietal cortex in primates, especially anthropoids. Although this example concerns the parietal cortex, the same considerations apply to the temporal visual cortex and the prefrontal cortex. These parts of the cerebral cortex have a size and complexity in primates that make any like-named structures in rodents pale in comparison. Rather than asking whether rodents “have it,” it’s more useful to ask a different question: What kind of posterior parietal cortex, prefrontal cortex, or temporal visual cortex do rodents have? In each case, rodents have fewer areas and much less diversity in neural representations.

Mixtures and miniatures The amalgam theory of cortical evolution, also called the amalgam–parcellation theory, holds that ancestral mammals had a cortex with all the properties that characterize the cortex of modern primates. But instead of separate areas with distinctive characteristics and functions, everything that’s in the cortex of primates is said to be intermixed within an area. Then, during primate evolution, this mixed-property area parcellated into dozens of functionally distinct cortical fields, but in small-cortex mammals it didn’t, so the mixed-property amalgam remains. This idea is sometimes applied to the whole cortex, or separately to the prefrontal, posterior parietal, or temporal visual cortex. The replica-in-miniature theory is simpler. It denies diversity among mammalian cortical maps and holds that small-cortex mammals have all the areas observed in anthropoids, but in microscopic form. For the visual cortex, the replica-in-miniature notion is more common than amalgams.14,15,16 For the prefrontal and posterior parietal cortex, both ideas have been advanced. Neither has convincing empirical support. The overarching reason is that they don’t rely on diagnostic traits, but instead rely on similarities, alone, none of which identify the prefrontal, posterior parietal, or visual areas (or their neuronal representations) uniquely.17,18 Because the arguments differ somewhat for the prefrontal cortex versus sensory areas, the next two sections, “Frontal fields” and “Sights and sounds,” discuss them separately. Frontal fields Chapters 12 and 13 discuss the prefrontal cortex in the context of primate paleontology and paleoecology. To do so without digressions, this chapter clears some detritus out of the way. The crucial concept is that several prefrontal areas, all of which are characterized by a conspicuous layer of small cell bodies, are primate specializations (Figure 11.4). This layer, revealed by Nissl stains, is called the internal (or inner) granular layer, and it’s also known as layer 4. Rats and mice lack homologs of these prefrontal areas, collectively called the granular prefrontal cortex. They do have homologs of other areas that are often included in the prefrontal cortex and which are characterized by the lack or near-lack of an internal granular layer. It’s important to recognize that the internal granular layer is simply a diagnostic trait in this instance; there isn’t any implication about its importance or functions.

Figure 11.4 Ideas about homologies among frontal areas in rodents and primates. A color code identifies proposed homologs. (A) Macaque cortex, with frontal areas color-coded according to the key at the bottom. (B) The replica-in-miniature theory as applied to the frontal cortex of rodents. (C) Homologies in rodents and primates according to comparative neuroanatomy. (D) The amalgam theory as applied to the frontal cortex of rodents, illustrated by a mixing of colors. In Part D, the colors represent physiological and functional properties, not cytoarchitectonic characteristics, so the key at the bottom of Part A doesn’t apply to the speckled regions in Part D. Abbreviations: AC, cingulate cortex (area 24 in primates); AS, arcuate sulcus; cc, corpus callosum; CgS, cingulate sulcus; CS, central sulcus; FEF, frontal eye field; IL, infralimbic cortex; IPS, intraparietal sulcus; LatS, lateral sulcus; LunS, lunate sulcus; M1, primary motor cortex; OB, olfactory bulb; OFC, orbitofrontal cortex; PF, prefrontal cortex; PFdl, dorsolateral prefrontal cortex; PFdm, dorsomedial prefrontal cortex; PFp, polar prefrontal cortex; PFvl, ventrolateral prefrontal cortex; PL, prelimbic cortex; PM, premotor cortex; PS, principal sulcus; STS, superior temporal sulcus. Numbers in Part A indicate Brodmann-like area designations. Adapted from T.M. Preuss and S.P. Wise, Evolution of prefrontal cortex, Neuropsychopharmacology 47, 3–19, 2022.

The word “granular” in granular prefrontal cortex requires explanation. It’s misleading but so common that it’s unavoidable. This term derives from cytoarchitectonics, the study of the distribution and cytological characteristics of neuronal cell bodies. (A sister discipline, myeloarchitectonics, refers to the distribution of myelinated fibers.) The problem is that what’s called granular cortex in the frontal lobe is either homotypical or dysgranular cortex, not granular cortex. Figure 11.5 illustrates four types of neocortex. Homotypical cortex, also known as eulaminate cortex, has each of the six layers of neocortex in standard proportions, and it contrasts with heterotypical cortex, which comes in three types: (1) agranular cortex, which lacks a qualitatively obvious concentration of cells corresponding to the internal granular layer; (2) true granular cortex (also known as koniocortex), which has a dense and unusually thick (and sometimes differentiated) internal granular layer; and (3) dysgranular cortex, which is intermediate between agranular and homotypical cortex.

Figure 11.5 Types of cerebral cortex. Layer 4 is the internal (or inner) granular layer (blue): a relatively dense concentration of granule-cell bodies as revealed by Nissl stains. Left: labels for several types of neocortex, with some new names at the top and traditional names at the bottom. Right: allocortex.

In the context of the frontal lobe, which lacks koniocortex, any concentration of small cell bodies in the internal granular layer is notable, so homotypical and dysgranular frontal areas are almost always called “granular.” I doubt that the phrase “homotypical and dysgranular prefrontal cortex” will catch on, so I sometimes call these areas “typically layered” in subsequent chapters. I recognize the downside of creating a new term, but this label becomes useful later. In what follows, typically layered prefrontal cortex means the same thing as granular prefrontal cortex. The term “agranular” can also be misleading, if taken literally. Fortunately, it isn’t meant to be taken literally. The main thing to keep in mind is that homotypical and agranular areas are clearly and obviously different kinds of cortex. It’s been known for more than a century that all neocortical areas pass through a stage during embryogenesis in which they have a typical laminar architecture. As development proceeds, the internal granular layer (layer 4) changes in some areas more than in others. In the agranular areas, most of these cells disperse to other layers. Molecular analyses show that some features of layer 4 remain, but this fact doesn’t negate the clear-cut differences between agranular and typically layered areas. Accordingly, it’s more accurate to think of these areas are nearly agranular, not absolutely agranular. Primates have a much more extensive granular prefrontal cortex than do other mammals. Many mammals, including rats and mice, have no granular prefrontal cortex at all. Dogs (Canis familiaris),19 domestic cats (Felis cattus),20 and Eastern gray squirrels (Sciurus carolinensis)21 are said to have small granular prefrontal areas, but they have nothing like the extensive granular prefrontal cortex observed in modern anthropoids, and whatever such areas they do have probably evolved convergently with those in primates. It might seem strange to focus so much attention on one layer of cortex, but the reason that the internal granular layer is important is that it serves as a diagnostic trait for frontal areas that are specific to primates or, for some of them, specific to anthropoids. This conclusion has generated controversy because of its implications for animal models in biomedical research. Consequently, ideas about cortical evolution have become entangled in extraneous issues about funding priorities. As a result, many neuroscientists wonder: If rats and mice have something called prefrontal cortex, why isn’t this good enough? The answer has to do with homology. There are many claims that the medial frontal cortex in rats or mice is homologous with the entire prefrontal cortex in primates,22,23,24,25 either as a miniature version (Figure 11.4B) or as an amalgam (Figure 11.4D). Both ideas are wrong.26,27,28,29,30,31 (Note that the graphical depiction of a frontal-cortex amalgam in Figure 11.4D deals with physiological and functional characteristics, not cytoarchitectonic ones. For example, the blue squares convey the mistaken idea that the rodent frontal cortex has all the physiological and functional properties that the granular prefrontal cortex has in macaque monkeys. They are not meant to imply that rodents have small bits of granular cortex scattered among other architectonic types.) Most claims that rats or mice have a homolog of the granular prefrontal cortex emphasize similarities. Examples include projections from granular prefrontal areas to the nucleus accumbens32 or reciprocal connections with the mediodorsal nucleus of the thalamus.33,34 However, similar connections mean nothing in this regard if agranular frontal areas in primates also have these connections, which they do.35,36,37 Likewise, some neurophysiological properties—the encoding of valuations,38 for

example—are ubiquitous in the neocortex,39 so they don’t identify prefrontal areas, either. There’s a recognized formula for identifying a homolog of the granular prefrontal cortex in rodents. If it exists, which it doesn’t, such an area would have traits that: (1) the granular prefrontal cortex of primates also has; and (2) the agranular prefrontal cortex of primates lacks. That is, it would have diagnostic traits for granular prefrontal cortex. The agranular prefrontal cortex of both primates and rodents comprises two groups of areas: (1) medial prefrontal areas, which are green in Figure 11.4A and C; and (2) agranular orbitofrontal areas, which are not illustrated here. Chapter 13 (“Anthropoid areas”) revisits this topic, and Figure 13.3 illustrates both the medial and orbital prefrontal areas, including both their granular and agranular components. Arguments for rodent–primate homologies based on behavioral data suffer from additional problems. Lesions or inactivations of the granular prefrontal cortex of macaque monkeys40 cause impairments on tasks said to measure spatial memory or spatial working memory, and some proponents of the replica-in-miniature or amalgam theories rely on impairments in spatial memory caused by lesions or inactivations of the medial frontal cortex in rats or mice.41,42 Whether these tasks measure the same kind of spatial memory is doubtful,27 but even assuming that they do, lesion effects in rodents don’t support claims for homology. The reason is that such results require a more in-depth analysis than simply an assessment of whether lesions do or don’t cause a statistically significant impairment on a task said to measure spatial memory. Frontal cortex lesions in rats don’t cause the severe and permanent impairments on these tasks that prefrontal cortex lesions cause in macaques. Rats with such lesions relearn the tasks quickly and perform normally thereafter.42,43 Monkeys with prefrontal cortex lesions never perform the tasks better than chance level if they learned it before the lesion40 and cannot learn the task at all if they sustain the lesion prior to any training.44 These differences refute arguments for homology based on lesion (or other loss-of-function) effects and are easy to explain on other grounds, such as the loss of medial prefrontal inputs to the hippocampus in rodents. The areas lesioned in macaques affect hippocampal function much less directly than the areas lesioned in rodents.45 Furthermore, the granular prefrontal cortex of macaques has physiological and connectional properties that the rodent frontal cortex lacks. The following is a small sample: •

•

Among the physiological properties, prefrontal neurons encode associations between auditory cues and abstract behavior-guiding rules (matching versus nonmatching).46 Another example involves associations between novel objectlike visual stimuli and abstract problem-solving strategies (change-shift versus repeat-stay).47 Importantly, the activity of these neurons reflects whether the monkeys implemented the correct strategy on a trial-by-trial basis.48 Related work shows that lesions of the orbital and ventral parts of the macaque prefrontal cortex completely block their ability to learn arbitrary associations rapidly. Normal, experienced macaques can simultaneously learn three or four associations between object-like visual stimuli and the goal of a reaching movement within a few trials.49 After prefrontal cortex lesions, however, they remain at chance levels for several dozen trials. It takes days for the impaired monkeys to learn something that only takes a few minutes for normal ones. Lesions of the medial frontal cortex in rats not only fail to cause an impairment on a similar task,50 but instead speed learning, especially in its early stages.51 Among the neuroanatomical differences, the granular prefrontal cortex of primates is part of several large-scale transcortical networks, which collectively include the granular prefrontal cortex, the posterior parietal cortex, and both the superior and inferior temporal cortex. Each of these regions contains a suite of cortical areas that are linked to each other by connections.52,53,54,55,56,57,58 Rodents have nothing like these large-scale cortical networks. In addition, the medial pulvinar is a primate-specific thalamic nucleus30,31,59 that has connections with most of the primate-specific cortical areas.60,61,62

In summary, the region of cortex commonly called prefrontal in rodents is not an amalgam that has all the properties observed in the granular prefrontal cortex of primates; it’s not a replica-in-miniature that has all the prefrontal areas that primates have; and it’s not a homolog of the granular prefrontal cortex in primates. Although it’s reasonable to say that rodents “have a prefrontal cortex,” they have homologs of only some of the prefrontal areas that primates have. Sights and sounds Primate–rodent comparisons dominate the literature, but they’re not the most important ones for understanding the evolution of sensory cortex, such as the visual areas. A comparison of visual areas in macaques,63,64 tree shrews,65 and galagos66 reveals much more. These data show that many new visual areas evolved during primate evolution. There have been claims that rodents have a replica-in-miniature of the 2–3 dozen visual areas in the macaque cortex,14,15 including both the dorsal and ventral visual streams, but these proposals misinterpret variations within an area as evidence for separate areas. Chapter 12 (“Seeing the new”) presents more details on this point, and Krubitzer et al.9 have reviewed this topic in yet more depth. So, I won’t rehash all that here. A consideration of auditory areas unmasks the implausible assumptions underlying the amalgam and replica-in-miniature theories of cortical evolution. In echolocating bats, specialized auditory areas represent echo delays and Doppler shifts,67,68,69 to cite just two examples. The word “represent,” in this sense, means that neurons in those areas are tuned to a relatively narrow range of either echo delays or Doppler shifts, and when acoustic inputs are in that range, these cells discharge. In this way, sensory representations provide echolocating bats with the ability to sense the distance of their insect prey and whether the bugs are getting closer or farther. Other mammals, including rodents and primates, lack these specialized areas and neural representations. Both the replica-in-miniature and amalgam proposals predict that rodents and primates should have cortical neurons that encode echo delays and Doppler shifts. After all, they all descend from a common ancestor, and everything must have been in the ancestral cortex somewhere, either as a replica-in-miniature or for parcellation during bat evolution. However, representations of echo delays and Doppler shifts would provide no survival benefits to rodents and primates. Most species in these two groups are herbivores, and even for primates that prey on insects, echo delays and Doppler shifts are irrelevant to their foraging strategies.

In summary, the amalgam and replica-in-miniature proposals are simply incoherent when applied to the auditory areas of bats, and there is little to recommend them for any other areas or species. Most neuroscientists accept the idea that bats have cortical areas that rodents lack, so it’s surprising that there’s so much resistance to the idea that primates also have areas that rodents lack. Although I reject the replica-in-miniature theory as propounded in the past, Chapter 13 (“Changes at the top”) resurrects something like it. There I suggest that new areas sometimes emerge via enlargement of variants within an area. The difference is that the idea advanced in Chapter 13 depends on well-defined, concrete representations in a specific region of cortex and a comparative analysis much broader than the usual primate versus rodent discussions. Lamentation of the lizard For completeness, I mention the triune-brain theory of Paul MacLean.70 As far as I know, no reputable comparative neuroanatomist takes his ideas seriously, so explicit refutations are rare in the contemporary neuroscience literature. One recent review went through the trouble,71 but more often the neuroscience literature simply ignores the triune-brain theory. All the same, the concept of a reptilian brain, often called a lizard brain, has captured the popular imagination. These terms refer to homologous brain structures in mammalian and nonmammalian brains, such as the basal ganglia and the amygdala, along with much of the diencephalon and brainstem. It’s true that reptiles, humans, and other mammals have these structures. However, they are fully mammalian in mammals, fully human in humans, and so forth. For example, no one doubts the role of the amygdala in defensive reflexes and actions. But in lizards the amygdala performs these functions in a lizard’s way, whereas in humans the amygdala performs them in a human way. In addition, the triune-brain doctrine also includes many frank errors. Among them is the idea that the hippocampus evolved in early mammals. Comparative neuroanatomy shows that the hippocampus evolved long before the advent of mammals.72,73 Likewise, many of the neocortical areas that MacLean considered to have evolved only in “advanced” mammals actually emerged in the earliest and most primitive mammals. He made similar mistakes about the evolution of the basal ganglia, which he considered to be part of the “reptilian brain” in its entirety. To the contrary, basal ganglia of primates has components that are specific to primates and therefore evolved relatively recently.74 Beyond these specific objections, the fundamental flaw in triune-brain theory is the assumption that homologous brain structures perform the same function, unchanged from the ancestral condition. That’s wrong. As Chapter 2 explained, the concept of homology implies only descent from a common ancestor. The functions of many homologous structures changed during mammalian and primate evolution. For example, because the amygdala has functional interactions with a host of cortical areas that evolved in primates, its functions differ from the amygdala of lizards, other reptiles, and other mammals. That is why the amygdala in primates performs its functions in a primate’s way and not in a lizard’s way. Rings for right and wrong reasons Old ideas about new areas Unfortunately, the neuroscience literature includes some old ideas about new areas, which lack a sound basis in evolutionary biology. Based on speculation about cortical evolution, Sanides75 proposed that the primary sensory areas of the cerebral cortex are “new areas,” meaning that they evolved most recently in evolution. He made the mistake of conflating simplicity in laminar architecture with phylogenetic antiquity, and complex lamination with recency. In addition, he imagined a specific “source” for cortical areas. Sanides shoehorned the cortex into areas that, as he imagined cortical evolution, “derived” from either the hippocampus or the piriform cortex: the dual-origins doctrine. According to his often-cited speculations, cortical areas evolved in a sequence from primitive to “advanced” cortex in a series of nested rings, which he called an evolutionary ur-trend.* Although Sanides’s ideas about dual origins and evolutionary trends have been thoroughly discredited,76 his followers have continued to recite these outdated ideas as fact.77,78,79,80,81 Some of his disciples have devoted an entire book82 to dual origins, long after evidence from comparative neuroanatomy falsified this idea conclusively. New ideas about old areas Why, in contradiction to the published opinions of established neuroanatomists, do I reject Sanides’s doctrine with such conviction? Sanides believed that the primary sensory areas—specifically, the primary visual (V1) cortex, the primary somatosensory (S1) cortex (called area 3b in anthropoids), and the primary auditory (A1) cortex—are among the most recently evolved cortical areas. The exact opposite is the case. Contemporary comparative neuroanatomy shows that these three primary sensory areas arose in the earliest mammals,83,84,85,86 and as such they are among the oldest neocortical areas, not the newest. This conclusion follows from the observation that all mammals studied to date have V1, S1, and A1 in roughly the same configuration, with similar topography (retinotopy, somatotopy, or tonotopy) and similar thalamic inputs, among many other features that point to homologies. Importantly, these three sensory areas not only exist in all placental and marsupial mammals, but also in a monotreme species: the duckbill platypus.87,88 Monotremes are more distantly related to other modern mammals than evolutionary biologists once believed. Mammals evolved from cynodonts (Figure 10.1A), and it now seems likely that two different cynodont lineages produced modern mammals: one leading to therians (placental and marsupial mammals); the other leading to monotremes.89 Given that monotremes and therians diverged relatively early in mammalian evolution, the fact that both groups have homologs of V1, A1, and S1 strongly indicates that these parts of the neocortex date to the origin of mammals, contrary to the dual-origins doctrine. And it’s not just those three areas that have homologues in all mammals, at least 16 such areas can be identified.

Figure 11.6 Cortical organization in mammals. (A) A figurative, flattened cerebral cortex showing its ring-and-core structure. Allocortex is blue; neocortex is pink. The neocortex comprises mesocortex (light pink) and core neocortex (dark pink). (B) A flattened and expanded version of the cortical mantle in Part C. This graphical depiction causes distortions, so it doesn’t show relative sizes accurately. However, it depicts the reconstructed cortex of a basal mammal, based on comparative neuroanatomy. (C) Brain drawing placing Part B in context. Abbreviations: A1, primary auditory cortex; G, primary gustatory cortex; S1, primary somatosensory cortex; S2/PV, secondary

somatosensory cortex, which is closely linked to a somatosensory field called PV; SC, caudal somatosensory cortex; SR, rostral somatosensory cortex; T, an ill-defined caudal temporal visual area; V1, primary visual cortex; V2, secondary visual cortex. Adapted from J.H. Kaas, The evolution of brains from early mammals to humans, Wiley Interdisciplinary Reviews in Cognitive Science 4, 33–45, 2013. Part C created by Mary K.L. Baldwin. (B and C) Adapted from E. A. Murray, S.P. Wise, M.K.L. Baldwin, and K.S. Graham, The Evolutionary Road to Human Memory, Oxford University Press, Oxford, UK, 2020.(A) © 2012 John Wiley & Sons, Ltd. (B and C) Oxford Publishing Limited

On the basis of these and other findings, Kaas83 reconstructed the cortex of basal mammals along the lines illustrated in Figure 11.6B. Because primates inherited a variant of this cortical map, it’s worth examining it in some detail. Not only did the inferred stem mammal have V1, A1, and S1, but also a dozen additional areas, at least. The original Kaas reconstruction includes ~20 areas, of which Figure 11.6B depicts the 16 that seem most reliable to me. Because S2 (the second somatosensory area) and PV are not always distinguishable in monotremes and marsupials,90 my version of the figure lumps them together. It also excludes several subdivisions of the anterior cingulate and orbital cortex that many neuroanatomists have recognized and omits some of the smaller visual areas (e.g., the prostriate cortex and some small visual fields sometimes called VM or V3 in murine rodents). My version is not meant to replace the original; instead, it’s a slight simplification intended to make the ring structure of neocortex clearer, as the next section explains. Reading rings rightly One of the reasons that the dual-origins doctrine continues to receive attention is that a few of its tenets are valid. Sanides imagined an ancestral condition in which the cerebral cortex consisted mainly, if not exclusively, of the hippocampus and the piriform cortex: the two largest allocortical areas in mammals. The allocortex is an easily recognizable type of cortex, which has only three layers (Figure 11.5). Although a simple hippocampus–piriform cortex scheme omits several smaller allocortical areas, a charitable assessment is that it generally agrees with the evidence from comparative neuroanatomy.72,76 Basal amniotes—which were the ancestors of birds, reptiles, and mammals—probably had a cerebral cortex with an entirely allocortical structure. Its medial cortex was homologous with the mammalian hippocampal complex, and its lateral cortex (sometimes called the lateral and ventral cortex) was homologous with the piriform cortex.72 Sanides got that part roughly right. More importantly for the present purposes, he also recognized the fundamental ring-and-core structure of the mammalian cortex, which Figure 11.6A illustrates. Despite complex sulcal and gyral patterns in many species, the cortex of all mammals, including primates, has retained this overall pattern of organization. In most classification schemes, neuroanatomists have recognized three rings around a core.75,91,92,93 The core includes somatosensory, motor, auditory, visual, and other neocortical areas, some of which are traditionally classified as association cortex. (Chapter 13, “Guilt by association,” discusses the concept of association cortex.) Some neuroanatomists restrict the terms neocortex and isocortex, which are synonymous, to the core; others include all areas other than the allocortex, as I do. I return to this point a little later, but first it’s important to spell out what’s in each ring: • • •

•

The outermost ring consists of allocortex, which is dominated by the hippocampus and the piriform cortex. There are also smaller allocortical areas, called transition areas, such as the amygdalohippocampal transition area. Obscure allocortical areas called the indusium griseum and the tenia tecta complete the outer ring. The areas adjacent to the allocortex are classified as periallocortex (or juxtallocortex) and the areas adjacent to the core are called proisocortex. Together, the periallocortex and proisocortex compose the mesocortex.76 The mesocortex of primates includes several areas omitted from Figure 11.6B. The periallocortex not only includes the retrosplenial cortex and parts of the agranular cingulate, orbital, and insular cortex that lie adjacent to the allocortex, but also the entorhinal cortex, subiculum, parasubiculum, and presubiculum. The proisocortex includes most of the anterior cingulate cortex (also known as area 24 in primates), prelimbic cortex (area 32), and infralimbic cortex (area 25), medially, along with parts of the agranular orbital and agranular insular cortex adjacent to the core. (Sometimes, areas 24, 25, and 32 are lumped together as the anterior cingulate cortex, and sometimes that term is reserved for area 24.) Together, the outer rings of the cortex, allocortex and mesocortex, compose the limbic cortex. The term limbic refers to the margin or boundary (limbus) of the cortex, which is characterized by two properties: axonal connections with the hypothalamus and a relatively direct influence over the autonomic nervous system.94,95,96,97

The crucial concept is that—contrary to Sanides’s doctrine—the periphery-to-core anatomical sequence doesn’t reflect an evolutionary sequence. Comparative neuroanatomy has refuted the sequential evolutionary “ur-trends” that Sanides proposed.76,98 Except for the allocortex, cortical areas evolved in a very different order than he imagined. Instead of a phylogenetic basis, there’s an ontogenetic basis for the ring structure of the cortex. It involves a medial-to-lateral gradient in expression of the hem signal and a lateral-to-medial expression of the anti-hem signal during embryogenesis.99 In addition, nearby areas tend to share connections and therefore axonal inputs, so lateral areas preferentially process and represent related kinds of information, which differ from the kinds of information that medial areas process and represent. None of these principles supports the dual-origins dogma, which for some reason continues to circulate in the neuroscience literature. These points might seem arcane, but they’re important because of their implications about cortical evolution in primates. Knowing neocortex The terminology used in this book, in which the neocortex includes all the cerebral cortex except the allocortex, emphasizes the idea that the neocortex is a synapomorphy of mammals. Another reason for combining the core neocortex and the mesocortex is that they develop the same way. Neurons migrate to the cortical mantle from the neuroepithelium, where they undergo their final round of cell division, called birth. Neurons born earlier end up deeper in the cortex, and those born later end up more superficially. This developmental process creates an inside-out pattern of neuronal migration, although some neurons (Cajal–Retzius cells) in layer 1 violate this rule. The core cortex and the mesocortex both develop in an inside-out sequence, so it makes sense to combine them and classify all such areas as neocortex (or as isocortex, an equivalent term*). The allocortex develops differently.

The most important changes during primate evolution were additions to the core neocortex: an extensive group of granular prefrontal, posterior parietal, and temporal areas, among others (Chapter 12). Notwithstanding these more-celebrated innovations, the ring neocortex did not remain unchanged. In the anterior cingulate cortex, for example, rodents have fewer subdivisions than macaques or humans, so it seems likely that primates evolved new components there, too. Even the subdivisions common to rodents and primates have differences in connections and neurotransmitter receptors.100,101,102,103 One ring area, the anterior insular cortex, expanded preferentially during human evolution (Figure 14.4). Chapter summary Among the species that neuroanatomists have studied in sufficient detail, comparisons among tree shrews, galagos, and anthropoids provide the most valuable information about cortical evolution in primates. As members of the sister group of Euarchontans, data from rodents can support inferences from tree shrew–primate comparisons. Unfortunately, the neuroscience literature includes several misleading ideas about cortical evolution, which are too often repeated uncritically. Among these misconceptions is the idea that because rodents have a homolog of some small part of a large and complex cortical region in primates, then nothing happened during evolution except scaling. As Chapters 12 and 13 explain, several new cortical areas emerged during primate evolution; the cortex of primates changed in composition as well as in size. The replica-in-miniature, amalgam, and dual-origin theories are also wrong. Proponents of the latter imagine that the most recently evolved areas in primate brains are primary sensory areas that perform relatively simple visual, auditory, and somatosensory functions. The real story of cortical evolution is precisely the opposite: The primary sensory areas (among other sensory areas) evolved in basal mammals, and, as subsequent chapters explain, the cortical specializations of primates contribute to functions far removed from simple sensory processing. A host of new areas helped primates of the past compete for survival in their time and place, and the next three chapters discuss these evolutionary developments in detail. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38.

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The functional and anatomical organization of marsupial neocortex: evidence for parallel evolution across mammals. Progress in Neurobiology 82, 122–41 (2007). 87. Krubitzer, L.A., Manger, P., Pettigrew, J., & Calford, M. Organization of somatosensory cortex in monotremes: in search of the prototypical plan. Journal of Comparative Neurology 351, 261–306 (1995). 88. Krubitzer, L.A. What can monotremes tell us about brain evolution? Philosophical Transactions of the Royal Society of London, B: Biological Sciences 353, 1127–46 (1998). 89. Cachel, S. Fossil Primates (Cambridge University Press, Cambridge, 2015). 90. Dooley, J.C., Franca, J.G., Seelke, A.M., Cooke, D.F., & Krubitzer, L.A. A connection to the past: Monodelphis domestica provides insight into the organization and connectivity of the brains of early mammals. Journal of Comparative Neurology 521, 3877–97 (2013). 91. Braak, H. Architectonics of the Human Telencephalic Cortex (Springer, Berlin, 1980). 92. Mesulam, M.M. Principles of Behavioral and Cognitive Neurology (Oxford University Press, New York, 2000). 93. Stephan, H. Evolutionary trends in limbic structures. Neuroscience and Biobehavioral Reviews 7, 367–74 (1983). 94. Catani, M., Dell’Acqua, F., & Schotten, M.T. A revised limbic system model for memory, emotion and behaviour. Neuroscience and Biobehavioral Reviews 37, 1724–37 (2013). 95. Lew, C.H. & Semendeferi, K. Evolutionary specializations of the human limbic system. In: Evolution of Nervous Systems (ed. J.H. Kaas & T.M. Preuss), 4, 277–91 (Elsevier, Amsterdam, 2016). 96. Price, J.L. Definition of the orbital cortex in relation to specific connections with limbic and visceral structures and other cortical regions. Annals of the New York Academy of Sciences 1121, 54–71 (2007). 97. Vogt, B.A. Cingulate cortex in the three limbic subsystems. Handbook of Clinical Neurology 166, 39–51 (2019). 98. Wise, S.P. Evolutionary and comparative neurobiology of the supplementary sensorimotor area. In: The Supplementary Sensorimotor Area (ed. H. Lüders) 71–83 (Raven Press, New York, 1996). 99. Aboitiz, F. Genetic and developmental homology in amniote brains: toward conciliating radical views of brain evolution. Brain Research Bulletin 84, 125–36 (2011). 100. Berger, B., Gaspar, P., & Vernay, C. Dopaminergic innervation of the cerebral cortex: unexpected differences between rodents and primates. Trends in Neurosciences 14, 21–7 (1991). 101. Öngür, D. & Price, J.L. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex 10, 206–19 (2000). 102. Vogt, B.A., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zollei, L., Polimeni, J.R., Fischl, B., Liu, H., & Buckner, R.L. Cingulate area 32 homologies in mouse, rat, macaque and human: cytoarchitecture and receptor architecture. Journal of Comparative Neurology 521, 4189– 204 (2013). 103. Vogt, B.A. & Paxinos, G. Cytoarchitecture of mouse and rat cingulate cortex with human homologies. Brain Structure and Function 219, 185–92 (2012).

*

Unfortunately, the older literature on cortical maps, which explored a wider diversity of species, used methods too crude for useful comparisons. * The term “ur-trend” doesn’t mean much, but it’s supposed to evoke a sense of something primeval or emanating from origins. * The reason that I prefer neocortex is that it means new cortex, which—relative to the allocortex—it is. The term isocortex implies that all the neocortex is the same, which in some respects it is, but in other respects it isn’t. Iso–, meaning equal, is the same prefix as in isobar, which refers to equal air pressure. Equal is a strong word, and even neuroanatomists who use the term isocortex acknowledge that the various neocortex areas are far from equal. If they were, there would be no architectonic maps.

12 Suites of specializations Overview Cortical specializations improved the ability of early primates to move and forage in the understory of dense forests. New posterior parietal and premotor areas guided reaching and grasping in a coordinate frame based on vision; a new or vastly enlarged hindlimb representation in the primary motor cortex improved pedal grasping; the frontal eye field (FEF) and new visual areas enhanced the ability to search for, identify, and maintain attention on items of value in a dim, cluttered visual space (without a fovea); and new parts of the orbital prefrontal cortex (PFo) updated the valuation of poorly seen or hidden food items based on both current biological needs and the association of a food’s visual appearance with more-visible objects. Together, the two novel prefrontal areas—FEF and PFo—improved foraging efficiency. In addition to its other roles, cortically controlled, hindlimb-dominated leaping was an effective antipredator strategy. The vision seemed to enter the house with me—the stretcher, the phantom-bearers, the wild crowd of obedient worshipers, the gloom of the forests, . . . the beat of the drum regular and muffled like the beating of a heart, the heart of a conquering darkness. It was a moment of triumph for the wilderness . . . —Joseph Conrad, Heart of Darkness, 1899

Introduction In this chapter, forests filter photons; galagos grab some grub; and our ancestors dine in the dark. But I begin with the epigraph. The image of a dark and gloomy forest, a wilderness that triumphs over any effort to tame it, has some relevance to primate evolution. Primates evolved in dense forests and had to conquer ceaseless darkness. The “vision” that begins the epigraph refers to involuntary visual imagery, also known as a reverie, but vision in its usual sense was central to the life of early primates. Many primate adaptations involved vision, others involved grasping, and a combination of the two—visually guided grasping—got some primates through the Eocene. Others didn’t make it. This chapter is the first of three that integrates paleontology (Chapter 5) and paleoecology (Chapter 6) into knowledge about cortical maps. One clue from primate paleoecology is especially important. In Eocene ecosystems, primate species competed with each other frequently, as well as with birds and other arboreal mammals.1 Subsequently, from the Oligocene onward, primate-on-primate competition became less common. Eocene primates were closely related species, so they began their competition with similar cortical maps, and any edge provided by enhanced cortical functions provided a competitive advantage. Because evolutionary changes occurred in extinct primates, it’s important to remember that they differed from modern species in significant ways. Two sources of intuition interfere with appreciating these differences. One involves vision. Our visual experience is a poor guide for understanding how Eocene primates saw their world, as a later section explains (“Dining in the dark”). The other involves social cognition. A great deal of thinking and writing about cortical evolution has concentrated on the cognitive demands of primate societies. Social cognition plays a sizable role in the lives of most modern primates, including prosimians, but that wasn’t always the case. All modern primates have probably descended from primate ancestors that led a solitary life, as many strepsirrhines do today (Figure 15.3).2 It’s important not to oversimplify the social lives of animals classified as solitary; that’s a relative term, which isn’t intended to be taken literally. The opposite of solitary is gregarious; and solitary is not synonymous with asocial. Nevertheless, the idea that ancestral primates had relatively simple social systems has two implications. First, the selective forces acting on the cortex of early primates involved foraging and predator avoidance rather than the demands of social behavior. Second, complex social systems evolved independently in the major primate lineages, long after primates diverged from other mammals. One final point of introduction: Some discussions claim that predation on modern primates is rare. That’s probably wrong,3 but even if it were true today, that idea wouldn’t apply to Eocene primates, which faced a different set of predators, some of which could climb or fly into trees (Box 12.1). Box 12.1 Eocene predators Arboreal life provided Eocene primates with protection from many predators, but by no means all of them. Extinct representatives of groups that prey on primates today—raptors, carnivorous mammals, and snakes—hunted small mammals during the Eocene. First, early owls date from the late Paleocene, and they could have preyed on primates throughout the Eocene. Second, a polyphyletic group of carnivorous eutherians, traditionally called creodonts, hunted in North America, Europe, Asia, and Africa during the late Paleocene and throughout the Eocene. Some creodonts were in a sister group of carnivores; others were an outgroup of carnivores related to pangolins. (Figure 3.3 illustrates the relationship of carnivores and pangolins to other eutherian mammals.) Creodonts became the dominant mammalian predators of the early and middle Eocene, and an early Eocene genus evolved a mobile foot structure typical of modern species that climb trees.4, 5 Carnivores emerged during the Paleocene as small, generalized predators, which they remained throughout much of the Eocene. Like creodonts, some Eocene carnivores could climb trees.5 Both carnivores and creodonts had dental specializations for hunting and meat eating, such as large canine teeth that served as weapons, along with specialized molars and premolars (carnassial teeth) that sheared meat. Creodonts thrived during the late Paleocene and much of the Eocene, but they became less common as carnivores diversified during the late Eocene. Third, according to a phylogenetic reconstruction of snakes, the ancestors of modern boas and pythons lived in North America, Asia, and

Africa during the Eocene.6 (Also, although it’s of more relevance to the next chapter, tree-climbing snakes were well established in South America when platyrrhines arrived.) From tip to toe Dining in the dark Chapter 6 explained that primates of the Paleocene and Eocene lived in tropical and subtropical rainforests. In these ecosystems, less than 2% of the light reaching the top of the trees makes it to the ground.7 So, illumination is always dim under the canopy, even during the day. Although the phrase “black as night” is common, nighttime is not completely dark, even in a rainforest. In addition to sunlight, illumination comes from a combination of moonlight, starlight, and phenomena called airglow and zodiacal light. Airglow results from the light emissions of various molecules in the upper atmosphere; zodiacal light is sunlight reflected from dust concentrated in the orbital plane of the solar system. Moonlight has only 0.002% of sunlight’s intensity,7 but it’s several-hundred-fold brighter than the other sources of nighttime light combined. Of course, moonlight varies according to a monthly cycle and cloud cover. For example, a quarter moon is one-tenth as bright as a full moon,8 and early primates also had to survive moonless and cloudy nights. Improved night vision provided Eocene primates with crucial adaptive advantages in foraging and predator detection. Like modern strepsirrhines, early Euprimates lacked a fovea, but their large eyes and small corneas sharpened binocular vision in the central part of the visual field, a form of central vision before the fovea evolved in basal haplorhines. In the retina of modern strepsirrhines, photoreceptors send converging projections to interneurons called bipolar cells, which decreases visual acuity to some extent but increases sensitivity to dim light. Early primates probably had a similar retinal architecture. Another adaptation for dim-light vision is the tapetum lucidum, a thin film of tissue that parallels the retina and reflects light. Photoreceptors can detect both reflected photons and directly adsorbed ones, which enhances sensitivity. Many mammals have a tapetum lucidum, but ancestral haplorhines lost it, so no tarsier or anthropoid has one despite a reversion to nocturnal foraging in several species, such as owl monkeys. It’s one of those things, like prehensile feet, that would come in handy, so it’s a bit of a shame that our ancestors lost it. Chapter 5 described some of the visual adaptations of primates. The two types of retinal photoreceptor cells—rods and cones—evolved at different times and have different spectral sensitivities. Cones evolved in early diurnal animals, but rods evolved later as an adaptation to dim-light conditions. Both of these developments predate the emergence of mammals, let alone primates. Rods are much more sensitive to light. Cones need about 200 photons for activation; rods require only one or two.7 Rods rely on the photoreceptive pigment rhodopsin, which has broad spectral sensitivity. Cones transduce photons via other opsins, each of which has a maximal sensitivity at a characteristic wavelength, from which color vision derives. Basal placental mammals inherited two types of cones: short-wavelength (S) cones tuned to blueish light and medium-tolong-wavelength (M–L) cones tuned to the red–yellow–green part of the visible spectrum. Basal Euarchontoglires and Euarchontans also inherited these two cone types, which together mediate dichromatic (two-color) vision. Note that people who have two-color vision are called color blind. In basal catarrhines, a third opsin evolved as a genetic duplication and modification of the M–L cone’s opsin, which endowed them with what’s called the routine form of trichromatic (three-color) vision. Platyrrhines developed a polymorphic form of trichromacy,* which is sex-linked.7 The cones tuned to the slightly shorter wavelength are called medium-wavelength (M) cones, which leaves the remaining descendants of the M–L cones as the long-wavelength (L) type. Thus, trichromacy was not a capacity of primates until after the platyrrhine–catarrhine split. Earlier primates had to make do with dichromatic vision, like most mammals. Based on modern strepsirrhines, rod receptors probably dominated the retina in early primates, with cone receptors playing a subsidiary role. Early primates were primarily nocturnal or crepuscular (dawn and dusk foragers), and they likely had retinas consisting of more than 95% rods. Of the two types of cones in early primates, the S cones, tuned to blueish light, probably contributed most to dim-light vision under the forest canopy.7 During Euarchontan evolution, the S cones changed from having a peak sensitivity to ultraviolet light, as they do in the outgroups of Euarchontans, to having their best response to blue–purple light, which is the dominant wavelength that penetrates the canopy.7, 9 Early primates inherited this trait from their Euarchontan ancestors. Moonlight on a cloudless night suffices to excite rod receptors and perhaps (barely) S cones, but new moons and cloud cover kept illumination levels well below what cone receptors could transduce. Although cone activation requires 100–200-fold more light than rods, cones exceed rods in both temporal and spatial resolution and therefore mediate high-acuity vision. The visual system maintains a high level of visuospatial resolution in pathways from the retina to the midbrain and thalamus. Interneurons, including the bipolar cells mentioned earlier, connect photoreceptors to retinal ganglion cells. They, in turn, project to the lateral geniculate nucleus (LGN) in the thalamus and to the superior colliculus in the midbrain (also known as the tectum or optic tectum). These projections arise from three types of retinal ganglion cells: parvocellular (P-type), magnocellular (M-type), and koniocellular (K-type). P-type neurons subserve high-acuity vision; M-type cells relay information about visual motion and other changes in the visual field; and K-type pathways appear to play a specialized role in dim-light vision. All three types of retinal ganglion cells project to the LGN, segregated to its different layers (from which the names of the types derive).10 Neurons in the LGN, in turn, relay visual information to the cortex.* Primate retinas differ from those of other mammals. Unlike many mammals, which have relatively few P-type cells, these high-acuity retinal ganglion cells compose ~80% of the retina’s output in primates, with the K- and M-types splitting the remaining ~20% fairly evenly.10 Handling hands and running feet Chapter 5 reviewed the grasping specializations that emerged during the transition to Euprimates. In addition to manual grasping, primates evolved a capacity for pedal grasping, with a divergent hallux to secure a grip. Fossil evidence indicates

that Euprimate-like pedal grasping evolved relatively late in the transition to Euprimates, after Euprimate-like manual grasping.11 The new reaching-and-grasping mechanisms of primates had many consequences. Skeletal traits that produced grasping hands and feet benefited from improved control mechanisms in the neocortex. Although other mammals can use their forelimbs to reach for objects (Box 12.2), primates evolved a new and specialized way of guiding reaching and grasping movements, one that uses visuospatial guidance in an extrinsic coordinate frame.12 Those last seven words pack a pretty big punch, so here’s a translation for neuroscientists unfamiliar with motor control theory: Primates guide movements based on vision of the outside world—in a coordinate system that has central vision at its origin and axes that extend into threedimensional space. Posterior parietal areas represent the targets of movements in terms of a specific location in retinal coordinates at a specific distance from a primate’s eyes. Then, premotor–parietal networks transform these extrinsic coordinates into intrinsic (body-based) motor commands that specify changes in muscle forces and joint angles. This new cortical mechanism not only improved the ability of primates to obtain food items, but it also improved leaping–grasping locomotion and the guidance of limb, hand, and foot movements in support of the diagonal sequence–diagonally coupled (DS–DC) gait described in Chapter 5 (“Branch managers”). Box 12.2 Grasping misconceptions Some rodent researchers have emphasized similarities between primates and rodents as they reach for objects in a laboratory setting. The title of one paper13 reveals a fundamental misconception that underlies their thinking: “Similar hand shaping in reaching-for-food (skilled reaching) in rats and humans provides evidence of homology in release, collection, and manipulation movements” [italics mine]. But as Chapter 2 explained, similarities alone don’t provide strong evidence for homology because they can also arise via homoplasy. The reaching kinematics documented in that study result from constraints imposed on a grasping limb, which are likely to evolve independently in any lineage that developed an ability to reach for and manipulate items. The morphological adaptations that primates use in grasping have no homologs among rodents or other mammalian species that reach, grasp, and manipulate items, such as raccoons. Neither rodents nor racoons (the latter being members of the carnivore order) have the phalangeal, metatarsal, and metacarpal adaptations that underlie reaching and grasping in primates. The more-general point is this: If kinematic similarities evolved independently in primates and in a group of procyonid carnivores (raccoons), as they almost certainly did, then it should be easy to accept that such similarities also evolved independently in rodents. There’s no evidence that the last common ancestor of rats, raccoons, and primates shared the skeletal specializations that underlie reaching and manipulation in primates, and this conclusion also applies to the related neural specializations. When neuroscientists consider only a few modern species and ignore evolution, it’s easy to confuse homoplasies with homologies. Leaping, launching, and landing The hindlimb-dominated, leaping–grasping mode of locomotion not only propelled Eocene primates through the trees during foraging, but it also served as an important predator-avoidance strategy. Because primate leapers both launch and land with their hindlimbs, they generate more force than the forelimbs, especially for extension movements and postural stability.14 In some modern prosimians, leaping serves as an important method for escaping from predators. Partly on this basis, Crompton and Sellers14 (p. 127) proposed that “leaping has . . . been adopted primarily and originally as a predator-avoidance device” [italics original]. Suite success Many of the evolutionary innovations that led to the success of primates involve the neocortex, so the remainder of this chapter explores how cortical maps changed in primates. A suite of new areas augmented the neocortical core, and it seems most likely that they evolved in basal Euprimates: • • • •

Several granular prefrontal areas emerged, including the FEF and other new parts of the prefrontal cortex (abbreviated here as PFo, which refers to granular and dysgranular areas of the orbitofrontal cortex). A transcortical network of posterior parietal and premotor areas, most of them primate specializations, occupied a central part of the core neocortex. A new part of the primary motor cortex (M1) evolved or greatly expanded. Several new visual areas developed in the occipital and inferior temporal cortex.

As primates competed with other primate species in Eocene forests, these cortical specializations provided advantages in a habitat that was poor in illumination but rich in nutrients. Two chapters discuss the primate-specific areas that evolved in Euprimates and helped some of them survive the Eocene. This one does so in the context of paleontology and paleoecology (Part II); Chapter 15 incorporates paleoneurology (Part III) into the discussion. The evidence for primate-specific cortical areas comes from comparative neuroanatomy. Figure 12.1, adapted from Kaas,15 presents cortical maps for three Euarchontoglires: galagos, tree shrews, and rats. The next three sections take up primate specializations in groups: (1) granular prefrontal areas (“In front of the front”); (2) premotor and motor areas, along with visual areas in the posterior parietal cortex (“Reaching for the sights”); and (3) visual areas in the occipital and temporal lobes (“Seeing in the trees”).

Figure 12.1 Cortical maps in selected Euarchontoglires. (A) A graphically flattened cortex of galagos. The cortex is rendered as if all the sulci are “stretched out” to illustrate areas buried within them. Colored shading indicates the new primate areas according to the key in the upper right. (B) Tree shrew cortex. (C) Rat cortex. The thickest red lines bound the primary somatosensory cortex; the next thickest bound other primary sensory areas. The inset box to the right shows surface drawings of the three brains. For abbreviations, see “List of abbreviations: figures” (p. xxiii). Cortical maps adapted from J.H. Kaas, The evolution of the complex sensory and motor systems of the human brain, Brain Research Bulletin 75, 384–90, 2008. Brain drawings by Mary K.L. Baldwin from M.K.L. Baldwin and S.P. Wise, Evolution of frontal cortex and thalamus in primates, in The Cerebral Cortex and Thalamus (ed. W.M. Usrey & S.M. Sherman) 596–607, Oxford University Press, New York, 2024.

In front of the front One of the most important developments during primate evolution was the emergence of a group of new prefrontal areas collectively called the granular prefrontal cortex.16,17,18,19,20,21,22,23 Chapter 11 introduced this topic, including some of the controversies and misconceptions in the literature. Figure 12.1A, the galago map, illustrates new prefrontal areas in primates, including PFo and FEF, which the next two sections take up, in turn.

Over the orb As Figure 12.1 illustrates, some parts of PFo probably emerged before primates evolved. Tree shrews have a small granular area in the appropriate location, which suggests that aspects of PFo evolved in ancestral Euarchontans.24 Additional components of PFo evolved in Euprimates, which, according to Wong and Kaas,25 are likely homologs of rostral parts of area 13 and/or area 14 in anthropoids (sometimes called areas 14r and 13b). Additional PFo areas emerged later, during anthropoid evolution, but I deal with PFo function here because it’s not what’s found in textbooks or in most of the neuroscience literature. The best evidence, which comes from studies of macaque monkeys, is that PFo underlies a very specific function: an assessment of a predicted food’s updated biological value. These valuations are represented in terms of a food’s desirability at a given time, and they are based on two crucial sources of information: current biological needs and the surface appearance of a food item (even if the item cannot be seen and therefore is predicted based on associative memories). Together, these new granular parts of the prefrontal cortex provided Eocene primates with advantages while foraging visually in a dim, cluttered habitat that included both desirable and nutritionally worthless items—all without a fovea. More than most cortical areas, functions have been attributed to PFo that other areas perform. The key data involve impairments in object-reversal tasks. In such tasks, subjects need to choose an object in order to obtain a reward. After learning which of two objects is the correct choice, the experimenters reverse the reward contingency. Then, to obtain a reward, subjects must avoid choosing the previously rewarded object and switch to the alternative. A variant of this task is called the 3-arm bandit, in which the probability of reward associated with each of three pictures changes gradually over a series of trials.26 Historically, an extensive literature involving loss-of-function studies, mainly in macaque monkeys, led to the conclusion that normal performance of both the object-reversal and the 3-arm bandit tasks depends on PFo. Related studies indicate that it also regulates affective behavior, as measured by avoidance of snakes, a predator of primates.27 Unfortunately, all these conclusions were wrong. More recent research28, 29 has revealed that the traditional way of making PFo lesions (i.e., removing it surgically) interrupts many white-matter tracts that terminate elsewhere in the prefrontal cortex. Selective PFo lesions, which leave these fibers intact,28 have no effect on object-reversal tasks, the 3-arm bandit task, or snake avoidance tests. Other parts of the prefrontal cortex mediate these behaviors, and Rudebeck et al.29 identified one of them. They found that the ventrolateral prefrontal cortex (PFvl), which consists of areas 12 and 45, is necessary for normal performance on the 3-arm bandit task. PFo is not. These results are important because a food’s value depends both on its desirability and availability. The former includes a food’s taste, smell, and nutritional content; the latter involves the likelihood of obtaining it. The 3-arm bandit task involves manipulation of reward probability, which corresponds to the likelihood obtaining food. A food item that’s unlikely to be available, perhaps because a competing conspecific is closer to it, has little value to an individual regardless of its quality. The distinction between desirability and availability valuations has direct relevance to anthropoid evolution and their foraging specializations. As explained in Chapter 6, Oligocene and Miocene anthropoids relied on foraging choices made at long distances from food items. They couldn’t see any food when they made their initial foraging choices. Instead, they needed to rely on an estimation of the likelihood that desirable food would be available at some distant location once they arrived. Rudebeck et al.29 found that PFvl improved such choices. As the next chapter explains, PFvl is an anthropoid adaptation,* so nonanthropoid primates (including those that lived before anthropoids evolved) had to make do without PFvl and, presumably, without the information it represents about food availability. Instead, it’s likely that PFo and its desirability representations guided foraging choices in early primates. In the laboratory, the demonstration of PFo’s function depends on the devaluation task.28,30 Macaques with lesions or inactivation of PFo fail to switch choices among objects based on an updated valuation of food items associated with each object. The test involves selective satiation on one of two food items. Prior to satiation, macaques choose objects associated with their preferred food. After consuming their preferred food to satiety, they shift their choice to objects associated with an alternative (nondevalued) food item. Macaques with complete and selective excitotoxic lesions of PFo fail this test; they continue to choose objects associated with their preferred food even though it has less current desirability than the alternative. A crucial finding is that PFo-lesioned macaques perform normally when choosing among visible food items. That is, given a direct, visually guided choice between a devalued and a nondevalued food, macaques (correctly) choose and obtain the nondevalued food. In contrast, given a choice between two objects, one associated with a devalued food and the other associated with a nondevalued food, they fail the test and obtain the devalued food: the kind they’ve just consumed to satiety. The same results follow bilateral amygdala lesions or disconnecting PFo from the basolateral nucleus of the amygdala, so it’s clear that PFo functions in cooperation with the amygdala,31 and the same conclusions apply to the human PFo.32, 33 In summary, recent studies show that PFo’s principal function is: (1) establishing neural representations that link the visual features of nonfood objects with the remembered visual appearance of hidden food items; and (2) linking that information with the food item’s updated desirability, based on current biological needs. PFo thus facilitates the transformation of visual inputs into estimates about the current biological value of hidden or poorly seen food items. Foraging for “hidden or poorly seen food items” describes the life of Eocene primates, or at least a big part of it. In the clutter of the terminal-branch milieu, clusters of leaves and branches often blocked their view of valuable objects, which in dim illumination were difficult to see even without obstructions. Chapter 15 (“Visions of value”) incorporates fossil evidence into these ideas. Oculomotor oversight Another granular prefrontal area, the FEF, plays a crucial role in visual search and attention, which also provided important advantages in Eocene forests. The evolution of the FEF has been controversial, but recent work in tree shrews has resolved most of the key issues. A homolog of the FEF has been claimed for both rodents34 and domestic cats,35 but findings from tree shrews demonstrate that whatever oculomotor areas those species have, they’re not homologs of the FEF in primates. A meticulous intracortical microstimulation study by Baldwin et al.36 confirmed previous work37,38: Tree shrews lack an FEF. These investigators

searched explicitly and extensively for a homolog of the FEF in the expected area (and elsewhere in the frontal cortex), but they found no region from which they could evoke eye movements with intracortical microstimulation, despite using a broad range of current intensities and both short- and long trains of current pulses. The absence of an FEF in tree shrews indicates that the FEF is a primate specialization. Chapter 11 explained the basis for that conclusion, which results from the fact that tree shrews are much more closely related to primates than either rodents or carnivores are (Figure 3.3). In contrast to tree shrews, the FEF is a clearly identifiable part of the granular prefrontal cortex in every primate species studied to date. Most data come from galagos, owl monkeys, cebus monkeys, squirrel monkeys, marmosets, and macaques.39,40,41,42,43,44,45 Among the FEF’s most conspicuous features, the FEF has significant connections with both the dorsal and ventral visual streams: an unusual trait among prefrontal areas, which usually have extensive connections with one or the other, but not both. Like many visual areas of cortex, the FEF projects to superficial layers of the superior colliculus. In rodents, the cortex between the primary motor cortex (M1) and the prelimbic cortex has been suggested as a homolog of the FEF (Figure 11.4B),34, 46, 47 although other studies indicate that it represents vibrissae movements.46,48,49,50,51,52,53 Because several premotor areas in macaques have oculomotor representations,41,54,55,56,57 simply evoking eye movements from an area is weak evidence for homology with the FEF. However, even if rodents and cats have oculomotor areas, the evidence from tree shrews indicates that they lack a homolog of the FEF that primates have. There is, of course, another possibility. The absence of an FEF in tree shrews could mean that they have secondarily lost it. But that’s unlikely for several reasons. The loss of an area like FEF would probably be accompanied by ecological conditions that de-emphasized its visual search and attention functions, a prospect that doesn’t apply to tree shrews. They live a semiterrestrial life that includes foraging for insects both at ground level and in trees, often digging out their prey.58 Even the most terrestrial tree shrews retain adaptations for arboreal life, including visual ones. Nothing in their life history points to a decrease in the importance of visual search and attention. Indeed, the organization of the visual cortex in tree shrews59 suggests that vision is very important to these species. A later section (“Seeing the new”), describes the visual areas of tree shrews. Like forward-facing eyes, the FEF is a synapomorphy of primates. In view of its function in visual search and attention60,61,62 and the paleoecology of Eocene primates, it’s possible to propose a specific adaptive advantage that the FEF provided: an enhanced ability to attend to and keep track of valuable items in the terminal-branch niche, which provided individuals with an edge in foraging efficiency. A related function involves resolving individual items of value when many items are crowded into a small fraction of an animal’s field of view. A perceptual phenomenon called pop-out, which depends in part on the FEF, enhances the processing of an item that differs from nearby ones, an aspect of selective attention that is both automatic and fast.60 Chapter 15 (“Contending with clutter”) addresses FEF functions in more detail, taking fossil evidence into account. Reaching for the sights Several new motor areas also evolved in primates (Figure 12.1A). One was a new or vastly enlarged hindlimb representation within the primary motor cortex (M1). It contributed to the control of pedal grasping and provided advantages in leaping– grasping locomotion. In addition, several new posterior parietal and premotor areas emerged. These primate specializations improved the visual control of reaching and grasping movements63 among the discontinuous and mechanically unstable branches of trees. The next two sections address the hindlimb M1 cortex and parietal–premotor networks, in turn. Pedal prehension The conclusion that a new or enlarged hindlimb M1 representation evolved in primates depends on two sets of observations: all primates have a large one and tree shrews don’t. Two cortical mapping studies have led to the conclusion that tree shrews lack an appreciable hindlimb representation in M1. Baldwin et al.36 (pp. 6–7) summarized their results for short-train intracortical microstimulation (ST-ICMS) and long-train stimulation (LT-ICMS) as follows: we combined all sites rostral to area 3a and termed the region as motor cortex (M). Hindlimb movements were not observed using either ST- or LT-ICMS parameters at any tested sites within motor cortex . . . , except for one site in one case . . .

Remple et al.37 reached a similar conclusion, and they also noted something important about corticospinal projections in tree shrews. They reported (p. 149) an “inability to elicit hindlimb movements outside of somatosensory cortex” and that “no corticospinal axons have been identified past the thoracic level in tree shrews . . . ” To reach hindlimb motor neurons, primate corticospinal fibers project to lumbar levels of the spinal cord, but tree shrews lack both an appreciable hindlimb M1 representation and projections from M1 to the lumbar spinal cord. Remple et al. also pointed out that rats, like tree shrews, have an M1 that lacks a prominent hindlimb representation, which suggests that ancestral Euarchontoglires might have had a similarly incomplete M1 representation. It’s not necessary to be absolutist about such matters. Whether tree shrews or rats have a minuscule M1 hindlimb representation or none at all makes little difference. The fact that all primates have a large M1 representation, and that tree shrews and rats don’t, tells us more about cortical evolution. M1 representations enlarge in relation how animals use various appendages. For example, spider monkeys (Ateles) have a large tail representation in M1 because they have prehensile tails. In contrast, rhesus monkeys have much less control of tail movements and, accordingly, have a much smaller tail representation in M1.56 Hominoids have lost both tails and M1 tail representations. Given the importance of pedal grasping to basal primates and most modern ones, it would be surprising if they didn’t have a large M1 hindlimb representation. Given the terminal-branch niche that Eocene primates inhabited, the advantages of improved cortical control of the hindlimbs and feet are obvious.

Stimulating findings Figure 12.1A illustrates six premotor areas and two posterior parietal regions in galagos. The best-studied outgroups of primates, rodents and tree shrews, have fewer premotor areas and a much smaller and simpler posterior parietal cortex (Figure 11.3), as Chapter 11 (“To have and have not”) discussed in more detail. Cortical stimulation studies have demonstrated that interconnected posterior parietal and premotor areas represent action maps with a variety of functions: bringing food to the mouth; defensive behaviors; reaching and grasping; and movements of the head and eyes that reorient the retina.64, 65 In the present context, an action map is a specialized kind of memory, in which a network of cortical neurons stores the motor commands for a series of coordinated movements. This concept is somewhat uncommon in neuroscience, but it’s just the motor analog of a common kind of sensory memory. People remember patterns of visual inputs that play out over time and can replay them from memory in the form of visual imagery: like the “vision” in the epigraph of this chapter. Similarly, a sequence of motor outputs stored in memory can be as information rich as visual images and, likewise, play out over time. Most work on action maps has been conducted on anthropoids, specifically macaques64, 65 and squirrel monkeys,66 but similar findings have been reported for galagos67, 68, tree shrews,36 and, to a lesser extent, rats (Figure 12.2).36, 69 The most important findings from these studies are that the action maps in tree shrews don’t resemble those of primates very closely, and those in rats even less so.

Figure 12.2 Action maps in selected Euarchontoglires. The effects of long-train intracortical microstimulation in rats (A), tree shrews (B), and galagos (C). Note that S1 in rats is homologous to area 3b in tree shrews and galagos. Abbreviations for areas: 1–2, 3a, 3b, components of the somatosensory cortex; A1, primary auditory cortex; FEF, frontal eye field; M1, primary motor cortex; MT, middle temporal visual area; PM, premotor cortex; M2, supplementary motor area; PP, posterior parietal cortex; PPr, rostral posterior parietal cortex; S1, primary somatosensory area; S2/PV, lateral somatosensory areas; SC, caudal somatosensory area; V1, primary visual cortex. Adapted from M.K.L. Baldwin, D. F. Cooke, and L. A. Krubitzer, Intracortical microstimulation maps of motor, somatosensory, and posterior parietal cortex in tree shrews (Tupaia belangeri) reveal complex movement representations, Cerebral Cortex 27, 1439–56, 2017.

In galagos, action maps are plentiful in three regions of cortex: the posterior parietal cortex, the premotor cortex, and M1 (Figure 12.2C). In contrast, tree shrews have many fewer (Figure 12.2B). They lack most of the premotor areas observed in

primates, but they have a homolog of M1, and intracortical stimulation can evoke some complex movements from there. Baldwin et al.36 evoked some similar movements from stimulation of the posterior parietal cortex of tree shrews, but only rarely and only from its most rostral part. They concluded that a nascent network of action representations predated the divergence of tree shrews and primates. However, most of the posterior parietal–premotor network evolved in primates. Rats, in contrast, have even fewer posterior parietal and premotor areas than tree shrews, but it’s possible that they also have action maps in their M1 cortex.36, 69 Even so, it would be a mistake to equate action maps in primates with those in nonprimates: a version of the “have it” fallacy (Chapter 11, “To have and have not”). The parietofrontal network in all primates, including prosimian galagos, is much more complex than in any nonprimate mammal,70 with a much more diverse set of action representations distributed over a large set of premotor and posterior parietal areas (Figure 12.2C). As Kaas et al.66 (p. 37) concluded: the posterior parietal cortex in primates “is a region that has changed greatly from that of nonprimate ancestors, judging from extant rodents and tree shrews.” The relevance of action maps to primate ecology was apparent as soon as Graziano and his colleagues discovered them in macaque monkeys,65 and they relate to the concept of visual affordances: the potential actions available to an animal based on what it can see. Affordances include objects or parts of an object that can be grasped, as well as branches of trees that can support locomotion. By using cortically stored memories to guide foraging, locomotion, and defense, the actions represented by interconnected premotor–parietal networks promoted the survival of Eocene primates.

Figure 12.3 Transcortical networks. (A) Selected connections in rats, tree shrews, and galagos. (B) A summary of the relevant connections and areas in basal Euarchontoglires and primates. Brown arrows indicate connections common to rats and galagos. Orange arrows indicate the primitive Euarchontoglires condition or rodent specializations: direct projections from primary and secondary visual and auditory areas to the medial agranular cortex (AGm), also known as the rostral motor area. Red, green, and purple connections, along with areas labeled in blue font, indicate primate specializations. (A) Adapted from M.S. Remple et al., The organization of frontoparietal cortex in the tree shrew (Tupaia belangeri): II. Connectional evidence for a frontal–posterior parietal network, Journal of Comparative Neurology 501, 121–49, 2007; (B) Adapted from L. A. Krubitzer, In search of a unifying theory of complex brain evolution, Annals of the New York Academy of Sciences 1156, 44–67, 2009.(A) © 2007 Wiley-Liss, Inc. (B)© 2009 New York Academy of Sciences

Figure 12.3A contrasts parietofrontal networks in the same three Euarchontoglires illustrated in Figure 12.2, and Figure 12.3B illustrates the augmentations of this network that evolved in Euprimates. Evidence from modern primates shows that premotor and parietal areas function together via these networks. In galagos, for example, a region in the posterior parietal

cortex that represents grasping movements has connections with a premotor-cortex region with representations of the same type.71 So, when a galago grabs some grub, interconnected posterior parietal, premotor, and primary motor modules guide the requisite movements. An important difference between primate and tree shrew networks involves the dorsal frontal area in the latter, labeled DF in the tree shrew part of Figure 12.3A. In all primates studied to date, visual areas in the posterior parietal cortex have dense interconnections with the dorsolateral and dorsomedial prefrontal cortex, extending far rostral to the motor areas. In contrast, parietal visual areas of tree shrews project only to motor areas, not to more rostrally situated cortex.38 Taken together, these findings indicate that a transcortical parietal–prefrontal network is another primate-specific trait. Seeing in the trees A comparison of the visual cortex in galagos, tree shrews, and rats indicates that several new visual areas evolved in primates (Figure 12.1). (Chapter 11 dealt with objections to that conclusion.) Some sciuriform rodents, especially diurnal ones, have a visual cortex more similar to tree shrews, and some other taxa, such as fruit-eating bats (Megachiroptera) and carnivores, have also evolved several new visual areas convergently.59, 72 In primates, new visual areas contributed to the emergence of a large temporal lobe and the lateral sulcus, which distinguish primate brains from those of other mammals (Figure 1.1). Seeing the new The new visual areas in primates include the posterior parietal areas discussed in the previous section. Additional ones include a dorsomedial visual area (DM) and a suite of areas that represent visual motion: the middle temporal (MT) complex, or at least most of it. The best evidence is that area V3 is also a primate specialization. Rodents probably lack homologs of all these areas (despite having an area sometimes called V3), and tree shrews lack most of them.59, 73, 74 A V3-like area did, however, evolve convergently in carnivores, as did an MT-like area. Lyon et al.59 concluded that tree shrews have about eight visual areas. Some controversy on this point centers involves MT. Although some authors have claimed that rodents have a homolog of MT, the evidence is unconvincing. The same goes for tree shrews, although it has more plausible candidates.10, 75 Figure 12.1B illustrates a tree shrew area called TD, which is in the expected location relative to V1 and V2. It’s significantly myelinated, and, like MT in anthropoids (but not in galagos), it projects to the FEF.76 However, it’s not separated from V2 by other extrastriate visual areas, as MT is in primates, and it’s more likely that TD is homologous to one of the other extrastriate areas in primates, most likely the dorsolateral visual area (DL), which the galago map in Figure 12.1A illustrates. On balance, the arrangement seen in all modern primates—a myelin-dense, motion-representing MT that receives direct inputs from V1 and V2—is probably unique to primates. In addition, MT receives input from the middle inferior pulvinar (PIm), which has direct retinal inputs and separates two other sets of pulvinar nuclei, both of which receive tectal inputs. Other mammals have a pulvinar-like nucleus that receives retinal input, but in tree shrews and other nonprimate mammals this nucleus is not clearly segregated from the nuclei that receive tectal inputs, as it is in primates.75, 77 MT and its satellite areas (MST, FST, and MTc) provide the posterior parietal and premotor cortex with information about where motion is occurring in the visual field, something of obvious importance to Eocene primates. Such information would have provided advantages in detecting predators, for example. Chapter 6 (“The prime of plesiadapiforms”) explained that large predators were absent during most of the Paleocene but emerged during the late Paleocene and Eocene. Some were adept at climbing into trees to obtain a meal, and others could fly (Box 12.1). These predators needed to move into the arbor of trees to prey on Eocene primates, at least at some point during their hunt, so improvements in detecting such movements were especially advantageous. Information about the motion of objects would also have contributed to the accuracy of reaching and grasping movements, which was especially challenging on an unstable substrate of fine branches. It’s common to emphasize predation on insects when considering motion detection, but in the terminal-branch niche just about everything moves relative to the retina and the animal’s body. Flowers, fruit, and leaves located on thin, flimsy branches move in response to forces exerted by a foraging primate itself and by wind. Specialized visual representations in the MT complex improved the detection of such movements. The proliferation of new areas enabled primates to develop several specialized kinds of neural representations. Relatively small visual areas, which have neurons that represent more visual space than large areas, have local connections that promote the detection of a given combination of visual features virtually anywhere in the visual field. This development proved to have far-reaching consequences, as Chapter 15 explains. Among them, it established a wide variety of visual representations, empowering primates to perceive the world and establish visual memories that are different from those of other mammals. Changing the old In addition to the new visual areas, evolutionary changes occurred in old ones. For example, V2 has a simpler organization in tree shrews than in galagos and anthropoids. In primates, V2 has a stripe-like organization, with interdigitating columns of cortex specialized for representing color, shape, and ocular disparity. The V2 cortex of tree shrews lacks these traits, as does its homologs in other mammals. V2 also has a higher density of neurons in primates than in other mammals.10 In addition to changes in V2, primates have a relatively large V1: more than double the area predicted from body size, with a magnified representation of the central 10° of visual space and more than three times the density of neurons compared to other mammals. These specializations enable the representation of high-acuity visual information with an emphasis on central vision. Because V1 is larger than in other mammals, a given column of neurons represents a smaller part of the visual field. Such high-acuity vision accompanies a more restricted spread of dendrites than in other cortical areas.78 Importantly, this principle holds both for primates with a fovea (haplorhines) and in those without one (strepsirrhines). In layer 3 of V1, cells within dense concentrations of cytochrome oxidase, commonly known as “blobs,” receive LGN inputs conveying K-type signals, which tree shrews lack. Columns of similar orientation selectivity—encoding the angles of branch-like stimuli, for example—occur in V1 of both primates and tree shrews, but not in rodents. Orientation columns

therefore seem to be a Euarchontan innovation, which evolved convergently in carnivores (as did “blobs” and other visual specializations of primates).79,80,81 Ocular-dominance columns, which alternate inputs from the left and right eyes, occur in most primate species, but not in tree shrews or rodents, so they seem to be another primate innovation.10 This aspect of columnar organization probably reflects the importance of stereoscopic vision to primates, another exploitation of their forward-facing eyes and large binocular field. Chapter summary Ancestral primates lived in dense forests, which dominated the Paleocene and Eocene from the equator to very high latitudes. These animals lived and foraged in dim light because only a small amount of light can penetrate the forest canopy. From their Euarchontan ancestors, early primates inherited a form of S-cone opsin tuned to the blue–purple light that dominates the understory of dense forests. A life of foraging in dim-light conditions helped these animals avoid predators, but it made many demands on their visual system. Two visual adaptations contributed to their success: (1) improved visual sensitivity, which depended on a large number and concentration of rod receptors in the retina; and (2) high-acuity visual signals from P-type retinal ganglion cells, first to the LGN and then to the visual cortex. Like modern strepsirrhines, most Eocene primates lacked a fovea and had dichromatic vision. Eocene anthropoids had foveal vision, but retained dichromacy. A new or greatly enlarged hindlimb M1 cortex, the FEF, new visual areas, several new premotor and posterior parietal areas, and the granular PFo probably emerged early in primate evolution. Natural selection favored the cortical representations encoded in these areas because they improved foraging efficiency and predator avoidance. More specifically, these neocortical areas provided Eocene primates with advantages via: (1) An improved ability to control reaching, grasping, and eye movements in an extrinsic, visual frame of reference (posterior parietal–premotor networks). (2) Improved postural stability gained by gripping branches with the feet while foraging with the mouth and hands; and more accurate control over foot and hindlimb muscles while approaching a perch during leaping–grasping locomotion (new or enlarged M1 hindlimb representation). (3) Enhanced use of visual information for finding, tracking, and maintaining attention on food items and predators in dim light (FEF and new visual areas). (4) Improvements in evaluating the desirability of hidden or poorly seen food items based on their association with nonfood objects and their remembered visual appearance (PFo). This chapter’s epigraph comes from the Heart of Darkness, in which the forest wilderness symbolizes the immutable horrors of inhumanity. But forests are not immutable; they change as the climate does, and the next chapter explores what happened to primates, their cortex, and forests as the atmosphere cooled and dried from the Oligocene onward. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31.

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Whisker movements evoked by stimulation of single pyramidal cells in rat motor cortex. Nature 427, 704–10 (2004). 50. Colechio, E.M. & Alloway, K.D. Differential topography of the bilateral cortical projections to the whisker and forepaw regions in rat motor cortex. Brain Structure and Function 213, 423–39 (2009). 51. Halley, A.C., Baldwin, M.K.L., Cooke, D.F., Englund, M., & Krubitzer, L.A. Distributed motor control of limb movements in rat motor and somatosensory cortex: the sensorimotor amalgam revisited. Cerebral Cortex 30, 6296–312 (2020). 52. Smith, J.B. & Alloway, K.D. Rat whisker motor cortex is subdivided into sensory-input and motor-output areas. Frontiers in Neural Circuits 7, 4 (2013). 53. Tandon, S., Kambi, N., & Jain, N. Overlapping representations of the neck and whiskers in the rat motor cortex revealed by mapping at different anaesthetic depths. European Journal of Neuroscience 27, 228–37 (2008). 54. Fujii, N., Mushiake, H., & Tanji, J. Intracortical microstimulation of bilateral frontal eye field. Journal of Neurophysiology 79, 2240–44 (1998). 55. Fujii, N., Mushiake, H., & Tanji, J. Distribution of eye- and arm-movement-related neuronal activity in the SEF and in the SMA and pre-SMA of monkeys. Journal of Neurophysiology 87, 2158–66 (2002). 56. Mitz, A.R. & Wise, S.P. The somatotopic organization of the supplementary motor area: intracortical microstimulation mapping. Journal of Neuroscience 7, 1010–21 (1987). 57. Moschovakis, A.K., Gregoriou, G.G., Ugolini, G., Doldan, M., Graf, W., Guldin, W., Hadjidimitrakis, K., & Savaki, H.E. Oculomotor areas of the primate frontal lobes: a transneuronal transfer of rabies virus and 14C-2-deoxyglucose functional imaging study. Journal of Neuroscience 24, 5726–40 (2004). 58. Agarwala, B.K. Notes on the distribution, habitat, and behavior of northern tree shrew Tupaia belangeri (Mammalia: Scandentia: Tupaiidae). Journal of Threatened Taxa 7, 6841–2 (2015). 59. Lyon, D.C., Jain, N., & Kaas, J.H. Cortical connections of striate and extrastriate visual areas in tree shrews. Journal of Comparative Neurology 401, 109–28 (1998). 60. Schall, J.D., Zinke, W., Cosman, J.D., & Schall, M.S. On the evolution of the frontal eye field: comparisons of monkeys, apes, and humans. In: Evolution of Nervous Systems (ed. J.H. Kaas & L.A. Krubitzer) 4, 249–75 (Elsevier, 2017). 61. Passingham, R.E. Understanding the Prefrontal Cortex: Selective Advantage, Connectivity, and Neural Operations (Oxford University Press, Oxford, 2021). 62. Passingham, R.E. & Wise, S.P. The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, and the Origin of Insight (Oxford University Press, Oxford, 2012). 63. Wu, C.W., Bichot, N.P., & Kaas, J.H. Converging evidence from microstimulation, architecture, and connections for multiple motor areas in the frontal and cingulate cortex of prosimian primates. Journal of Comparative Neurology 423, 140–77 (2000). 64. Graziano, M.S., Taylor, C.S., & Moore, T. Complex movements evoked by microstimulation of precentral cortex. Neuron 34, 841–51 (2002). 65. Graziano, M.S.A. Ethological action maps: a paradigm shift for the motor cortex. Trends in Cognitive Sciences 20, 121–32 (2016). 66. Kaas, J.H., Qi, H.X., & Stepniewska, I. The evolution of parietal cortex in primates. Handbook of Clinical Neurology 151, 31–52 (2018). 67. Stepniewska, I., Gharbawie, O.A., Burish, M.J., & Kaas, J.H. Effects of muscimol inactivations of functional domains in motor, premotor, and posterior parietal cortex on complex movements evoked by electrical stimulation. Journal of Neurophysiology 111, 1100–19 (2014). 68. Stepniewska, I., Fang, P.C., & Kaas, J.H. Microstimulation reveals specialized subregions for different complex movements in posterior parietal cortex of prosimian galagos. Proceedings of the National Academy of Science USA 102, 4878–83 (2005). 69. Brown, A.R. & Teskey, G.C. Motor cortex is functionally organized as a set of spatially distinct representations for complex movements. Journal of Neuroscience 34, 13574–85 (2014). 70. Krubitzer, L.A. & Padberg, J. Evolution of association pallial areas: parietal association areas in mammals. In: Encyclopedic Reference of Neuroscience (ed. A.B. Butler) 1225–31 (Springer, Berlin, 2009). 71. Stepniewska, I., Friedman, R.M., Miller, D.J., & Kaas, J.H. Interactions within and between parallel parietal-frontal networks involved in complex motor behaviors in prosimian galagos and a squirrel monkey. Journal of Neurophysiology 123, 34–56 (2020). 72. Kaas, J.H., Krubitzer, L.A., & Johanson, K.L. Cortical connections of areas 17 (VI) and 18 (VII) of squirrels. Journal of Comparative Neurology 281, 426–46 (1989). 73. Bryant, K.L. & Preuss, T.M. A comparative perspective on the human temporal lobe. In: Digital Endocasts (ed. E. Bruner, O. Emiliano, & T. Naomichi) 239–58 (Springer, Japan, 2018). 74. Rosa, M.G.P. & Krubitzer, L.A. The evolution of visual cortex: where is V2? Trends in Neurosciences 22, 242–8 (1999). 75. Baldwin, M.K.L., Balaram, P., & Kaas, J.H. The evolution and functions of nuclei of the visual pulvinar in primates. Journal of Comparative Neurology 525, 3207–26 (2017). 76. Krubitzer, L.A. & Kaas, J.H. Cortical integration of parallel pathways in the visual system of primates. Brain Research 478, 161–5 (1989). 77. Kaas, J.H. & Baldwin, M.K.L. The evolution of the pulvinar complex in primates and its role in the dorsal and ventral streams of cortical processing. Vision (Basel) 4, 3 (2019). 78. Elston, G.N. Specialization of the neocortical pyramidal cell during primate evolution. In: The Evolution of Nervous Systems (ed. T.M. Preuss & J.H. Kaas) 3, 191–242 (Elsevier, New York, 2007). 79. Preuss, T.M. Taking the measure of diversity: comparative alternatives to the model-animal paradigm in cortical neuroscience. Brain, Behavior and Evolution 55, 287–99 (2000). 80. 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* In polymorphic trichromacy, different alleles at an X-linked locus encode the M–L opsin, which produces trichromacy in heterozygous females. That’s the predominant form of vision in platyrrhines, but there are exceptions. Owl monkeys (Aotus) have only one opsin (monochromacy), and howler monkeys (Alouatta) evolved the routine form of trichromacy convergently with catarrhines. * A periodically rediscovered facet of thalamic function is that it’s much more than a simple relay. Every generation of neuroscientists, it seems, “discovers” that complex corticothalamic interactions play a crucial role in brain function. For example, the reticular nucleus of the thalamus receives glutamatergic inputs from the infragranular layers of the cortex and sends inhibitory, GABAergic inputs to thalamic nuclei. These inhibitory inputs affect information flow to and from the cerebral cortex, and this is but one example of functions beyond those of simple relay nuclei. * Some experts maintain that PFo is needed to perform normally on the 3-arm bandit task, but they include part of area 12 in their definition of PFo. Their definition seems reasonable in the absence of evolutionary considerations, but an evolutionary perspective clarifies the issue considerably. The part of area 12 in question, called area 12o

(the ‘o’ stands for orbital), is part of the suite of prefrontal areas that evolved in anthropoids and therefore belongs with the other parts of area 12 in PFvl, not in PFo.

13 Anthropoid adaptations Overview New cortical areas evolved in anthropoids, many of which exploited foveal vision. In the temporal cortex, new visual areas represented feature conjunctions associated with distant resources. New prefrontal areas used this information to improve foraging choices, which reduced the number of unproductive, long-distance foraging journeys that exposed anthropoids to predation. Small parts of the posterior parietal cortex expanded into distinct areas, which represented visual affordances: the actions available to individuals based on what they can see. Together with the premotor cortex, these areas guided movements of the hands, fingers, head, and eyes in fovea-based coordinate frames, which improved reaching, grasping, and manipulating objects, as well as the control of overt attention. Auditory areas represented sounds made by feeding animals and conspecifics, and a new somatosensory area received cutaneous inputs that—via interactions with a specialized part of the primary motor cortex—guided the manipulation of fruit for a tactile assessment of ripeness. Sometimes . . . your life isn’t exactly what you want it to be . . . and sometimes you find yourself doing things you never thought you’d do. . . . But the thing is, nobody said it was going to be fun, at least, nobody said it to me. —Lawrence Kasdan and Barbara Benedek, The Big Chill, Columbia Pictures

Introduction In this chapter, fingers fondle foods; frontal fields flourish; and a Nobel-prize winner strays from his lane. But I begin with the epigraph. In a movie called The Big Chill, a group of college friends gather years after graduation, at a funeral for one of their crew. The quotation comes from an outsider, who wonders why they’re so disappointed with their lives, which they find harder and less fun than college. Hard lives and little fun were the way of the world for our anthropoid ancestors, especially those that lived during the Oligocene and Miocene. It’s difficult to say how much fun earlier anthropoids had, but life was easier during the Eocene. As small animals that lived in trees, Eocene anthropoids had a ready source of fruits, flowers, tender leaves, insects, and sap, along with a perch that provided a degree of protection from predators. Fun isn’t the right word for what they experienced, but a life of safety and satiety has satisfactions of its own. The big chill Then came the big chill, after which anthropoids changed. As the atmosphere cooled and aridified ~34 Ma, ecosystems transformed rapidly. This abrupt climate change, called the Eocene–Oligocene climatic transition (EOCT), created an evolutionary bottleneck that removed a fifth of mammalian diversity,1 and primates were no exception. This chapter discusses cortical maps in the context of anthropoid adaptations to these challenging conditions. Platyrrhines had already populated the New World when the big chill occurred, so they and primates of the Old World experienced the effects of global cooling separately. Afterward, primates disappeared entirely from Europe and North America as forests retreated, and many extinctions also occurred in Asia, Arabia, and Africa. Figure 6.3 shows that strepsirrhines disappeared entirely from Fayum, North Africa at this time, and half of the anthropoid species also died out. It took 4–5 million years for anthropoid diversity to rebound there. Additional periods of global cooling followed the Oligocene, and anthropoids adapted to cooler climes with four interconnected increases in size: of their bodies; of the territory they exploited for food; of their social groups; and of their cortex. The next four sections address these topics, in turn. Beefy bodies In response to the Eocene–Oligocene climatic transition, catarrhines and platyrrhines entered the one-to-several-kilogram range independently (Figure 6.4). By the early Miocene, all small-bodied anthropoids had died out. [Marmosets, tamarins, and owl monkeys seem to contradict that statement, but their small size resulted from a secondary reduction from 1.2–1.3 kg ancestors (Box 2.2, Figure 6.4A).] A large body required diets with a much higher caloric yield than their ancestors had needed. Foraging frugivores Anthropoids adopted several diverse foraging strategies to obtain more calories. The ancestors of colobines adapted to a diet that included leaves, with gastric specializations to digest them. But other Oligocene and Miocene anthropoids had omnivorous diets and relied on fruit as a major source of nutrition. Species that favored frugivory faced several problems. After the early-Oligocene cooling, the seasonality and volatility of fruit production increased markedly. Only a small percentage of trees in a given locale had ripe fruit at any given time, with different tree species fruiting at different times and extensive variation among individual trees of each species. During some seasons, ripe fruit was rare. Their frugivory and large bodies meant that anthropoids needed to forage over a large territory to obtain sufficient nutrition, and they often had to rely on fallback foods during periods of dearth. For example, Assamese macaques forage for flowers from February through April, for young leaves from March through June, for fruits from March through October, and for young bamboo leaves the remainder of the year.2 A larger foraging range led to an exit from the terminal-branch niche favored by their ancestors. Anthropoids made a transition to arboreal quadrupedy, and they used the stout branches of trees as highways (literally and metaphorically) through their forests. This mode of locomotion often entailed changes in elevation, which came at a high cost of energy. At

first, arboreal quadrupedy resulted in relative slow and cautious movements along large branches. Figure 6.6 illustrates the finding that, until the Miocene, femur morphology reflected the initial anthropoid adaptations to arboreal quadrupedy. Later, at about the same time as upward grade-shifts in encephalization and corticalization occurred (Chapter 8), the femur evolved into its modern forms. These adaptations proceeded independently in hominoids, platyrrhines, and cercopithecoids as all three groups adopted new forms of locomotion. No modern anthropoids have retained the ancestral femur morphology, just as no modern anthropoids have retained a prosimian-size cortex. In platyrrhines and cercopithecoids, a speedier form of quadrupedy, called cursorial locomotion, evolved. Many species became terrestrial quadrupeds, which conserved energy at the expense of higher predation risks. In hominoids, suspensory locomotion permitted re-entry into the terminal-branch niche for the smaller species and improved long-distance brachiation in others. Collectively called antipronograde locomotion, changes in the scapula, as well as the femur, accompanied this mode of moving. And, in hominins, bipedal (orthograde) locomotion increased the carrying capacity of individuals at the expense of a yet higher level of energy expenditure. The flow chart in Figure 6.5 highlights additional anthropoid adaptations of both skeletal morphology and behavior, mostly from Cachel.1 It includes only a little about the brain, but it emphasizes two developments related to foraging: the emergence of trichromatic vision (independently in platyrrhines and catarrhines); and a decrease in the relative size of the olfactory bulbs. A later section (“Sights and sounds on the side”) returns to these points. Social circles As a legacy of their haplorhine heritage, Oligocene and Miocene anthropoids usually foraged diurnally or crepuscularly (at dawn and dusk). Along with longer journeys, foraging in daylight increased the risk of predation compared to nocturnal primates. As mentioned earlier, part of their vulnerability resulted from a preference for foods that varied regionally and seasonally, and sometimes failed to materialize at all. The threat of predation provided a strong selective pressure for minimizing risks by reducing unproductive foraging forays. In part to mitigate such threats, anthropoids evolved a variety of social foraging systems, which contrasted with the relatively solitary foraging inferred for basal Euprimates (Figure 15.3).3 Foraging in groups made it more likely that one of its members would detect a predator, but this advantage came at a price: intragroup competition for resources, which many anthropoids partly resolve via strict social hierarchies. Foraging in distant places, but coming up empty, was dangerous for our anthropoid ancestors. A field study of Assamese macaques,2 mentioned earlier, found that they chose their sleeping and resting sites based on two main factors: predator avoidance and proximity to preferred foods. Safety and satiety were also the major concerns of Oligocene and Miocene anthropoids. Social systems contributed to the former but somewhat complicated their quest for the latter. Constructing cortex Along with changes in body size, foraging range, and social systems, new cortical areas emerged during anthropoid evolution, and the cortex also enlarged. The relative timing these two developments is unknown, but fossil evidence says something about the timing of cortical expansion. During the Eocene, the brain and cortex scaled with body size, remaining in the prosimian range of encephalization and corticalization (Figure 8.12). Then, long after the Oligocene cooling of ~34 Ma and the evolutionary bottleneck it caused, an upward grade-shift occurred independently in the three main anthropoid lineages during the Miocene: after ~16–15 Ma in platyrrhines and cercopithecoids; and sometime between ~26 Ma and ~18 Ma in hominoids (Chapter 8, “Miocene monkeys and apes”). Evolutionary changes occurred in all four lobes of the cerebral cortex, but I begin with the frontal lobe for two reasons: Fossil endocasts indicate that early Miocene anthropoids had relatively small frontal lobes (Chapter 8, “Gray-matter grooves”), which expanded later; and new prefrontal areas emerged during anthropoid evolution (Chapter 11, “Frontal fields”). Frontal-field phylogeny Figure 13.1 illustrates the boundary between the frontal and parietal lobes in a selection of Euarchontoglires: the rostral limit of area 3a. Everything to the left of each red line is frontal cortex. Anthropoids have more frontal cortex, relative to the remainder of the cortex, than do tree shrews, rodents, tarsiers, or strepsirrhines, although the latter come close.

Figure 13.1 Relative size of the frontal lobe in Euarchontoglires. Partial cortical maps of selected Euarchontoglires, plotted onto surface views of left hemispheres. Each cortical map is degraded to match the information available for the least-studied group, tarsiers. The diameter of each gray circle is proportional to the mean encephalization quotient (EQ) of each taxon (from Figure 8.1A), although it’s not necessarily the EQ of the example species. Red lines mark the rostral boundary of area 3a, which corresponds

to the boundary between the parietal and frontal lobes. Abbreviations: A1, primary auditory cortex; Aud, auditory cortex; Dys, dysgranular cortex; M1, primary motor cortex; MT, middle temporal visual area; S1, primary somatosensory cortex; V1, primary visual area; V2, second visual area. Galago, tarsier, and owl monkey drawings adapted from P. Wong, C.E. Collins, and J.H. Kaas, Overview of sensory systems of Tarsius, International Journal of Primatology 31, 1002–31, 2010; tree shrew drawing adapted from P. Wong and J.H. Kaas, Architectonic subdivisions of neocortex in the tree shrew (Tupaia belangeri), Anatomical Record (Hoboken) 292, 994–1027, 2009; squirrel drawing adapted from P. Wong and J.H. Kaas, Architectonic subdivisions of neocortex in the gray squirrel (Sciurus carolinensis), Anatomical Record (Hoboken) 291, 1301–33, 2008.

The frontal lobe comprises two groups of areas: motor cortex and prefrontal cortex. The former includes both the primary motor cortex and the nonprimary motor cortex4 (more often called premotor cortex); the latter includes both the granular prefrontal cortex and the agranular prefrontal cortex. Chapter 11 (“Frontal fields”) discussed what the terms granular and agranular mean and don’t mean in this context, but the big picture is this: (1) Anthropoids have more granular prefrontal areas than strepsirrhines; (2) strepsirrhines have more granular prefrontal areas than tree shrews; and (3) rats and mice lack any granular prefrontal areas at all. A recent review5 summarizes the evidence for those conclusions, as well as the history of ideas about the prefrontal cortex. I won’t rehash those arguments here, and it’s not necessary to know the history. It’s enough to know that rats and mice lack homologs of the granular prefrontal cortex, which primates have in abundance. Anthropoid areas Figure 13.2, adapted from Kaas,6 illustrates differences between cortical maps in an anthropoid (owl monkeys) and a strepsirrhine (galagos). It extends Figure 12.1, which contrasts cortical maps in galagos, tree shrews, and rats. To repeat the conclusions from Chapter 12, evidence from comparative neuroanatomy shows that the frontal eye field (FEF, area 8) is a primate specialization, as is most of the granular orbital prefrontal cortex (PFo, most of areas 13 and 14), with some aspects of PFo perhaps dating to ancestral Euarchontans.7

Figure 13.2 Cortical maps of an anthropoid and a strepsirrhine. (A) The neocortex in owl monkeys (Aotus), a New World anthropoid. (B) The galago map from Figure 12.1A. Colored shading indicates the new primate areas according to the key in the lower left . The top key shows colors for new and modified anthropoid areas. For abbreviations, see “List of abbreviations: figures” (p. xxiii) Adapted from J.H. Kaas, The evolution of the complex sensory and motor systems of the human brain, Brain Research Bulletin 75, 384–90, 2008. The green shading comes from D.H. O’Connor, L.A. Krubitzer, and S. Bensmaia, Of mice and monkeys: somatosensory processing in two prominent animal models, Progress in Neurobiology 201, 102008, 2021. Brain drawings by Mary K.L. Baldwin from M.K.L. Baldwin and S.P. Wise, Evolution of frontal cortex and thalamus, in The Cerebral Cortex and Thalamus, (ed. W.M. Usrey & S.M. Sherman) 596–607, Oxford University Press, New York, 2024.(A) © 2007 Elsevier Inc. Waiting for brain drawings

In contrast to the FEF and most of PFo, other granular prefrontal areas are anthropoid specializations. They are located rostral to the FEF, and, as first noted by Preuss and Goldman-Rakic,8 many of them have less myelin than the areas common to both galagos and anthropoids.8,9,10,11 These comparisons indicate that the more-rostral prefrontal areas emerged during haplorhine evolution; and, in view of the small frontal lobe of tarsiers (Figure 13.1),12 they probably evolved in anthropoids. The new anthropoid areas include the polar prefrontal cortex (PFp, area 10), the dorsomedial prefrontal cortex (PFdm, area 9), the dorsolateral prefrontal cortex (PFdl, area 46, sometimes divided into areas called 46 and 9/46 or into areas called 46r and 46c), the ventrolateral prefrontal cortex (PFvl, areas 12 and 45, as well as area 47 in some species), and the rostral part of PFo (area 11). Figure 11.4A illustrates the rough locations of these prefrontal areas in macaque monkeys.*

Figure 13.3 is a companion to Figure 11.4A. Figure 13.3 presents more detail, along with a different way of naming the prefrontal areas; Figure 11.4A is simpler and easier to remember. Figure 13.3 highlights three cytoarchitectonic types of neocortex (Figure 11.5): granular cortex, dysgranular cortex, and agranular cortex. Chapter 11 (“Frontal fields”) explained the meaning of those terms, but it’s worth re-emphasizing that the term “granular” is a misnomer. By convention, the typically layered areas, which are classified as homotypical in other parts of the neocortex, are called granular in the frontal lobe of primates. Dysgranular areas are usually included, as well.

Figure 13.3 Types of frontal cortex in selected Euarchontoglires. For abbreviations, see “List of abbreviations: figures” (p. xxiii) Adapted from T.M. Preuss and S.P. Wise, Evolution of prefrontal cortex, Neuropsychopharmacology 47, 3–19, 2022.

One unresolved issue is whether the anthropoid-specific prefrontal areas emerged before the platyrrhine–catarrhine split. If so, then areas 9, 10, 11, 45, 46, etc. are homologies in New World and Old World primates; otherwise, they are homoplasies. A related issue is whether the anthropoid-specific prefrontal areas emerged before the frontal lobe enlarged during the Miocene. It’s clear that the expansion occurred independently in platyrrhines and in two catarrhine lineages: hominoids and cercopithecoids (Chapter 8, “Miocene monkeys and apes”). So, one possibility is that pre-existing prefrontal areas became larger, with no additions. Alternatively, the expansions might have involved the addition of new prefrontal areas, in which

case they must have evolved independently in platyrrhines, cercopithecoids, and hominoids. And there are innumerable intermediate conditions. As far as I know, there’s no way to rule out any of these possibilities because fossil evidence reveals almost nothing about cortical maps. For example, consider the cortex of ceboid and cercopithecoid monkeys. Ceboids are platyrrhines; cercopithecoids are catarrhines. Most neuroscientists assume that the like-named prefrontal areas in ceboid and cercopithecoid monkeys are homologous, and there’s no evidence to the contrary. However, most neuroscientists also assume that the like-named frontal sulci are homologous, and there’s convincing evidence to the contrary. As Chapter 8 (“Graymatter grooves”) explained, endocasts from fossil crania show that early catarrhines lacked the sulci that characterize the frontal lobe of ceboids and cercopithecoids. There’s simply not enough known about prefrontal areas in any platyrrhine to discern whether their shared features result from homology or homoplasy. Given that it’s an open question, where it’s necessary to take a stand, I’ll assume that a pre-existing suite of homologous anthropoid-specific prefrontal areas expanded during the Miocene, but that’s all it is: an assumption. In Chapter 12, I summarized some functional specializations of the new prefrontal areas that emerged in ancestral primates, integrating the paleoecology summarized in Chapter 6, but withholding conclusions based on fossil evidence. For the new anthropoid prefrontal areas, I defer a discussion of functional specializations to Chapter 16, where I deal with the ecological and fossil evidence together. Here’s a preview: The new prefrontal areas of anthropoids represent behavioral goals, along with the actions that have achieved those goals in the past, and the expected outcomes of those actions based on memory. With the addition of these prefrontal areas to pre-existing reinforcement-learning mechanisms, anthropoids could learn faster and therefore make fewer fruitless foraging choices,13,14,15 which provided a selective advantage in a predatorrich ecosystem with volatile resources. In this instance, the word “fruitless” has both its literal and metaphorical meanings. Shifting synapomorphies To avoid producing a multivolume work, I have perhaps left the impression that cortical evolution in anthropoids involved nothing but the addition of new areas. There’s much more to cortical evolution than that, and both the FEF and the orbital prefrontal cortex (PFo) provide examples. The FEF is homologous among primates, but that doesn’t mean that it’s identical in all primates or that it performs precisely the same functions. Once a new area evolves, selective forces operate on it, and these forces have changed the FEF. One change is reflected in the paucity of projections from the FEF to the superior colliculus (corticotectal projections) in galagos compared to macaques. Using retrograde axonal tracing techniques, Baldwin et al.16 found that corticotectal projections originate from several parts of the granular prefrontal cortex in galagos, but very few neurons in the stimulationdefined FEF have such projections. A follow-up study with anterograde fiber tracing confirmed that finding.17 Earlier studies had suggested a robust corticotectal projection from the galago FEF, but they didn’t define it with microstimulation and therefore probably inadvertently involved adjacent areas in tracer injections. Stepniewska et al.17 (p. 647) concluded that: changes in the connections of the FEF, and therefore visuomotor functions, emerged after prosimian and anthropoid primate lines of evolution diverged. Given the evidence that prosimian galagos more closely resemble early primates overall, it is tempting to suggest that the more pronounced FEF connections with the [superior colliculus] and . . . visual area MT are innovations of the anthropoid line.

Thus, two new features characterize the FEF of anthropoids: enhanced corticotectal projections and pronounced MT connections. The evolution of the fovea might account for both. A true fovea evolved in stem haplorhines, the ancestors of tarsiers and anthropoids. Not only did anthropoids inherit a fovea, but projections from the retina to the brain also changed. Box 13.1 explains that, in anthropoids, the high-acuity (P-type) retinal ganglion cells project only to the thalamus and not to the superior colliculus. Thus, high-acuity visual signals can’t reach the superior colliculus directly from the retina and instead arrive via a corticotectal route, in part from the FEF. By routing the fine-grain visual information through the cortex, the visual signals pick up cortical information along the way, which provides advantages over direct P-type retinotectal projections. In anthropoids, the FEF receives information about behavioral goals from the anthropoid-specific parts of the prefrontal cortex, so it’s able to: (1) represent high-acuity information about a visual stimulus that’s been selected as a goal for action; and (2) transmit this information to the superior colliculus to bias its oculomotor outputs toward the chosen goal, as opposed to other visual stimuli. Because galagos lack the new anthropoid parts of the prefrontal cortex, their FEF has no such information and, accordingly, lacks a major corticotectal projection. Likewise, enhanced connections between the FEF and MT probably reflect top-down attention toward moving visual stimuli that serve as goals, as opposed to other aspects of visual motion. Box 13.1 Changes to retinofugal projections Chapter 12 (“Dining in the dark”) outlined a key specialization of primate retinas: a much higher complement of highacuity P-type retinofugal cells, which outnumber M-type and K-type cells several fold. In most mammals, including strepsirrhine primates, all three types of retinal ganglion cells project to the superior colliculus; but in anthropoids, only the M-type and K-type cells do so. Thus, sometime during anthropoid evolution, P-type retinal outputs switched from targeting both the superior colliculus and the lateral geniculate nucleus of the thalamus (LGN) to targeting the LGN alone.18 As a result, the superior colliculus of anthropoids lacks the direct, high-acuity visual inputs that P-type retinal ganglion cells provide in other mammals. In addition, new parts of PFo play a more direct role in the selection of goals than in the ancestral condition, as revealed by findings about area 11. By inactivating area 13 and area 11 separately, Murray et al.19 discovered that: (1) area 13 needs to be active to update valuations as a monkey consumes a food to satiety; and (2) area 11 needs to be active for a monkey to choose an object associated with the currently most-valued food. Both parts of PFo evolved in primates, but area 11 is

probably an anthropoid specialization. Thus, in anthropoids, it’s likely that a new part of PFo plays a direct role in choosing goals. In summary, anthropoid evolution involved both the emergence of several new prefrontal areas and changes in two older primate specializations, the FEF and PFo. New features also emerged in older areas, including V1 and V2. Chapter 16 examines the adaptive advantages provided by these evolutionary changes. Fruit and fingertips In addition to new prefrontal areas, a specialized subdivision of the primary motor cortex (M1) emerged in anthropoids,20 which human inherited.21 Located in the caudal part of M1 (M1c), it receives cutaneous information22, 23 and projects strongly and monosynaptically to α-motor neurons in the spinal cord.24, 25 This feature contrasts with M1’s outputs in other mammals. For example, rats have only sparse monosynaptic corticospinal projections to motor neurons, if any.26 The shaded column of traits in Figure 13.4 illustrates the variation in such projections among anthropoids.

Figure 13.4 Phylogeny of traits involved in grasping and manipulation. Green circles indicate the presence of a trait (listed at the top, with lines pointing to the relevant column); red circles indicate the absence of a trait; gaps indicate a lack of data. The green bars indicate three independent lineages in which complex manipulation evolved. Adapted from A.B. Goldring and L.A. Krubitzer, Evolution of parietal cortex in mammals: from manipulation to tool use, in Evolution of Nervous Systems, 2nd edition (ed. L.A. Krubitzer and J.H. Kaas) 2, 259–86, Elsevier, New York, 2017.

Figure 13.4 also illustrates the emergence of area 2 and improvements in manual dexterity. According to Goldring and Krubitzer,27 a suite of traits underlying the finely controlled manipulation of objects evolved independently at least three times in anthropoids: in cebus monkeys among platyrrhines; in macaques and their allies (the papionins) among cercopithecoids; and in ancestral hominids. Chapter 5 (“Feeling fingers and other specializations”) explained that early primates evolved a modification of touch receptors on their fingers, especially their fingertips. The loss of surrounding connective tissue made Meissner’s corpuscles exquisitely sensitive. In anthropoids, these cutaneous receptors send inputs to both the somatosensory cortex and to M1c (Figure 13.2A). They signal stretching or deformation of the skin covering the fingertips, including papillary ridges. Anthropoids use this information to sense the softness or hardness of fruit, as well as to control the finger movements involved in manipulating such items. Softness correlates with both ripeness and a high nutritional value, and for many fruits, such as figs, their compressibility indicates ripeness much better than visual cues. As illustrated in Figure 13.5, anthropoids

that spend the most time eating fruits have a higher density of Meissner’s corpuscles on their fingertips than do other anthropoids.28 The metaphor “tactile fovea” refers to the high resolution and cortical magnification of sensory information from the fingertips, much like the central few degrees of visual space. A “tactile fovea” evolved in ancestral primates, and anthropoids repurposed it: improving both foraging choices and the fine control of finger movements.

Figure 13.5 Fingers, “foveas,” and frugivory. Relationship between the density of Meissner’s corpuscles on fingertips and the proportion of feeding time spent on frugivory in a selection of anthropoids. Adapted from J.N. Hoffmann, A.G. Montag, and N.J. Dominy, Meissner corpuscles and somatosensory acuity: the prehensile appendages of primates and elephants, Anatomical Record A: Discoveries in Molecular, Cellular, and Evolutionary Biology 281, 1138–47, 2004.

Changes at the top In addition to new cortical areas at the front of the brain, changes also occurred at the top: in areas with low Brodmann numbers: areas 1–3, 5, and 7 (Box 13.2). Figure 13.2A highlights one low-number parietal area that evolved in anthropoids: somatosensory area 2, which sends cutaneous (among other) inputs to M1c.29 The rarity of a separate area 2 among platyrrhines indicates that it probably evolved independently in platyrrhines and catarrhines. Among catarrhines, area 2 most likely emerged in their last common ancestor, and Figure 11.3 highlights additional somatosensory areas thought to have evolved in anthropoids.27 Box 13.2 Brodmann numbers

Why do areas 1, 2, and 3 have low numbers? The answer is that as Brodmann examined cortical areas in a series of horizontal sections through the human brain, he began at the top and worked his way down. He called the first area that he recognized area 1, the next one area 2, and so forth. Later neuroanatomists have altered his scheme by inserting higher numbers into his dorsal-to-ventral sequence: area 46, for example.

Figure 13.6 Action maps in galagos, squirrel monkeys, and rhesus macaques. (A) A primate chronogram adapted from Figure 4.2. (B) The effects of long-train intracortical microstimulation in three primate species. Adapted from J.H. Kaas, H.-X. Qi, and I. Stepniewska, The evolution of parietal cortex in primates, Handbook of Clinical Neurology: The Parietal Lobe 151, 31–52, 2018.© 2018 Elsevier

Figure 13.6 illustrates the expansion and proliferation of action maps in the posterior parietal cortex of anthropoids. Some of its areas are components of the dorsal visual stream, a transcortical network that relays various conjunctions of places, directions, motions, and speeds to areas that control movements. In addition, many parts of the posterior parietal cortex receive somatosensory inputs: proprioceptive signals that indicate the configuration of the limbs. Through motor adaptation, these inputs come into alignment with visual signals representing the location of the hand. Once aligned, proprioceptive inputs can mediate movements in the dark or in dim light,30 perhaps another adaptation to foraging in rainforests. According to Kaas,31 (p. 45) anthropoid evolution has seen “a further expansion of . . . [the posterior parietal cortex] in Old World monkeys and the larger New World monkeys.” He (p. 45) also concluded that: the caudal part of area 5 . . . seems to have expanded. . . . In addition, the more lateral 7a and 7b regions appear relatively larger, with the 7b region most involved in somatosensory processing and 7a in visual functions.

Chapter 12 explained that in prosimian galagos, both the posterior parietal and premotor areas have action maps that represent a repertoire of motor memories. Figure 12.2 compares action maps in rodents, tree shrews, and galagos.32 In

contrast to the dramatic differences between primates and nonprimates,32 the action maps observed in anthropoids resemble those of galagos,33, 34 but with expansion in anthropoids. Figure 13.6 illustrates action maps in galagos and two anthropoids: squirrel monkeys and rhesus monkeys. These maps reflect an expansion of the posterior parietal and premotor cortex from an ancestral Euarchontan state with a few simple action maps to one with representations that can support the many ways anthropoids interact with objects, foods, and threats, including eye movements that orient the fovea toward items of value (positive or negative). Each kind of action map in Figure 13.6 requires the representation of locations in a specific coordinate frame, one appropriate for a particular kind of movement. One posterior parietal area in anthropoids, the medial intraparietal area (MIP), represents visual targets for reaching.35,36 It transforms the retinal location of a target object into a coordinate system that takes into account the orientation of the fovea and the current hand location.37,38 MIP has dense, reciprocal connections with the dorsal premotor cortex (PMd).35,37,38 Another anthropoid area, the anterior intraparietal cortex (AIP), functions in grasping.39 It represents visual aspects of the target to be grasped, such as its contours, size, and grasp points, and it has dense interconnections with a different premotor area, the ventral premotor cortex (PMv).40 A third anthropoid posterior parietal area, the ventral intraparietal cortex (VIP) represents defensive behaviors, mainly with the arms. Another posterior parietal region, the lateral intraparietal cortex (LIP),41 reorients the fovea via eye movements. To do so, it represents visuospatial targets in a retina-centered coordinate system and uses its dense connections with the FEF42 and superior colliculus43 to control the orientation of the fovea, also known as gaze, foveation, visual fixation, and overt attention. The emergence of the fovea in stem haplorhines changed the ecological significance of eye movements. For instance, it favored a mechanism for maintaining foveation on a moving object, as opposed to eye movements based on the entire retinal field. Posterior parietal areas contribute to that function. On the assumption that galagos more closely resemble the early-primate condition, the color code in Figure 13.6 suggests that small representations in ancestral primates expanded into separate areas in anthropoids: an idea that has implications for the origin of new areas. One example involves the oculomotor region of the posterior parietal cortex. The galago map has a small neural representation for eye movements (orange) within a single area: the rostral posterior parietal cortex (PPr). It seems likely that early primates had a similar arrangement, which proved to be advantageous and therefore became subject to selection. A genetic mechanism that augmented the number of neurons in advantageous areas would increase the oculomotor representation to the point that, over time, a new area emerged in catarrhine anthropoids: the lateral intraparietal area (LIP), as indicated in the rhesus monkey map. And the same goes for MIP, AIP, and VIP, among other posterior parietal areas. Perhaps this principle is a general one, which also applies to the typically layered areas of the frontal and temporal lobes. When a variant within an area has increased sufficiently in size during evolution, it seems like a separate area; otherwise, it’s a subdivision or module within an area, often a small one. In a sense, this idea is a version of the replica-in-miniature theory of cortical evolution, but restricted to a finite set of concretely defined representations, which grounds the theory in a way that avoids its drawbacks. It also circumvents tedious discussions based on a taxonomy of areas, modules, subdivisions, subareas, and clusters of cortical columns. When a set of columns reach a certain size, they no longer seem like a module or subdivision and seem more like an area. The distinction probably isn’t important. What matters are the neural representations involved and their contribution to fitness. Accordingly, throughout this book, I’ve chosen to use the word “area” and not obsess too much about its precise definition. In summary, the posterior parietal cortex expanded greatly during anthropoid evolution. Figures 11.3 and 13.6 show that it’s larger and more complex in anthropoids than in prosimians.27 Accordingly, the new prefrontal areas described earlier (“Anthropoid areas”) aren’t an anomaly; many cortical areas emerged or expanded during anthropoid evolution. Sights and sounds on the side Changes also occurred in the temporal lobe. Figure 13.2A highlights some of them, including new or expanded visual areas in the inferior temporal cortex, which contribute to the ventral visual stream. New auditory areas augmented the superior temporal cortex. Little systematic work has been reported for comparisons of the temporal lobe in anthropoids and prosimians, and these areas remain ill-defined in many respects. Without retinotopic maps to define individual cortical fields, it’s difficult to draw firm conclusions about what happened to the temporal cortex during anthropoid evolution. Despite these difficulties, by contrasting monkeys and prosimian galagos, Kaas18 (p. 1243) concluded that Old World monkeys “have greatly expanded regions of visual cortex in the temporal . . . lobes compared to most New World monkeys and all prosimians.” Chapters 7 and 8 reviewed fossil evidence indicating that the temporal lobe and lateral sulcus emerged and enlarged early in primate evolution,44,45,46 during the early Eocene, before the upward grade-shift of the cortex into the modern prosimian size range. Bryant and Preuss47 (p. 240) concluded that “the anterolateral expansion of [temporal] cortex, which is found in all primate species, is a uniquely primate specialization.” This expansion continued during anthropoid evolution (Figure 13.2A), with an overall trend toward more visual and auditory areas. Despite the remaining uncertainties, it’s clear that the ability of a relatively small cortical area to represent features over a large part of the visual field underlies an important adaptive advantage: an ability to represent new and different combinations of sensory information, at varying levels of complexity. Unfortunately, this property precludes the retinotopic maps that would make these areas easier to define. Chapter 16 pursues this point in the context of fossil evidence summarized in Part III. Here I concentrate on how these areas contributed to foraging choices in the Oligocene and Miocene ecosystems discussed in Part II. Some primates, especially strepsirrhines like ring-tailed lemurs, use olfactory cues to follow fruit odors to their source,48 but this strategy doesn’t work very well for most modern anthropoids, which depend on distant resources. Oligocene and Miocene anthropoids faced the same problem. Not only was it too far to smell food items, but it was also too far to see and evaluate them, at least until after the animals had traversed a significant amount of ground (or tree limbs). Instead, other sensory information guided their initial foraging choices. In savannas and open woodlands, combinations of shapes and colors from landscapes and distant clusters of trees served as signs of resources, along with spatial memories about where

individuals had found food in the past. Clusters of trees, often called stands, were especially important because some of them contained the fruit that anthropoids wanted. The sounds made by feeding animals, including birds and other primates, also provided imported cues about the locations of valuable foods. The shift from local foraging in a terminal-branch niche to long-range journeys established a strong selective pressure for new forms of vision- and audition-based foraging choices, with less reliance on olfaction. The visual information that anthropoids used also changed. Instead of relying exclusively on the identification of nearby food items and nearby objects that obscured them, far-distant patterns of colors and shapes became important. It’s in this ecological context, in part, that new visual, auditory, and polysensory (visual–auditory) areas contributed to the fitness of anthropoids. The trade-off between vision and olfaction has been a common theme of writing on primate evolution, but it’s been somewhat misunderstood, I think. According to the traditional view, vision became more important and olfaction less so from the origin of primates. However, in view of fossil evidence showing that the olfactory bulbs contracted in Miocene monkeys after ~16–15 Ma (Figure 8.4A), a re-examination of the vision–olfaction trade-off is warranted. Accordingly, Chapter 8 (“Brains versus bulbs”) discussed that trade-off in a nontraditional way, one that recognized several phases of olfactory-bulb contraction during primate evolution. Some of it occurred before the advent of haplorhines, as reflected by the smaller olfactory bulbs in strepsirrhines than in closely related Euarchontans (Figure 8.4B). However, most olfactory-bulb contraction happened later (Figure 8.4C). One phase reflected a transition to diurnal foraging in early crown haplorhines, and it accompanied the emergence of the fovea. Later—independently in platyrrhines, cercopithecoids, and hominoids—this phylogenetic trend accelerated (Figure 8.4C). The traditional view emphasizes a trade-off between olfactory-bulb size and visual-cortex size. It contains an element of truth, but as the olfactory bulbs contracted during the middle-to-late Miocene, the prefrontal cortex expanded more than the visual cortex did, at least in monkeys. Accordingly, a valuable line of inquiry might relate Miocene paleoecology to the prefrontal areas that expanded during anthropoid evolution. As forests dried and contracted under the stress of global cooling, middle-to-late Miocene anthropoids faced a significant challenge. The availability of their preferred food items, mainly fruit, became much less predictable than it had been for their Oligocene and early Miocene ancestors. The resulting selective pressures favored the emergence or enlargement of prefrontal areas, which could use visual information to improve foraging choices, a topic that Chapter 16 explores in more depth. Guilt by association Chapter 8 explained that an expansion of the frontal lobe accompanied an upward grade-shift in the size of the anthropoid cortex. The comparative evidence summarized earlier in this chapter (“Anthropoid areas”) points to an enlargement of the granular prefrontal cortex as a major contributor to this expansion. The parietal (“Changes at the top”) and temporal (“Sights and sounds on the side”) cortex also enlarged. Most of these evolutionary changes involved typically layered cortex, which is called homotypical in the parietal and temporal lobes and granular in the frontal lobe (Figure 11.5). For this reason, neuroanatomists have often grouped these areas together as homotypical association cortex. In what follows, I refer to all those areas as typically layered—for reasons explained earlier (Chapter 11, “Frontal fields”). The term association cortex implies more to some neuroscientists than to others. Originally, association areas were contrasted with sensory and motor areas. Eccles,49 for example, proclaimed that 95% of the human cortex consisted of association cortex and only 5% functioned in sensory perception or motor control. However, long before that pronouncement, neuroanatomists and neurophysiologists had realized (as the Nobel-prize-winner Eccles did not) that many of the so-called association areas were better understood as components of either the visual or auditory system. The posterior parietal and MT cortex are components of the dorsal visual stream; inferior and superior temporal cortex are components of the ventral visual stream and auditory system, respectively. This understanding shrunk the remaining association cortex to the prefrontal cortex and a few multimodal or polysensory areas. With so much of the association cortex reclassified as sensory cortex, some neuroscientists rejected the concept of association cortex entirely. Multimodal or polysensory areas, especially ones that combine acoustic and visual information, fired the imagination of some neuroscientists for a while. To these investigators, associations among sensory modalities were the end-all and be-all of advanced cognition. Even the prefrontal cortex was reinterpreted as part of extended sensory systems, characterized by the progressively abstract convergence of inputs from the visual, auditory, somatosensory, olfactory, and gustatory systems.50 However, cortical representations that combine a visual feature with an auditory feature don’t differ in any fundamental way from those that combine a visual feature with another visual feature: they both combine two features into a conjunctive representation. Some areas, such as the superior temporal polysensory area, represent conjunctions of visual and acoustic features; other areas represent conjunctions color and shape: two visual features. The difference between those two types of conjunctive representations is less profound than it might appear on the surface. Although cross-modal representations lack magical powers, the concept of association cortex continues to be prominent in the neuroscience literature, in part because of its linkage with the large, typically layered areas that dominate the human brain. In this context, it’s worth considering why neuroanatomists consider this type of cortex to be typical. Both names for it —eulaminate and homotypical (Figure 11.5)—invoke its typicality. Eulaminate means truly (or properly) layered, and homotypical includes the word typical. However, no one who started their neuroscience career by studying rodent brains, as I did, would consider this type of cortex to be typical. In rodents, most areas have either an agranular or koniocortical cytoarchitecture (Figure 11.5). But early-20th-centrury neuroanatomists didn’t begin by studying rodent brains; they began by studying human brains. Pioneering neuroanatomists such as Campbell, Brodmann, and von Economo considered typically layered areas to be typical because they take up so much of the human neocortex. As Chapter 14 spells out, this type of cortex expanded greatly during hominin evolution. Based on comparisons among modern species, it’s clear that the Euarchontoglires common ancestor had relatively little typically layered cortex. Humans have so much that it dominates the entire cerebrum. So, where did it all come from? The answer, for the most part, is that some of the typically layered areas emerged in primates prior to the strepsirrhine–haplorhine split, and other such areas evolved in anthropoids. The expansion of these areas in hominoids and hominins continued a phylogenetic trend that began much earlier in primate evolution.

For several reasons, interest in the concept of association cortex has intensified recently. All along, the prefrontal cortex didn’t really fit neatly into any classification other than association cortex. It receives inputs from many sensory systems, of course, but only indirectly, and its thalamic inputs don’t belong to a well-defined sensory system, either. Accordingly, the association cortex never really vanished, it merely moved forward—into the frontal lobe. As for the parietal and temporal areas, perhaps they began as parts of the visual or auditory systems but later evolved more generalized functions, as expected for human association cortex. Chapter 17 pursues this idea. To preview that discussion here: (1) Genovesio et al.51 proposed that parts of the posterior parietal cortex changed from representing metric relations, such as more or less of some item, to representing the abstract relations that underlie relational, metaphorical, and analogical reasoning; and (2) Murray et al.52 proposed that parts of the temporal cortex, especially the anterior temporal lobe, changed from representing visual categories to representing the generalizations and categorizations underlying semantic memory. Chapter summary In response to the challenges posed by global cooling at the onset of the Oligocene, anthropoids became larger animals, which foraged for high-calorie foods and traveled long distances to obtain them. They had to cope with a markedly increased volatility of resources. The constancy of their ancestors’ rainforests gave way to seasonal changes in temperate zones, as angiosperm trees and shrubs synchronized their production of flowers and fruits to match the seasons. Climate-related shortfalls in resources were common. The farther and more frequently anthropoids needed to travel to obtain their preferred foods, the more vulnerable to predation they became. Larger body size mitigated the threats from some predators but made anthropoids more desirable prey for others. The development of social groups meliorated predation threats, despite the competition for resources inherent in social foraging. The evolution of hierarchies managed such competition in many anthropoid species, at least to an extent. New, typically layered areas emerged in the frontal, parietal, and temporal lobes of anthropoids. The new prefrontal areas came to occupy a substantial proportion of the frontal lobe, and Chapter 16 addresses their functions. For now, I stress two of the major inputs to the granular prefrontal cortex: from the posterior parietal cortex and from the temporal cortex: •

•

The posterior parietal cortex enhanced the processing of relational, metric information and other metric aspects of the visual world, such as relative distances, relative durations, relative motion, and various visual coordinate frames in which primates control eye movements, reaching, and grasping. The action maps represented by parietal–premotor networks enlarged from an earlier primate condition, and subareas within their posterior parietal cortex expanded into separate anthropoid areas such as AIP, LIP, MIP, and VIP. New and expanded areas in the temporal cortex increased the variety of representations of visual and acoustic stimuli, at varying levels of complexity. Every new area or subarea provided an advantage over the ancestral state because it enabled the cortex of anthropoids to represent and store information that the cortex of their ancestors could not.

Additional specializations include the caudal primary motor cortex (M1c), a part of M1 with new features: predominantly cutaneous inputs and enhanced monosynaptic projections to motor neurons. Together, these new features empowered anthropoids to exploit the enhanced sensitivity of Meissner’s corpuscles that characterizes primates, including improved their control of fine finger movements for the manipulation of fruits and an assessment of their ripeness. In the challenging world of the Oligocene and Miocene—a world characterized by deforestation, resource volatility, and intensified competition—most anthropoids abandoned the terminal-branch niche of their ancestors. Its potential for safety and satiety no longer sufficed. During the Miocene, surviving anthropoids adopted new forms of locomotion, which enabled them to move large distances more quickly, and the cortex expanded about the same time (Chapter 8). Both kinds of evolutionary change—in modes of locomotion and cortex size—occurred independently in platyrrhines, cercopithecoids, and hominoids; and in all three lineages the ancestral forms vanished. The evolution of an ever-larger cortex, which began in late Eocene, continued with another upward grade-shift. For our ancestors, the hominoids, it happened during the early Miocene, and the next chapter chronicles the continuation of this evolutionary trend in Plio-Pleistocene hominins. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

Cachel, S. Fossil Primates (Cambridge University Press, Cambridge, 2015). Liu, G.B., Liu, S., Li, Y., & Huang, Z. Predator avoidance and food patch proximity influence sleeping site use of Assamese macaques in a limestone forest of Southwest Guangxi, China. American Journal and Biological Anthropology 178, 244–56 (2022). DeCasien, A.R., Williams, S.A., & Higham, J.P. Primate brain size is predicted by diet but not sociality. Nature Ecology and Evolution 1, 112 (2017). Wise, S.P. Nonprimary motor cortex and its role in the cerebral control of movement. In: Dynamic Aspects of Neocortical Function (ed. G. Edelman, W.E. Gall, & W.M. Cowan) 525–55 (John Wiley, New York, 1984). Preuss, T.M. & Wise, S.P. Evolution of prefrontal cortex. Neuropsychopharmacology 47, 3–19 (2022). Kaas, J.H. The evolution of the complex sensory and motor systems of the human brain. Brain Research Bulletin 75, 384–90 (2008). Wong, P. & Kaas, J.H. Architectonic subdivisions of neocortex in the tree shrew (Tupaia belangeri). Anatomical Record (Hoboken) 292, 994–1027 (2009). Preuss, T.M. & Goldman-Rakic, P.S. Myelo- and cytoarchitecture of the granular frontal cortex and surrounding regions in the strepsirhine primate Galago and the anthropoid primate Macaca. Journal of Comparative Neurology 310, 429–74 (1991). Burman, K.J., Palmer, S.M., Gamberini, M., & Rosa, M.G.P. Cytoarchitectonic subdivisions of the dorsolateral frontal cortex of the marmoset monkey (Callithrix jacchus), and their projections to dorsal visual areas. Journal of Comparative Neurology 495, 149–72 (2006). Glasser, M.F., Goyal, M.S., Preuss, T.M., Raichle, M.E., & Van Essen, D.C. Trends and properties of human cerebral cortex: correlations with cortical myelin content. Neuroimage 93, 165–75 (2013). Cruz-Rizzolo, R.J., Lima, M.A.X.D., Ervolino, E., Oliveira, J.A.D., & Casatti, C.A. Cyto-, myelo- and chemoarchitecture of the prefrontal cortex of the Cebus monkey. Biomed Central Neuroscience 12, 6 (2011). Wong, P., Collins, C.E., & Kaas, J.H. Overview of sensory systems of Tarsius. International Journal of Primatology 31, 1002–31 (2010). Passingham, R.E. Understanding the Prefrontal Cortex: Selective Advantage, Connectivity, and Neural Operations (Oxford University Press, Oxford, 2021). Passingham, R.E. & Wise, S.P. The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, and the Origin of Insight (Oxford University Press, Oxford, 2012). Wise, S.P. The evolution of the prefrontal cortex in early primates and anthropoids. In: Evolution of Nervous Systems (ed. L.A. Krubitzer & J.H. Kaas) 3, 387–422 (Elsevier, New York, 2017). Baldwin, M.K.L. & Kaas, J.H. Cortical projections to the superior colliculus in prosimian galagos (Otolemur garnetti). Journal of Comparative Neurology 520, 2002–20 (2012). Stepniewska, I., Pouget, P., & Kaas, J.H. Frontal eye field in prosimian galagos: intracortical microstimulation and tracing studies. Journal of Comparative Neurology 526, 626–52 (2018). Kaas, J.H. The evolution of the visual system in primates. In: The New Visual Neurosciences (ed. J. Warner & L. Chalupa) 1233–46 (MIT Press, Cambridge, MA, 2014).

19. Murray, E.A., Moylan, E.J., Saleem, K.S., Basile, B.M., & Turchi, J. Specialized areas for value updating and goal selection in the primate orbitofrontal cortex. Elife 4, e11695 (2015). 20. Kaas, J.H. Evolution of somatosensory and motor cortex in primates. Anatomical Record A: Discoveries in Molecular, Cellular, and Evolutionary Biology 281, 1148–56 (2004). 21. Geyer, S., Ledberg, A., Schleicher, A., Kinomura, S., Schormann, T., Bårgel, U., Klingberg, T., Larsson, J., Zilles, K., & Roland, P.E.. Two different areas within the primary motor cortex of man. Nature 382, 805–7 (1996). 22. Tanji, J. & Wise, S.P. Submodality distribution in sensorimotor cortex of the unanesthetized monkey. Journal of Neurophysiology 45, 467–81 (1981). 23. Strick, P.L. & Preston, J.B. Two representations of the hand in area 4 of a primate. II. somatosensory input organization. Journal of Neurophysiology 48, 150–9 (1982). 24. Rathelot, J.A. & Strick, P.L. Subdivisions of primary motor cortex based on cortico-motoneuronal cells. Proceedings of the National Academy of Science USA 106, 918–23 (2009). 25. Witham, C.L., Fisher, K.M., Edgley, S.A., & Baker, S.N. Corticospinal inputs to primate motoneurons innervating the forelimb from two divisions of primary motor cortex and area 3a. Journal of Neuroscience 36, 2605–16 (2016). 26. Wise, S.P. & Donoghue, J.P. The motor cortex of rodents. In: Cerebral Cortex (ed. A. Peters & E.G. Jones) 243–70 (Plenum, New York, 1986). 27. Goldring, A.B. & Krubitzer, L.A. Evolution of parietal cortex in mammals: from manipulation to tool use. In: Evolution of Nervous Systems (ed. L.A. Krubitzer & J.H. Kaas) 2, 259–86 (Elsevier, New York, 2017). 28. Hoffmann, J.N., Montag, A.G., & Dominy, N.J. Meissner corpuscles and somatosensory acuity: the prehensile appendages of primates and elephants. Anatomical Record A: Discoveries in Molecular, Cellular, and Evolutionary Biology 281, 1138–47 (2004). 29. Jones, E.G., Coulter, J.D., & Hendry, S.H.C. Intracortical connectivity of architectonic fields in the somatic sensory, motor and parietal cortex of monkeys. Journal of Comparative Neurology 181, 291–348 (1978). 30. Shadmehr, R. & Wise, S.P. The Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning (MIT Press, Cambridge MA, 2005). 31. Kaas, J.H., Qi, H.X., & Stepniewska, I. The evolution of parietal cortex in primates. Handbook of Clinical Neurology 151, 31–52 (2018). 32. Baldwin, M.K.L., Cooke, D.F., & Krubitzer, L.A. Intracortical microstimulation maps of motor, somatosensory, and posterior parietal cortex in tree shrews (Tupaia belangeri) reveal complex movement representations. Cerebral Cortex 27, 1439–56 (2017). 33. Stepniewska, I., Gharbawie, O.A., Burish, M.J., & Kaas, J.H. Effects of muscimol inactivations of functional domains in motor, premotor, and posterior parietal cortex on complex movements evoked by electrical stimulation. Journal of Neurophysiology 111, 1100–19 (2014). 34. Stepniewska, I., Fang, P.C., & Kaas, J.H. Microstimulation reveals specialized subregions for different complex movements in posterior parietal cortex of prosimian galagos. Proceedings of the National Academy of Science USA 102, 4878–83 (2005). 35. Kalaska, J.F. & Crammond, D.J. Deciding not to go: neuronal correlates of response selection in a go/nogo task in primate premotor and parietal cortex. Cerebral Cortex 5, 410–28 (1995). 36. Cui, H. & Andersen, R.A. Posterior parietal cortex encodes autonomously selected motor plans. Neuron 56, 552–9 (2007). 37. Buneo, C.A., Jarvis, M.R., Batista, A.P., & Andersen, R.A. Direct visuomotor transformations for reaching. Nature 416, 632–6 (2002). 38. Cisek, P. & Kalaska, J.F. Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45, 801–14 (2005). 39. Baumann, M.A., Fluet, M.C., & Scherberger, H. Context-specific grasp movement representation in the macaque anterior intraparietal area. Journal of Neuroscience 29, 6436–48 (2009). 40. Rizzolatti, G. & Luppino, G. The cortical motor system. Neuron 31, 889–901 (2001). 41. Snyder, L.H., Batista, A.P., & Andersen, R.A. Intention-related activity in the posterior parietal cortex: a review. Vision Research 40, 1433–41 (2000). 42. Bruce, C.J. & Goldberg, M.E. Primate frontal eye fields: I. Single neurons discharging before saccades. Journal of Neurophysiology 53, 603–35 (1985). 43. Paré, M. & Wurtz, R.H. Progression in neuronal processing for saccadic eye movements from parietal cortex area lip to superior colliculus. Journal of Neurophysiology 85, 2545–62 (2001). 44. Gurche, J.A. Early primate brain evolution. In: Primate Brain Evolution: Methods and Concepts (ed. E. Armstrong & D. Falk) 227–46 (Plenum, New York, 1982). 45. Radinsky, L. The fossil record of primate brain evolution. In: 49th James Arthur Lecture on the Evolution of the Human Brain, 1–27 (American Museum of Natural History, New York, 1979). 46. Radinsky, L. Primate brain evolution. American Scientist 63, 656–63 (1975). 47. Bryant, K.L. & Preuss, T.M. A comparative perspective on the human temporal lobe. In: Digital Endocasts (ed. E. Bruner, O. Emiliano, & T. Naomichi) 239–58 (Springer, Japan, 2018). 48. Cunningham, E.P., Edmonds, D., Stalter, L., & Janal, M.N. Ring-tailed lemurs (Lemur catta) use olfaction to locate distant fruit. American Journal of Physical Anthropology 175, 300–7 (2021). 49. Eccles, J.C. The modular operation of the cerebral neocortex considered as the material basis of mental events. Neuroscience 6, 1839–56. (1981). 50. Jones, E.G. & Powell, T.P.S. An anatomical study of converging sensory pathways within the cerebral cortex of the monkey. Brain 93, 793–820 (1970). 51. Genovesio, A., Wise, S.P., & Passingham, R.E. Prefrontal–parietal function: from foraging to foresight. Trends in Cognitive Sciences 18, 72–81 (2014). 52. Murray, E.A., Wise, S.P., & Graham, K.S. The Evolution of Memory Systems: Ancestors, Anatomy, and Adaptations (Oxford University Press, Oxford, 2017).

* Ventromedial prefrontal cortex is a commonly used (and abused) descriptive term. I don’t use it because it’s ill-defined in the neuroscience literature, especially in neuroimaging papers. The parts of the prefrontal cortex that are both ventral and medial include granular areas (parts of areas 9 and 10) and agranular areas (area 25, for instance). Some neuroimaging activations of the “ventromedial prefrontal cortex” or “ventromedial frontal cortex” are entirely within the agranular areas; others are entirely within the granular areas; and many involve various combinations of the two.

14 Human hemispheres Overview A few new areas probably emerged in the human cortex, but the major development was a dramatic expansion of the typically layered areas (more commonly known as homotypical association cortex). Mainly because of their enlargement, the cortex expanded into the modern human size range, mostly in the past 2 million years. A transcortical network that includes connections between temporal and frontal cortex via the arcuate fascicle enlarged and reorganized. In addition, the hippocampus decreased in relative size during most of anthropoid evolution but increased during human evolution, probably in response to new inputs and functions. The genetic mechanisms of cortical expansion include gene duplications that extend neural stem-cell proliferation and keep neurons in an immature state, thus increasing the number of neurons as well as the number of dendritic spines and synapses. A within-gene deletion in the chimpanzee–human common ancestor promoted synaptogenesis in the prefrontal cortex. . . . again I trot on stage the only true villain in my story: the oversized human brain. . . . Just about every adult human being back then had a brain weighing about three kilogrammes! There was no end to the evil schemes that a thought machine that oversized could imagine and execute. —Kurt Vonnegut, Galápagos, 1985, Dial Press

Introduction In this chapter, the hippocampus wanes and waxes; homologies produce homoplasies; and the brain balloons. But I begin with the epigraph. In Vonnegut’s novel Galápagos, a pandemic strands the only fertile humans on the Galapagos Islands. There they adopt an aquatic life, swimming for their food. According to the story, the advantages of a streamlined head in such conditions favored smaller brains, so after a million years the human cortex contracted to a fraction of its current size. Vonnegut exaggerated the mass of the human brain—it’s less than half the 3 kg he supposed—but maybe he was right about the evil schemes. Whether evil, good, or the usual mucking around, human cognition depends on the cerebral cortex. Whole hemispheres By any measure, humans have a much larger brain than any other hominoid. Human brains are ~1,400 cm3, those of gorillas ~500 cm3; chimpanzees ~400 cm3; orangutans ~375 cm3; and gibbons ~85 cm3. The question is: How did the human brain reach that size? At one level, the answer is simple: cortical expansion. Studies of allometry seek a deeper answer by studying the relative growth of various components of the brain and body. Studies of allometry are important, but relative size is not the only measure that’s needed to understand cortical evolution. Absolute size is also significant because it determines the richness and repertoire of cortical representations.1 Chapter 15 (“Representations revisited”) delves into this idea a bit, but an emphasis on absolute size is rare in the allometry literature.2 While reading discussions of cortical allometry, it’s easy to lose sight of the tremendous size difference between the cortex of apes and humans. There’s some evidence, although I consider it weak and unconvincing, that the absolute size of primate brains correlates better with problem-solving ability and behavioral flexibility than measures of relative brain size, such as the encephalization quotient (EQ).3,4 Chapter 15 (“The cortex complete”) pursues this point, but it doesn’t go into comparative psychology in any detail. The reason is that the tasks used to assess animal intelligence are, in my opinion, insufficiently constrained to provide reliable and interpretable results. Put another way, there are so many ways to solve the problems posed by those tasks that it’s impossible to know what an animal is doing to achieve its scores, no matter how large a battery of “IQ” tests an animal takes. This doesn’t mean that the absolute size of the cortex in unimportant for intelligence, it only means that no one really knows how to measure intelligence in animals, despite the fascination with this topic in popular science. Along with our larger brain volume in absolute terms, humans have a larger brain relative to body mass than any other hominids. Humans fall within the body-size range of great apes, but with much larger brains. Chimpanzees and bonobos typically weigh ~40–50 kg; orangutans ~80–90 kg; gorillas ~150–200 kg. As noted earlier, none of their brains exceeds ~500 cm3, but human brains are nearly three-fold larger in a ~60–80 kg body. Figures 8.6 and 8.7 illustrate the change in relative brain size during hominin evolution. Although estimations of EQ values for hominins differ depending on the formula used, the slope of the brain–body mass regression, and whether phylogenetic sampling is considered, the massive expansion of the human cortex dwarfs such details. Area analysis Given the fact that the cortex expanded dramatically during human evolution, two key questions arise: • •

Did new areas appear or did inherited ones expand? If the latter, which areas expanded the most?

This section deals with the first question; the next one (“Sensational size”) deals with the second. Given the evidence that several new cortical areas evolved in anthropoids (Chapter 13), it might seem likely that the same phylogenetic trend continued during hominin evolution. At least one expert believes that it did. Kaas5 (p. 42), citing four sources,6,7,8,9 contrasted prosimians, anthropoids, and humans:

galagos appear to have at least 50 cortical areas . . . and as many as 129 areas have been proposed for macaques . . . Human brains . . . may have as many as 150–200 or more cortical areas . . .

Unfortunately, such estimates depend on weak and sometimes capricious methods for defining cortical areas. One problem is that most of the literature on new human areas depends on contrasts between macaques and humans. Despite the temptation to attribute any such differences to human evolution, macaque–human contrasts might reflect evolutionary changes that occurred earlier in hominoid evolution rather than specifically in hominins. Chapter 11 (“A Declaration of Independence”) emphasized some of the problems with such comparisons. The paucity of neuroanatomical data from apes weakens conclusions about new areas in humans because, without such information, it’s nearly impossible to identify specifically human traits except for brain size and shape: the same kind of information available from fossils. Technical problems have also hampered progress. Cortical mapping studies in monkeys have, historically, depended on different methods than those used in humans, which weakens any comparison. Even when investigators apply the same neuroimaging methods across several species, size differences can lead to different interpretational problems. Except for topographically organized areas, clear-cut definitions of cortical areas are difficult to come by. Architectonic analyses, especially observer-independent forms, provide important information, but the differences among areas are often small and inconsistent. Even for observer-independent approaches, the distinction between variation within an area and variation between areas is rarely as principled as the published maps imply. More promising are studies that concentrate on patterns of axonal connections, sometimes called connectional “fingerprints.” The basic idea is that an area’s overall set of connections is sufficiently conserved to enable the identification of homologies despite some evolutionary changes in its connections. For example, if an area has ten major connections and loses two of them during evolution, then the eight conserved connections can establish a homology. Other methods, such as the distribution of neurotransmitters, receptors, histochemical markers, patterns of gene expression, functional neuroimaging, covariation in neuroimaging signals (coupling), etc., bear on cortical field definition, but it’s rarely obvious how many areas a given region has or where their boundaries are. With these limitations in mind, the weight of the current evidence suggests that the number of cortical areas is generally similar in humans and the larger monkeys of both the New- and Old World, such as macaques and cebus monkeys. For example, cortical parcellation and mapping studies that compare humans and macaques indicate that they have a similar number of prefrontal areas.10,11,12,13 Sallet et al.13 and Neubert et al.12 parcellated the prefrontal cortex by examining coupled activations among cortical voxels. They relied on correlated blood-oxygen-level decreases (BOLD signals), as detected with magnetic resonance imaging (MRI), which probably reflect reasonably direct connections. They concluded that the number and arrangement of prefrontal areas is similar in humans and monkeys, although many differ in size. For example, the lateral part of the polar prefrontal cortex (area 10) is much larger in humans than in macaques14 or cebus monkeys.15 Likewise, Balsters et al.16 studied corticostriatal “fingerprints,” which depend on coupled activations between cortical and striatal voxels. By contrasting macaques, humans, and mice, they found that the largest differences between humans and macaques involves the lateral part of the polar prefrontal cortex and its projections to the rostral caudate nucleus. Otherwise, macaques and humans have a similar set of prefrontal areas, which dramatically differ from those in mice. The lateral part of the polar prefrontal cortex might be a new area in humans, or it might exist as a smaller area in other anthropoids.15 Regardless, it’s one of the regions where substantial evolutionary change occurred during human evolution, much of it after our divergence from panins.14 One region where new areas seem to have evolved is the medial prefrontal cortex. Compared to macaque monkeys, humans have more subdivisions of the prelimbic cortex (area 32),17 which is often considered to be part of the anterior cingulate cortex and is sometimes called the pregenual cortex. The basic picture, however, is that relatively few new cortical areas emerged during hominin evolution. The current literature is full of uncertainties, but there’s reason to expect that studies of connectional “fingerprints” will clarify the matter in the next few years. Existing evidence suggests that even the areas underlying the most distinctively human behaviors— such as a species-specific theory of mind,18 autobiographical and cultural memory,19, 20 and multiple-demand cognition21— have homologs in macaque monkeys. Such findings indicate that the areas in question (but not necessarily their functions) have descended from ancestral anthropoids. The parenthetic phrase in the previous sentence is important because an area’s functions can change during evolution, especially when it receives inputs from newly evolved or elaborated brain structures. Chapter 17 returns to this point. Sensational size Prefrontal passions Given the conclusion that the human cortex expanded without adding many new areas, the second question comes to the fore: Which cortical regions, if any, enlarged most during hominin evolution? Since the time of Brodmann in the early twentieth century, most expert opinion has held that the prefrontal cortex in humans is larger than expected for a primate, an anthropoid, a hominoid, or a hominid. However, some experts have challenged this idea.22,23,24,25,26 Here’s what the disagreements are about: different expectations about the size of the human prefrontal cortex relative to other parts of the cortex (or the brain, or the body). Here’s what the disagreements are not about: the fact that the volume of the human prefrontal cortex is several-fold greater than in any other primate, including our closest relatives. One issue is whether the prefrontal cortex has enlarged disproportionately compared to other areas (or to the cortex as a whole). In isometric scaling, also known as proportionate or linear scaling, ratios remain constant and the differences between large and small areas increase. For instance, if the volume of the prefrontal cortex in an ancestral species was ten times that of some reference area, then it remains 10-fold larger when the whole cortex doubles in size in a descendant species. Let’s say, to use round numbers, that the prefrontal cortex in an ancestor was 100 cm3 and that of the reference area was 10 cm3. That’s a ratio of 10:1 and a difference of 90 cm3. Now imagine that during evolution the whole cortex doubled in volume, and the areas expanded proportionately. In the descendant species, the prefrontal cortex would be 200 cm3 and that of the reference area would be 20 cm3. The ratio would remain 10:1, but the difference would be twice as much as in the

ancestral condition: 180 cm3 versus 90 cm3. The prefrontal cortex would have to be much greater than 200 cm3 in the descendant species to provide evidence for selective, disproportionate, nonlinear expansion of the prefrontal cortex during evolution.

Figure 14.1 Prefrontal predominance in humans. (A) Granular prefrontal cortex as a percentage of the frontal lobe in modern primates. (B) Relationships among the species included in Part A, matched for color. (C) Same data as in Part A, plotted in the format of Part D. (D) The volume of prefrontal cortex versus the remaining cortex. Dashed lines show confidence limits. (A, C) Adapted from G.N. Elston et al., Specializations of the granular prefrontal cortex of primates: implications for cognitive processing, Anatomical Record A: Discoveries in Molecular, Cellular, and Evolutionary Biology 288, 26–35, 2006. (D) Adapted from R. A. Barton and C. Venditti, Human frontal lobes are not relatively large, Proceedings of the National Academy of Sciences USA 110, 9001–6, 2013.

Therefore, according to some authorities, the fact that the granular prefrontal cortex is larger in humans than in other anthropoids has little evolutionary significance. Humans, in their view, have the amount of prefrontal cortex expected for a catarrhine (or anthropoid, or primate) brain expanded to human size. Figure 14.1D comes from a plot published by Barton and Venditti in support of this conclusion.22 Based on their measurements, the human prefrontal cortex falls very near the regression line for catarrhines and well within confidence limits. Other comparative neuroanatomists have rejected such challenges and agree with the traditional view,27,28,29,30,31,32 which is that the prefrontal cortex expanded disproportionately during human evolution. According to this idea, the prefrontal cortex is larger in both relative and absolute terms in humans than in other anthropoids, hominoids, hominids, and hominins. I return to the analysis of Barton and Venditti22 shortly because there are some technical problems with how they drew a boundary between the prefrontal cortex and the remainder of the frontal lobe. Before dealing with these disagreements, however, it’s useful to stress areas of agreement. As mentioned earlier, no one disputes the dramatic enlargement of the human prefrontal cortex in absolute size: in terms of volume, for example, but by any other measure, as well. The vertical reference bar in Figure 14.1D displays a 5-fold increase, and it emphasizes the fact that the human prefrontal cortex is ~3–4-fold larger in volume than in any other hominid species. Figure 14.1A and C come from Elston et al., 33 who reviewed evidence showing that the granular prefrontal cortex takes up ~80% of the frontal lobe in humans, much more than in any other primate, including chimpanzees and gorillas. The analysis of Barton and Venditti doesn’t conflict with this finding in any way. Both groups of authors—as illustrated in Figure 14.1C from Elston et al. and

Figure 14.1D from Barton and Venditti—found a relationship between the total amount of cerebral cortex and the extent of the granular prefrontal cortex. Figure 14.2D illustrates evidence that the prefrontal cortex expanded preferentially, in contrast to the conclusions of Barton and Venditti. Ratios of the volume of prefrontal cortex to a reference area, such as the remainder of the frontal lobe (Figure 14.2A) or the primary visual (striate) cortex (Figure 14.2B), reveal that the prefrontal cortex is disproportionately larger in humans than in other anthropoids or other hominoids.29 Figure 14.2D also shows that the granular prefrontal cortex of chimpanzees has expanded disproportionately relative to nonhominid anthropoids (i.e., anthropoids other than great apes).

Figure 14.2 Expansion of the prefrontal cortex in humans. (A) Prefrontal cortex versus the remainder of the frontal lobe. The dash line is a simple regression based on the cercopithecoid monkeys in the sample and gibbons. (B) Prefrontal cortex versus the primary visual (striate) cortex (V1), in the format of Part A. (C) Evolutionary tree of anthropoids, with brackets encompassing the paraphyletic groups important to the analysis, color-matched to other parts of the figure. (D) Ratios of observed to predicted volume of prefrontal cortex. White bars show ratios for prefrontal cortex versus the remainder of the frontal lobe; black bars show ratios for the prefrontal cortex versus the V1 cortex. R.E. Passingham and J.B. Smaers, Is the prefrontal cortex especially enlarged in the human brain? Allometric relations and remapping factors, Brain, Behavior, and Evolution 84, 156–66, 2014.© Oxford Publishing Limited

A follow-up study confirmed these conclusions. That analysis, which Figure 14.3B illustrates, was an advance on the one depicted in Figure 14.2 because it took phylogenetic sampling into account. Like Figure 14.2A, Figure 14.3B plots the volume of granular prefrontal cortex against the volume of the remainder of the frontal lobe. There is a statistically significant difference between hominids (the great ape–human clade) and other anthropoids, which the phylogenetic tree in Figure 14.3A displays in matching color (brown). Figure 14.3C shows that in the chimpanzee–human clade, the prefrontal cortex expanded to a greater extent than the typically layered areas of the temporal and parietal lobes, although the difference is modest.

Figure 14.3 Preferential expansion of prefrontal cortex. (A) An evolutionary tree of anthropoid primates, emphasizing data in Parts B and C. Lineages in brown show a phylogenetically significant increase in the extent of granular prefrontal cortex versus other parts of the frontal lobe; those in blue show a phylogenetically significant increase in the extent of granular prefrontal cortex versus other typically layered (i.e., homotypical) areas. (B) Volume of granular prefrontal cortex versus other areas in the frontal lobe. The solid line shows the regression through monkeys and gibbons and the dashed lines show confidence limits. Brown data points indicate significant differences as tested with phylogenetic statistics. (C) Volume of prefrontal cortex versus typically layered areas of the parietal and temporal lobes. Blue data points indicate significant differences as tested with phylogenetic statistics. (B, C) Adapted from J.B. Smaers et al., Exceptional evolutionary expansion of prefrontal cortex in great apes and humans, Current Biology 27, 1549, 2017.

Other studies have approached the issue via diffusion-weighted structural neuroimaging, which provides information about myelin density.27, 34, 35 Donahue et al.27 added cytoarchitectonic analysis and MRI of resting-state covariance (coupling) to maps of myelin density. Figure 14.4A renders the lightly myelinated areas in dark blue for humans, chimpanzees, and macaque monkeys: plotted onto an inflated image of the cortex (to expose cortex buried in sulci). Figure 14.4B adds marmosets and two sets of labels. Numbers identify the myelin-rich regions; letters do so for the myelin-poor regions. These studies showed that the human prefrontal cortex (region A) is much larger than in other anthropoids, including chimpanzees, but the primary motor, primary somatosensory, and primary visual areas are only a little larger. In terms of volume, the ratio of the granular prefrontal cortex to the primary motor cortex (M1), for example, is 9.4 in humans, 2.9 in chimpanzees, and 3.1 in macaques.27 Thus, the granular prefrontal cortex expanded dramatically in humans compared to yet another reference area, in accord with the analyses illustrated earlier (Figures 14.2 and 14.3), which were based on brain sections rather than neuroimaging.

Figure 14.4 Myelin density in the cortex of four anthropoids. Myelin-rich and myelin-poor areas based on structural magnetic resonance imaging. (A) The top row shows a lateral view of the cortex; the bottom row shows a medial view. The gray region includes the corpus callosum and subcortical structures on the medial surface of the hemisphere. Measurements of the volume of granular prefrontal cortex depend on the identification of its caudal boundary. The white lines indicate an arbitrary caudal boundary: the genu of the corpus callosum. Blue and purple lines indicate the best expert estimate and the most conservative estimate of that boundary, respectively. (B) Letters mark regions with low myelination: A, granular prefrontal cortex; B, inferior posterior parietal cortex; C, anterior temporal cortex; D, medial posterior parietal cortex; E, anterior insular cortex. Numbers mark regions with high myelination: 1, primary somatic sensorimotor cortex; 2, primary auditory cortex; 3, primary visual cortex; 4, middle temporal cortex; 5, intraparietal visual cortex; 6, retrosplenial cortex. The same color scale applies to all parts of the figure. In the catarrhine brains, brain images are “inflated” to expose cortex in sulci. (A) Adapted from C.J. Donahue et al., Quantitative assessment of prefrontal cortex in humans relative to nonhuman primates, Proceedings of the National Academy of Sciences USA 115, E5183–92, 2018. (B) Reproduced from D.C. Van Essen et al., Cerebral cortical folding, parcellation, and connectivity in humans, nonhuman primates, and mice, Proceedings of the National Academy of Sciences USA 116, 26173–80, 2019.(A) © Quantitative assessment of prefrontal cortex in humans relative to nonhuman primates/PNAS (B) © Cerebral cortical folding, parcellation, and connectivity in humans, nonhuman primates, and mice. David Van Essen/PNAS.

Earlier, I mentioned that there are problems with how Barton and Venditti22 estimated the size of the granular prefrontal cortex. The main one was their use of the genu of the corpus callosum as its caudal boundary, which is a mistake.36 Figure 14.4A illustrates the problem.27 White lines mark the caudal boundary of the granular prefrontal cortex based on the arbitrary criterion that Barton and Venditti used. Purple and blue lines show the caudal boundary of the granular prefrontal cortex based on cytoarchitectonics. Purple lines show a conservative boundary, which corresponds to the least plausible amount of granular prefrontal cortex, and blue lines indicate the most likely boundary. The Barton–Venditti plot in Figure 14.1D, based

on the white lines, reflects a serious underestimate of the volume of the granular prefrontal cortex in humans. That’s why their data point for the human prefrontal cortex falls near the regression line and not well above it, as it should be. There are two other objections to the conclusion that the granular prefrontal cortex expanded preferentially in humans. One arises from the fact that the frontal lobe takes up roughly a third of the cortex in both humans and chimpanzees.26 This finding has led some neuroscientists to question whether the human prefrontal cortex expanded relative to the last common ancestor of humans and chimpanzees. But, as Figures 14.1A, 14.2A and D, and 14.3B confirm, the granular prefrontal cortex expanded at the relative expense of other parts of the frontal lobe, and this point is also easy to appreciate in Figure 14.4. In Figure 14.2D, for example, the prefrontal cortex expanded three-fold more than the remainder of the frontal lobe. So, it takes up a larger proportion of the frontal lobe, as illustrated explicitly in Figure 14.1A. All four analyses confirm that a much greater proportion of the human frontal lobe consists of granular prefrontal cortex than in our closest relatives. Accordingly, the finding that the frontal lobe remained at about a third of the cortex doesn’t contradict the conclusion that the prefrontal cortex enlarged preferentially. The same arguments apply to the parietal and temporal lobes, which have similar proportions in human and chimpanzee brains.37 But, also like the frontal lobe, they have myelin-poor, typically layered regions that expanded during human evolution at the relative expense of other areas in both lobes. Accordingly, a proportionate expansion of the frontal, parietal, and temporal lobes doesn’t contradict the idea that the typically layered areas in each lobe expanded disproportionately relative to other cortical areas in the same lobe or relative to the whole cortex. A final objection comes from studies of neuronal counts and densities. Gabi et al.38 reported that neuronal density decreases rostrally in the human cortex with the same slope as in nonhuman anthropoids. This finding led these investigators to question whether the prefrontal cortex differs in humans versus other anthropoids, at least in terms of neuron counts. In that paper and in a book by Herculano-Houzel,39 the authors assumed that the caudal limit of the granular prefrontal cortex corresponds to the genu of the corpus callosum, as Barton and Venditti did. On this basis, they estimated that the prefrontal cortex has 8% of the neurons in the human brain, like other anthropoids. But as Figure 14.4A illustrates, the use of an arbitrary genu-based boundary leads to a significant underestimate of the extent of the granular prefrontal cortex.27 Accordingly, the granular prefrontal cortex in humans has substantially more than 8% of the 16 billion neurons39 in the human brain. As assessment of how much more will require a neuron-counting study that considers cytoarchitectonic boundaries or some trait similarly diagnostic of the granular prefrontal cortex. Other findings also point to a prefrontal cortex expansion in humans. Some lobules of cerebellar cortex send cerebellothalamocortical inputs to the granular prefrontal cortex; others send such inputs to other cortical areas. Balsters et al.40 found that, in humans, prefrontal-projecting lobules have increased in size relative to other cerebellar lobules. Recently, this kind of analysis was extended to corticostriatal projections.16 It confirmed that the anthropoid-specific areas of prefrontal cortex project to anthropoid-specific regions of the striatum and that they are much larger in humans than in macaques. Typical lamination, atypical expansion As mentioned earlier, the myelin-density maps* in Figure 14.4, combined with cytoarchitectonics, point to expansion of the typically layered areas of parietal and temporal cortex, as well as frontal cortex. These regions include the granular prefrontal cortex (labeled ‘A’ in Figure 14.4B); the inferior parietal (‘B’) and the medial parietal (‘D’) cortex;9, 41 and the temporal cortex (‘C’), especially the anterior temporal lobe and the inferior and superior temporal cortex, the latter of which includes areas involved in decoding speech. The anterior temporal lobe, which plays a central role in semantic memory, has expanded significantly, especially in the rostral and lateral directions.42,43,44 In addition, myelin-poor parts of the anterior insular cortex enlarged during human evolution (‘E’). Graphical–computational methods that align homologs and other landmarks, sometimes called expansion maps, confirm a differential enlargement of typically layered areas in the frontal, parietal, and temporal lobes. Expansion maps for humans versus macaque monkeys45,46,47 include the one replicated in Figure 14.5A.45 It quantifies the spatial distortions needed to maintain alignment of the landmarks and homologs, which shows how much cortical regions would need to enlarge to convert a macaque cortex into a human cortex. (However, that’s not what happened during evolution. Extinct catarrhine and hominoid species were our ancestors, not modern macaques, and that fact becomes important a little later.)

Figure 14.5 Relative expansion of cortical regions. (A) Based on registration of proposed homologs and other landmarks, yellow indicates the largest difference between macaque and human cortex: approximately 30-fold (30x); blue indicates areas with less or no difference. (B) Based on connections of white matter tracts associated with specific cortical voxels, dark blue and magenta areas indicate the largest difference between macaque and human cortex, with magenta indicating the greatest difference (but see Part D for a different perspective). (C) Based on sulcal patterns in fossil endocasts, green areas indicate the largest difference between early humans and modern humans. (D) A graphical explanation of the underestimation of prefrontal cortex expansion in analyses like those in Parts A and B (but not in Part C). The expansion factors are arbitrary and only for heuristic purposes. Assume that the last common ancestor (LCA) of humans and macaques had a granular prefrontal cortex (PF) with a size of 1 in arbitrary units. Fossil evidence shows that the frontal cortex was smaller in middle Miocene cercopithecoids than in modern ones (Chapter 8). If the granular prefrontal cortex doubled (2x) in size during the Miocene grade-shift in cercopithecoids, then what appears to be a 30-fold expansion would have actually been a 60-fold expansion. (A) Adapted from J. Hill et al., Similar patterns of cortical expansion during human development and evolution, Proceedings of the National Academy of Sciences USA 107, 13135–40, 2010. (B) Adapted from R. B. Mars et al., Whole brain comparative anatomy using connectivity blueprints, eLife, doi: 10.7554/eLife.35237, 2018. (C) Adapted from M. S. Ponce de Leon et al., The primitive brain of early Homo, Science 372, 165–71, 2021.

Like the myelin-density maps in Figure 14.4, the expansion map in Figure 14.5A confirms that the large, typically layered areas of frontal, parietal, and temporal lobes are much larger in humans than in macaques. In this map, yellow areas indicate the largest expansions. Figure 14.5B is based on an analysis of white matter tracts and their connectional targets in the cortex, and it leads to similar conclusions.48 In that map, magenta and dark blue colors mark the regions that are larger in humans than in macaques. Figure 14.5C comes from the same study as Figure 8.8.49 It’s based on sulcal patterns in fossil humans, and in that map green regions are larger in modern humans than in early humans. The general agreement among these three different methods reinforces the conclusion that the typically layered areas expanded preferentially during hominin evolution. Of the three expansion maps in Figure 14.5, only Figure 14.5C is a direct evolutionary analysis. As just mentioned, it’s based on a comparison of sulcal patterns observed in fossil Homo species. The other two are based on a comparison between two modern species: rhesus macaques and humans. Because humans didn’t evolve from macaques, to understand the evolutionary significance of maps like those in Figure 14.5A and B and comparisons like those in Figure 14.4, it’s important to consider the last common ancestor of macaques and humans. The expansion maps in Figure 14.5A and B depend on the assumption that modern macaques resemble their last common ancestor with humans closely. However, as Chapter 8 (“Gray-matter grooves”) explained, fossil evidence indicates that the frontal lobe expanded relatively recently in cercopithecoid monkeys (after ~15 Ma), long after their divergence from hominoids. Because of this finding, Figure 14.5A and B probably underestimate the evolutionary enlargement of the prefrontal cortex in humans. Figure 14.5D presents imaginary data to illustrate how a 30-fold difference between modern humans and modern macaques, combined with a 2-fold increase in the prefrontal cortex during cercopithecoid evolution, translates to a 60-fold evolutionary expansion from the last common ancestor of cercopithecoids and humans. Of course, these numbers serve heuristic purposes only; accurate ones will depend on estimating the expansion of the granular prefrontal

cortex during cercopithecoid evolution. For now, we only know that the frontal lobe enlarged, and even for this finding a detailed quantitative analysis remains for the future. The point made by Figure 14.5D might seem like a minor quibble. After all, whether the prefrontal cortex expanded 30fold or 60-fold in humans doesn’t affect many theories about cortical evolution. However, there’s an important consequence of inferring evolutionary changes from comparisons of macaques and humans. An examination of Figure 14.5B might suggest that the prefrontal expanded less than the posterior parietal and temporal areas. A phylogenetic analysis supports the opposite conclusion (Figure 14.3C). It shows that the prefrontal cortex expanded significantly more than other typically layered areas,32 although the difference is small. Overall, the studies cited here all agree that many typically layered areas, often called homotypical association cortex, expanded greatly during human evolution. Temporal tracts Structural neuroimaging methods reveal a major expansion and reorganization of fiber tracts in the human temporal lobe. The relevant comparisons include humans, chimpanzees, gorillas, and macaques. One modification involves the middle temporal gyrus. This structure is a hominoid-specific trait, as judged by its absence in cercopithecoid monkeys. It has dense connections with both the frontal and occipital cortex via the inferior frontal occipital fascicle (IFOF).50

Figure 14.6 Evolutionary changes in the temporal lobe of anthropoids and frontotemporal pathways. (A) Evolutionary tree showing two major phases of tract reorganization. (B) Tract illustrations (colored arrows). Abbreviations: AF, arcuate fascicle; EmC, extreme capsule; IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; MLF, medial longitudinal fascicle; UF, uncinate fascicle; VP, ventral pathway. (A) Adapted from M. Braunsdorf et al., Does the temporal cortex make us human? A review of structural and functional diversity of the primate temporal lobe, Neuroscience and Biobehavioral Reviews 131, 400–10, 2021. (B) Reproduced from K. L. Bryant and T. M. Preuss, A comparative perspective on the human temporal lobe, in Digital Endocasts (ed. E. Bruner, O. Emiliano, and T. Naomichi), Springer, Japan, 239–58, 2018.(A)© 2021 Marius Braunsdorf et. el. Published by Elsevier Ltd. (B) © 2018, Springer Japan KK

Figure 14.6 illustrates some differences in fiber-tract morphology between macaques and humans, including both the modifications of pathways and human-specific cognitive capacities. Given the evolutionary distance between cercopithecoid monkeys and hominoids, it should come as no surprise that fiber-tract differences between macaques and humans48,51,52 arose from an accumulation of changes that occurred at different times, in different ancestral species. The figure emphasizes two such developments. In the most recent one, there’s been a rewiring of a pathway, the arcuate fascicle (AF), which connects the frontal and temporal lobes.53,54 Hemispheric asymmetries complicate the picture somewhat,55* but generally speaking, significant changes occurred in both hemispheres during human evolution.56,57,58 In the inferred ancestral anthropoid condition, the AF had connections restricted to the dorsal temporal cortex, as it does in macaques. In contrast, this pathway extends significantly farther into the temporal cortex in humans than in macaques, chimpanzees, or gorillas. As part of this development, the AF has extensive connections with the middle temporal gyrus.51,59 Gorillas have some of these connections, but in much simpler form than in humans, and the same goes for comparisons between humans and the other adequately studied apes.60 The AF extends more into the middle temporal gyrus in gorillas than in monkeys, but not as much as in humans. Figure 14.6 also marks an earlier change in temporal-lobe fiber tracts: of the inferior longitudinal fascicle (ILF). The ILF has recognizable subdivisions in both humans and chimpanzees, but not in macaques.60 It also conveys more elaborate occipital–anterior temporal lobe connections in humans than in macaques. In humans, the anterior temporal lobe is a cortical hub for semantic memories, especially highly generalized ones, and this modification occurred, or at least began, in an ancestor common to gorillas, panins, and hominins.50 Allocortical alterations Two phylogenetic analyses have found that the hippocampus decreased in relative size during most of anthropoid evolution.61,62 Figure 14.7 illustrates size trends for cortical field CA3 of the hippocampus, which is representative of total hippocampus size. CA3 decreased in size during most of anthropoid evolution, as it increased in size in prosimians. Not all anthropoid lineages continued the trend toward a smaller hippocampus, but contraction prevailed prior to the Miocene. Despite an expansion of CA3 in some Miocene anthropoids, in only a few species did the hippocampus approach its ancestral size. A size decrease usually means that hippocampal functions became less important, as occurred more dramatically in cetaceans (Chapter 15, “Cortical contractions”).63 Accordingly, anthropoids presumably relied on neocortical areas to carry out some aspects of hippocampal function, presumably related to navigation. One possibility involves the representation of visual scenes. The guidance of locomotion within a visual scene is one of the ancestral functions of the hippocampus,19 but similar representations in the inferior temporal cortex might have become more important for Oligocene and Miocene anthropoids. Figure 14.7 suggests that studies of cebus and squirrel monkeys could test that hypothesis.

Figure 14.7 Hippocampal contraction in anthropoids, followed by expansion in humans. Phylogenetically reconstructed changes in the size of cortical field CA3 of the hippocampus. The dashed horizontal line shows the ancestral size. Values on the y-axis are a unitless measure of relative size, based on residuals from a phylogenetically corrected analysis of variance. Abbreviations: Paleo, Paleocene; P-P, Plio-Pleistocene. Data from D.R. Vanier, C.C. Sherwood, and J.B. Smaers, Distinct patterns of hippocampal and neocortical evolution in primates, Brain, Behavior and Evolution 93, 1–11, 2019.

Later, the hippocampus increased in relative size during hominin evolution, thus reversing the anthropoid trend.61, 62 Figure 14.7 illustrates this large upward grade-shift, which suggests that the functions of the hippocampus became more important in humans than in our anthropoid ancestors, including our hominid ancestors. In a previous book,19 my coauthors and I proposed that hippocampal functions changed during human evolution because of connections with new or expanded neocortical areas, especially those in the granular prefrontal cortex and anterior temporal lobe. Chapter 17 (“Sense of self”) returns to this point. Cortex and chromosomes Figure 14.8A illustrates an important aspect of cortical development in primates.39 In other mammals, as the number of cortical neurons increases, neuronal density decreases. During primate evolution, developmental programs overcame that limitation. As the cortex expanded, the density of neurons remained relatively constant. Accordingly, for a cortex roughly matched in size, primates have several-fold more neurons than nonprimates (Figure 14.8B).

Figure 14.8 Neuronal density and counts for the cerebral cortex. (A) Primates maintain a relatively constant density of neurons as the number of neurons increases (red circles), whereas in other mammalian lineages the density decreases as a function of neuron number (blue circles). (B) As a consequence of the relationship in Part A, primates have many-fold more neurons than other mammals, matched for a given brain size. The dashed horizontal line indicates an arbitrarily selected cortex size. Adapted from S. Herculano-Houzel, The Human Advantage: How Our Brains Became Remarkable, MIT Press, Cambridge, 2016.

Two conclusions follow from these observations: (1) Early in primate evolution, genetic mechanisms developed to maintain a high density of neurons regardless of the absolute size or number of neurons in the cortex; and (2) later, during the Eocene, Miocene, and Plio–Pleistocene, expansions of the cortex not only increased its volume and mass, but also the number of neurons. These genetic changes affected some parts of the cortex more than others,64,65,66,67,68,69,70,71 which accounts for the differential expansion of the typically layered areas during human evolution (“Typical lamination, atypical expansion”). Box 14.1 Homologies that produce homoplasies Many neuroscientists prefer homology over homoplasy, in part because conserved traits are more amenable to translational research. In this context, it might be of interest that homoplasies at the morphological level can result from homologies at the molecular level. Put the other way around, homologous genes can generate morphological homoplasies. In genetics, homologous genes that have a similar function are called orthologs; evolutionarily related genes with different functions are called paralogs. Genes that guide development show remarkable conservation across animal lineages. The well-known Hox genes guide the development of body plans, for example. Some Hox genes, such as HoxD13, control the size and organization of the thumb, which varies dramatically among mammals, including primates. Other Hox genes control the development of the other digits. When distantly related lineages encounter selective pressures that favor a morphological trait controlled by HoxD13, similar changes can occur independently. In this way, a homologous (orthologous) gene produces homoplasy. A generalized thumb in an ancestral species can morph into a specialized thumb in several independently evolving lineages, and it can do so without other changes in the genome, and vice versa. The result is “rampant” and “ubiquitous” homoplasy.72 A common refrain in popular science is that humans share 99% (or some other high percentage) of our genome with chimpanzees or some other species. This fact supposedly shows how closely related we are, but small changes in the genome can have enormous effects. In primates, many such changes have arisen from gene duplication (although novel genes also evolve from noncoding DNA). More than 80% of the gene duplications postdate the divergence of cercopithecoids and hominoids, which probably occurred ~26–23 Ma. Other gene duplications occurred about the time that orangutans diverged from other hominids, ~14 Ma, and another burst of gene duplication occurred in an ancestor of humans, gorillas, and

chimpanzees ~10 Ma, three to four million years before the divergence of chimpanzees and humans.73 Many of these duplications occurred in genes that control neural development (Box 14.1). Genomic locations where such duplications and rearrangements occur repeatedly, known as duplication hubs, compose ~5% of the human genome and have played an important role in human evolution, especially for the cortex.72 One of these duplications affected cortical evolution profoundly, as illustrated by the black bars and labels in Figure 14.9B. Sometime after ~14 Ma, in a common ancestor of gorillas, chimpanzees, and humans (ancestral Homininae), a replication error inserted an incomplete and functionally defective copy of the ancestral NOTCH2 gene into the genome (labeled as the “original NOTCH2 duplication” in Figure 14.9B).74 A subsequent replication error inserted an additional nucleotide sequence from the same gene ~4–3 Ma, during hominin evolution, which restored the gene’s function. Later, that gene—NOTCH2NL —duplicated twice more, so the human genome has three active versions of the gene on chromosome 1.

Figure 14.9 Genetics of cortical expansion in hominins. (A) Encephalization quotients (EQs) of australopithecine and human species, adjusted for phylogenetic sampling. Filled circles, hominins; unfilled circle, chimpanzees. (B) Evolutionary tree of extant Homininae, highlighting human-specific changes in the genome: SRGAP in red; NOTCH2 in black; and Hominini-specific deletions (HSD) in green. (A) Adapted from H. P. Püschel et al., Divergence-time estimates for hominins provide insight into encephalization and body mass trends in human evolution. Nature Ecology and Evolution, 5, 808–19, 2021. (B) Evolutionary tree adapted from I. T. Fiddes et al., Human-specific NOTCH2NL genes affect notch signalling and cortical neurogenesis, Cell 173, 1356–69, e1322, 2018.

The NOTCH2NL protein blocks a signal that stops the division of neural stem cells.75 This blockade leads to the production of many more neurons, which augments the human neocortex. Figure 14.9A plots phylogenetic EQs, using the same data as illustrated in Figures 8.6 and 8.7. It shows that hominin brains expanded rapidly after the NOTCH2NL duplications ~3 Ma, and it’s well known that most of the EQ increase results from cortical enlargement. Other genes implicated in human brain expansion also arose through duplication. A gene called SRGAP2 is also located on human chromosome 1, near one of the NOTCH2NL paralogs. It plays a role in increasing connectivity among neurons. Ancestral mammals had one version of this gene, but it underwent three duplication events during human evolution, so modern humans have four versions: SRGAP2A, B, C, and D. Based on contrasts among the genomes of humans, chimpanzees, and orangutans, these duplications occurred ~3.4, ~2.4, and ~1.0 Ma, as indicated in Figure 14.9B. The first of these duplications occurred just before the oldest manufactured stone tools.76, 77 The second, ~2.4 Ma, corresponds roughly with the appearance of Homo habilis in the fossil record, named for their association with a sophisticated Paleolithic toolkit called the Oldowan tradition.78 Chapter 17 (“Types of tools”) returns to this topic. One product of these duplications, the paralog SRGAP2C, counteracts the effect of the original SRGAP2, the orthologous gene. The ortholog promotes the maturation of dendrites, which terminates the rapid and profuse generation of dendritic spines during corticogenesis. Accordingly, the paralog has the opposite effect. It delays maturation, a trait known as neoteny. By counteracting the ancestral gene, it extends the profuse proliferation period for dendritic spines, especially long ones, which increases spine density as well as dendritic branching.79, 80 SRGAP2C hasn’t always played this role. About 2.4 Ma, it took over the antagonistic function of an earlier paralog, SRGAP2B.80 In addition to SRGAP2C, two other human-specific gene duplications played an important role in human brain evolution: HAR5 and ARHGAP11B. HAR5 stands for human-accelerated regulatory region 5, which is a primate-specific duplication on chromosome 10. The duplication that produced HAR5 occurred in catarrhines after their divergence from platyrrhines. During human evolution, this gene underwent a number of base-pair substitutions, which altered its function.81 High levels of HAR-gene expression occur in the most highly expanded cortical networks of human brains, those involving the typically layered areas.71 Although their precise role remains unclear, the HAR genes contribute to the development of synapses and dendrites in cortical areas linked to social cognition. For instance, HAR5 is transcribed copiously in a transcortical network that includes the dorsomedial prefrontal cortex, which plays a role in representing oneself and others (Chapter 17, “Sense of self”). These functions have attracted the attention of translational neuroscientists because disruptions of these areas are associated with an increased incidence of schizophrenia82 and autism.71 As for ARHGAP11B, expression of this gene in mouse embryos increases the number of mitotic divisions in radial glial cells, which causes the subventricular zone of the developing cortex to increase in thickness by almost a third.83 Introduction of ARHGAP11B into the marmoset genome leads to similar results.84 These findings support the idea that ARHGAP11B contributed to the expansion of the cortex in humans. However, there’s a claim in the two papers cited here that isn’t well supported. The authors of both the mouse and marmoset papers claim that expression of ARHGAP11B induces gyrification in addition to more neurons.83, 84 I see no evidence of gyri in the published photomicrographs. There are some minor disruptions of lamination and a bump of some sort, mainly rostrally in the frontal cortex, but nothing even remotely resembling a gyrus. Regardless, it’s clear that ARHGAP11B is among the duplicated genes that enabled the typically layered areas to expand during hominin evolution. In addition to duplications, within-gene deletions also contributed to cortical expansion in humans, and one of them increases the number of dendritic spines and spine density in the granular prefrontal cortex.85 There’s a caudal-to-rostral increase in synaptic density, which correlates with an increase in both the volume of prefrontal cortex and its number of neurons.86,87,88 All these measures are more pronounced in humans and chimpanzees than in other catarrhines.33 The synaptic-density gradient in adults matches the concentration of retinoic acid and its receptors during embryonic development, which are associated with an enhancement in the expression of a synaptic-organizer gene, CBLN2.89 Deletions within the CBLN2E2 enhancer gene occurred in an ancestor of humans and chimpanzees (the Hominini) after its divergence from gorillas. Figure 14.9 labels these deletions as Hominini-specific deletions 1 and 2 (HSD1 and HSD2). These withingene deletions removed binding sites for a repressor of the synaptic organizer. In the absence of this inhibitory signal, synaptogenesis was exaggerated along the retinoic acid gradient, which resulted in a greater number and density of dendritic spines and synapses in the granular prefrontal cortex. Although future research will reveal much more about the genetics of cortical expansion, the findings reviewed here suffice to show that the gene duplications and deletions illustrated in Figure 14.9 contributed to an expansion of the cortex generally and of the typically layered areas specifically, with some being specific to the granular prefrontal cortex. Genetic changes occurred at various times during hominoid evolution, including some specific to hominins. They led to an expansion of transcortical networks that link the granular prefrontal cortex with the typically layered areas in the posterior parietal and temporal cortex. The expansion of these networks enabled functional specializations to emerge within them, which contribute to the cognitive capacities that characterize humans.90 Chapter 17 examines these novel functions. Chapter summary There isn’t much evidence that new cortical areas emerged during human evolution, but there’s a lot of evidence that the typically layered areas expanded dramatically. These areas, more often called homotypical association cortex, include the granular prefrontal cortex; the medial and inferior posterior parietal cortex; and the superior, inferior, and anterior temporal cortex. The anterior insular cortex also enlarged. The expanded prefrontal areas include the polar prefrontal cortex (area 10) and other parts of the prefrontal cortex that emerged during anthropoid evolution: the dorsolateral, ventrolateral, and dorsomedial prefrontal cortex, along with parts of the orbital prefrontal cortex. Most cortical enlargement occurred after ~3 Ma. During anthropoid evolution, the hippocampus tended to decrease in relative size, but this trend reversed sharply during human evolution. These findings imply that other parts of the anthropoid cortex became more important for something than

the hippocampus was. Then, something happened to make the hippocampus more important in humans. Chapter 17 examines what those “somethings” might have been. This chapter brings Part IV to a close. The epigraph of this chapter came from a Vonnegut novel about pea-brain humans, one million years in the future. In his story, a smaller cortex led to a superior society. He blamed an “oversized” cortex for “evil schemes,” but the same cortex is also responsible for human cognition, culture, and creativity. Part V explores the ecological factors that selected for all that. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50.

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* Myelin-density maps in diffusion weighted structural fMRI are related to myeloarchitectonic maps, which are based on the pattern and density of myelin staining in histological sections. * Hemispheric specializations in the human cortex are so well known that I don’t discuss them in this book. In nonhuman animals, especially in monkeys, there have been claims for hemispheric specializations. My guess is that approximately 1 in every 20 attempts to demonstrate such differences have led to a statistically significant results at the magic p (or α) level of 0.05. But that’s just a guess because negative results are rarely reported.

PART V WHY THE CORTEX CHANGED

15 Eocene expansions Overview Most ideas about cortical evolution depend on the textbook view of cortical organization: that it comprises visual, auditory, somatosensory, motor, association, and limbic areas. A more parsimonious idea is that all cortical areas specialize in representing various combinations of information, also known as feature conjunctions. When an area’s specialized feature conjunctions provide an adaptive advantage, selection favors developmental programs that populate it with more neurons. The resulting expansion improves the area’s original representations, and it also serves as an exaptation for new areas and novel conjunctions, which then become subject to selection. Combined with fossil evidence about cortical grade-shifts during the Eocene, this idea informs proposals about when the characteristic shape of primate brains evolved; ecological factors favoring the enlargement of specific cortical areas; the role of primate-on-primate competition in cortical expansion; and the evolution of overt attention in anthropoids. Ford Prefect: How can you have money . . . If none of you actually produces anything? It doesn’t grow on trees you know . . . Management Consultant: Since we decided a few weeks ago to adopt the leaf as legal tender, we have, of course, all become immensely rich . . . —Douglas Adams, The Restaurant at the End of the Universe, Pan Books, 1980

Introduction In this chapter, cortex contends with clutter; the dentate diminishes; and a lemur gets in someone’s head. But I begin with the epigraph. Despite the fruits of Douglas Adams’s imagination, money doesn’t grow on trees. But many valuable things do, and primates have depended on them since the origins of our order. What’s new is old Neuroscientists have written about cortical evolution for more than a century. Brodmann recognized that the typically layered areas dominate the human brain, and he observed that other primates and other mammals have much less cortex of that type. Chapter 14 said much the same thing. The traditional way of expressing this idea is to say that homotypical association areas expanded preferentially in human brains. Other early twentieth-century anatomists, such as Campbell, the Vogts, and von Economo, agreed. So, what’s new here? A brief answer is that it’s the old stuff that’s new: fossils and the insights they provide. This chapter combines ideas about cortical maps (Chapter 12) with the fossil record, which indicates that cortical expansion into the modern prosimian grade probably occurred during the late Eocene, after ~40 Ma (Chapter 7). Here’s something that’s not new: It’s obvious that humans have the areas and networks that compose the cortex because they provided an adaptive advantage to ancestral species. That’s nothing more than restating evolutionary theory (putting aside other factors, such as genetic drift and phenotypic plasticity). Notwithstanding its broad explanatory power, general evolutionary theory can’t answer the two main questions posed in this book: 1. 2.

When did upward grade-shifts in cortex size occur, and in which primates did it happen? What areas contributed to these expansions, and what ecological factors drove these evolutionary changes?

The fossil record constrains answers to the first question, which has important implications for the second because timing is crucial to both. The cortex complete Representations revisited When neuroscientists have addressed these questions, they have usually done so in terms of the traditional view of cortical organization. According to textbooks and the summaries of cortical function found on the internet, cortical areas specialize in either visual, auditory, or somatosensory perception, memory, the control of voluntary movements, or executive functions (mainly the planning, prioritization, and sequencing of goals), with association areas and limbic cortex performing broader, cross-system functions. Without abandoning the textbook view entirely, The Evolution of Memory Systems1 developed a more parsimonious idea, which addresses the cortex in its entirety. It draws on the principle that a similarity in structure suggests a common function. For instance, the entire cerebral cortex, including allocortex, shares many structural features, such as a laminated cytoarchitecture and efferents arising from pyramidal cells that have apical dendrites extending toward the pial surface. A description of common features, such as nonpyramidal cell types, could fill a book. But not this one. Instead, the following proposal informs this chapter and the remaining ones: Proposal 1. Conjunctive representations are units of selection. All cortical areas and their subdivisions have the same basic function: to represent combinations of information, known as conjunctive representations or feature conjunctions. During primate evolution, advantages went to individuals that had a greater number of cortical neurons and connections devoted to conjunctive representations that provided a survival advantage. Via selection, areas with beneficial representations expanded during primate evolution, enabling specializations that sometimes resulted in a new area or a new subdivision within a pre-existing area. As new areas arose, their afferents and intrinsic circuitry generated novel conjunctions of information, which their efferents broadcast to other areas—old and new—via transcortical networks.2,3 This information modified representations in recipient areas, which resulted in additional novel conjunctions, all of which were subject to selection.

Box 15.1 discusses what the word “representation” means in this context. Box 15.1 Enaction traction The word “representation” has many meanings. In philosophy, an emphasis on representations in the brain contrasts with ideas about embodied cognition and enacted cognition. In this sense of the word, representations depend entirely on inputs to the brain. That’s not what representation means here. I am convinced by the ideas of Paul Cisek,4,5 who proposed that the brain should be viewed as an extended feedback system in which actions affect the sensory inputs that an animal receives as it exploits local resources or explores elsewhere. Cisek’s theory of brain evolution rejects the traditional view of the central nervous system as an input–output computer in which sensory inputs lead to motor outputs: perception– action cycles, as Fuster puts it.6 That conceptualization of brain function is highly incomplete. It’s not that the brain lacks inputs and outputs, Cisek’s point is that by concentrating so much on this one component of a larger feedback system, neuroscientists neglect the fundamental function of the central nervous system: to control movements that generate specific kinds of sensory inputs—the ones that evolution has motivated an individual to seek and obtain. Animals evolved to seek and obtain the sensory inputs that promote survival and replication. Buzsáki has developed a similar theory.7 The representational view espoused here is entirely consistent with the idea that cognition depends on interactions with the outside world, rather than merely input-driven representations in the brain. These ideas don’t conflict because, as I use the word “representation,” it simply means that when you have a representation of a lemur in your head, you don’t have an actual lemur in your head, which would be bad. Having a representation of a lemur means that some information about lemurs—in this case, a generalization about the semantic meaning of the word “lemur”—is generated, processed, and stored by an interconnected neuronal network that performs a computation based on its afferents (and intrinsic circuitry) and broadcasts the product of that computation via its efferents. A high-order, semantic representation of “lemurs” is a conjunction of features, analogous to the green, alligator-skin cube in Figure 15.2. The difference is that the category “lemur” involves conjunctions of both sensory and semantic information, such as what it takes to qualify for the honor of being a lemur and why flying lemurs (colugos) are not lemurs despite their name. An emphasis on cortical representations—as opposed to sensory, motor, executive, limbic, and association areas—might seem like a radical departure from the traditional view of the cortex, but it’s not. The information that goes to the sensory and motor areas makes them visual, somatosensory, auditory, or motor; those are the kinds of conjunctions they represent. So, Brodmann’s area 1 remains well described as a somatosensory area, to cite one example. Combinations of sensory modalities, such as the visual–auditory conjunctions of the superior temporal polysensory area, are merely another type of conjunction. Other representations are much more complex. Regardless of the level of complexity, each area has a specialization: a unique combination of information represented in its networks of neurons. The differences among each area’s type of representation are often little more than the addition or subtraction of elements. Take, for example, the premotor and prefrontal areas.8 Representations in the prefrontal cortex include goals, actions that achieve goals, visual affordances that guide actions, the sensory context in which goals have been pursued, and the outcomes that have occurred.9,10 Representations in the premotor cortex represent a subset of that information, mainly actions and the visual affordances that guide them. Terms like premotor and prefrontal make it seem like there are only two types of feature conjunction within these large expanses of cortex, but that’s wrong.8 Instead of two types of representations—premotor and prefrontal—there are dozens, if not hundreds. Box 15.2 expands on this point and some of its implications. Box 15.2 Areas The idea that large regions of the human cortex have blended, overlapping combinations of feature conjunctions has implications about the number of cortical areas, as well as the concept of cortical areas, in general. Perhaps the question— How many cortical areas does the human brain have?—is ill-posed. What matters is the diversity of conjunctive representation that promote fitness. When a region is sufficiently large and homogenous in its representations, then neuroscientists can identify it as a functionally distinct cortical field: an area. It would “light up” in a neuroimaging scan under appropriate circumstances. If a region specializes in many related, but slightly different representations, it’s difficult to know what to call an area. For broad regions of cortex, such as the granular prefrontal cortex, shoehorning everything into some discrete number of areas enables neuroscientists to draw maps that resemble those of well-defined sensory and motor areas. But the apparent certainty suggested by such maps is illusory. Although a representational perspective is not revolutionary, it reframes the search for selective factors a little. Instead of thinking about how selection may have operated directly on cortical areas and their functions—perception, memory, executive and motor control, emotional regulation, social cognition, etc.—we can consider what kinds of cortical representations might have provided a survival advantage. For instance, rather than evaluating whether social complexity led to a larger cortex,11,12 we can consider how social demands could have favored certain kinds of representations, and the same goes for other ecological drivers. A representations-first approach—as opposed to the traditional, systems-first approach—is a small change from the usual way of thinking about cortical expansion, but I think it’s an important one.

Figure 15.1 Conjunctive representations in the cortex of primates. An imaginary cortex, flattened to remove the laminar, depth dimension. The bulge to the left represents the olfactory bulb, included for orientation. Nothing is remotely to scale. The core neocortex (dark pink) contains the prefrontal (PF), posterior parietal (PP), and inferior temporal (IT) cortex. Attributes correspond to the qualitative features of vision, such as color, shape, glossiness, translucence, and visual texture; the word “metrics” refers to quantitative features, such as locations, directions, distances, number, order, speeds, etc. The outer ring consists of allocortex (blue), which includes the hippocampus (Hipp); between the core neocortex and the allocortex is the ring neocortex (light pink), which includes the perirhinal cortex (PRh) and entorhinal cortex (ERh). The specialized types of conjunctive representations are noted inside the rectangles, and each area’s representations contribute to perception, memory, and the selection of action based on its specialized representations. Adapted from E.A. Murray, S.P. Wise, and K.S. Graham, The Evolution of Memory Systems: Ancestors, Anatomy, and Adaptations, Oxford University Press, Oxford, 2017.© Oxford Publishing Limited

Figure 15.1 presents five examples of conjunctive representations and the functions that they serve: representations that lend themselves to words like objects, goals, scenes, and so forth. But, of course, there are many others, most of which are difficult to express in simple terms. PFo, for example, represents visual features of nonfood items associated with specific kinds of food, the visual features of foods, and their value, as updated by the current desirability of a food item based on current biological needs. That’s a mouthful: in more ways than one. Representations evolved as they provided adaptive advantages, not as they made it easier for neuroscientists to describe them. An emphasis on representations, rather than functions, also makes it easier to understand the distinction between selective pressures and current functions. Factors favoring the initial evolution of a new kind of representation don’t limit its future contributions to fitness. For instance, a type of representation that initially improved vision-based foraging choices could also provide advantages in social interactions; and representations that fostered predator recognition could later improve face recognition. For readers interested in this perspective, a previous book, The Evolution of Memory Systems,1 presents these ideas in detail. I won’t repeat the jaw-breaking arguments in that 500-page tome here, and I don’t expect many readers of this book to consult it. It’s enough to keep in mind the following aspect of Proposal 1: When a species benefited from a given kind of neural representation, developmental programs increased the number of neurons in the area that generated, processed, and stored those representations, so it became larger; when a species no longer benefited from a given variety of representation, the cortical area that generated it became smaller or was lost. The next two sections expand on these two evolutionary processes: expansion and contraction. Cortical contractions With so much emphasis on cortical expansion, it might seem like nothing else happens. However, two examples of cortical contraction involve the hippocampus, which has the advantage of being easier to measure than most cortical areas. Chapter 14 (“Allocortical alterations”) discussed a decrease in the relative size of the hippocampus during anthropoid evolution.13,14 However, that contraction was tiny compared with the shrinkage of the hippocampus that occurred in cetaceans.15 The cetacean contraction probably occurred because other cortical areas became more important than the hippocampus for navigational functions. The transition from a quadrupedal terrestrial life to riverine and then marine habitats imposed many novel navigational demands on cetaceans, and it seems likely that neocortical areas “took over” some hippocampal functions. Like bats, echolocation guides aspects of navigation in cetaceans. Toothed whales and dolphins emit high-frequency clicks via a specialized structure near their blowhole, and another specialization on their forehead focuses sound waves directionally. Both the focusing mechanism and a modified cochlea evolved soon after toothed whales split from baleen whales (Figure 10.2C).16 Toothed whales and dolphins also use echolocation to detect food—such as squid or schools of fish—in murky oceanic environments in which neither vision nor olfaction is very useful.16 Accordingly, it seems likely cetaceans have a suite of specialized auditory areas, which represent various parameters of reflected sounds. If so, then the hippocampus would have

become less important to cetaceans as auditory areas became more important for both detecting prey and navigating to them. All these ideas are speculative—there’s no neurophysiological evidence about how cetaceans navigate or maps of their auditory cortex—but the next statement is a fact: In some cetaceans, the hippocampus and dentate gyrus have all but disappeared.15 Extrastriate enlargement The ventral visual stream provides an example of the opposite evolutionary trend: expansion. But it’s not a random example, it’s the one that produced the characteristic shape of primate brains. In Chapter 1, I said that any reasonably competent neuroanatomist could recognize the cranial endocast in Figure 1.1 as a primate brain. Despite the absence of fossil evidence about the animal’s body, the shape of the cortex identifies it unambiguously as coming from a primate.

Figure 15.2 The ventral visual stream in anthropoids. Visual areas differ in the types and complexity of the visual feature conjunctions they represent. Three visual submodalities are depicted: color, visual texture, and shape. Two features, Vis A and Vis B, are different shades of green; a third feature, Vis C, is an alligator-skin visual texture; and a fourth feature, Vis D, is a shape, in this case a cube as rendered in two-dimensional space. The perirhinal cortex represents feature conjunctions at a level of

complexity that distinguishes most objects in a primate’s natural habitat. Here, this level is termed “high,” but there are many more complex and higher levels of feature conjunction elsewhere in the cortex. Abbreviations: ES, extrastriate visual cortex; IT, inferior temporal cortex; PRh, perirhinal cortex; V1, primary visual cortex. Adapted from E.A. Murray, S.P. Wise, and K.S. Graham, Evolution of Memory Systems: Ancestors, Anatomy, and Adaptations, Oxford University Press, Oxford, 2017.© Oxford Publishing Limited

Proposal 1 posits that selection favors representations of novel visual conjunctions when they provide an adaptive advantage. As Figure 15.2 illustrates, low-order visual areas, including the extrastriate visual areas in the occipital and caudal temporal cortex, represent relatively simple conjunctions of visual submodalities, such as color, shape, visual texture, glossiness, and translucence. Other extrastriate areas represent more complex conjunctions. For example, a high-order conjunction in the perirhinal cortex might combine attributes from each of several visual submodalities, such as a cube with dark green and light green, alligator-skin covering (Figure 15.2). This feature conjunction includes several colors, a visual texture, and a shape. Other information might include the orientation of an object, its distance (computed via ocular disparity signals), and so forth, in various combinations at some location in visual space. Accordingly, the first illustration in this chapter, Figure 15.1, distinguishes between qualitative visual features like color, called attributes, and quantitative aspects of vision, called metrics. In addition to locations, quantitative visual information includes a given kind of movement in visual space: at a particular speed, in a certain direction, at a given distance. The dorsal visual stream, which includes the posterior parietal and middle temporal (MT) cortex, represents visual metrics; the ventral visual stream, which includes the inferior temporal and perirhinal cortex, represents visual attributes. These concepts lead to Proposal 2, which depends on fossil evidence indicating when, during the Eocene, the cortex expanded in primates (Chapter 7): Proposal 2. The characteristic shape of primate brains evolved by the early Eocene. As primates exploited the terminal-branch niche of angiosperm trees, extrastriate visual areas expanded as they came to include more neurons and interconnections, which improved the quality and diversity of their feature conjunctions— at various levels of complexity. In this way, selection led to an increase in the variety of visual representations that primates could process and store. The rostrolaterally extended temporal lobe and the lateral (Sylvian) fissure emerged because of these evolutionary changes,17,18,19,20,21 and these developments gave primate brains their characteristic shape.21 Fossil evidence shows that the temporal cortex expanded during the early Eocene,22,23,24 when any overall expansion of primate brains was sufficiently modest that they remained within the Euarchontoglires size range.

Eocene enlargements I have invoked the phrase “primates of modern aspect” frequently in this book. For paleontologists, it’s an elegant way to refer to Euprimates without including stem primates (plesiadapiforms). That’s fine, but to be truly modern a primate needs a trait absent from both stem primates and early crown primates: a relatively large brain consisting mostly of neocortex. As Chapter 7 explained, fossil evidence shows that the cortex remained at the Euarchontoglires grade during early primate evolution. Then, during the late Eocene, primate brains underwent an upward grade-shift in encephalization and corticalization. This conclusion has two implications: (1) The ecology of the late Eocene provided the driving forces for cortical expansion; and (2) in view of their divergence times, at least four and perhaps five upward grade-shifts occurred independently in the major primate clades (Figure 7.6). Many neuroscientists will find the second conclusion difficult to accept, but there’s a reason for convergent evolution that makes it plausible: Individuals in independently evolving primate lineages faced similar problems in similar habitats. The question is: What were these “problems”? One possibility is that global cooling caused an intensification of competition in forest habitats under stress, and increased competition for dwindling resources favored individuals with more cortical neurons devoted to specific kinds of representations. Climate change and cortex change Figure 9.1 illustrates a temporal relationship between the late Eocene grade-shifts and global cooling. Correlations don’t imply causality, of course, but it’s difficult to overstate the importance of climate change to the lives of Eocene primates. Temperature fluctuations not only affected the resources available to primates, but also influenced body size, foraging strategies, daily activity patterns, and diet, among other things. Global cooling has often constrained where and how primates lived, although the effects are indirect. As Chapter 6 explained, Eocene primates were small animals that lived in the terminal branches of angiosperm trees in dense tropical and subtropical rainforests. High global temperatures and a humid atmosphere supported lush and diverse vegetation, which included protective foliage and many sources of nutrition: insects; young and tender leaves; fruits and seeds; flowers; nectar; buds; shoots; and saps and gums. During the Paleocene and most of the Eocene, primates lived in ecosystems with abundant resources and little seasonal variation. Then, late in the Eocene, something happened to select for cortical expansion. The comparative neuroanatomy discussed in Chapter 12 identified new, primate-specific areas that contribute to cortical maps in primates. Selection operated upon these areas and their neural representations, among others. Primate specializations included: • • • •

New prefrontal areas, including the frontal eye field (FEF) and new parts of the orbital prefrontal cortex (PFo). New motor areas: a greatly enlarged or entirely new hindlimb representation in the primary motor cortex (M1) and several new premotor areas. The dorsal visual stream: an enlarged set of posterior parietal areas and the MT complex, which provided metric information and visual affordances to premotor and prefrontal areas. The ventral visual stream: a much-expanded set of extrastriate visual areas in the occipital and inferior temporal cortex, which represented novel conjunctions of visual attributes.

This understanding leads to two proposals: one on cortical areas and representations; the other on cortical expansion.

Proposal 3. Primate adaptations to dim-light, terminal-branch foraging produced a suite of new cortical areas that exploited enhancements in vision. New premotor and posterior parietal areas improved visually guided reaching and grasping; and the new or enlarged hindlimb representation in M1 enhanced the cortical control of hindlimb movements, especially those involving their feet. Chapter 13 (“Changes at the top”) explained the visual coordinate frames in which parietal– premotor networks represent eye, head, hand, and foot movements for grasping, leaping, climbing, feeding, and defensive movements. They improved visually guided movements via the representation of action maps, which stored conjunctions of visual affordances and motor programs associated with them. Collectively, they represented the movements available to primates based on what they could see. The FEF and several new visual areas, such as V3 and MT, provided an improved neural mechanism for identifying, keeping track of, and attending to valuable items, a capacity that was especially advantageous in the cluttered space of the terminal-branch niche. Several new visual areas of primates contributed to the identification and localization of items in dim light, with the MT complex providing information about motion in central visual space. Part of PFo probably evolved in a Euarchontan ancestor common to tree shrews and primates,25 but additional PFo areas emerged during primate evolution. They contributed to updating the valuation of hidden or poorly seen food items in terms of desirability, based on current biological needs. Unlike the orbitofrontal cortex of most other mammals, PFo function depends to a large extent on inputs from visual areas, specifically the perirhinal cortex and inferior temporal cortex. These connections empowered Eocene primates to make foraging choices based on the remembered appearance of unseen resources, as recalled from memory based on associations with nonfood objects viewed in dim light. Put another way, PFo enabled early Euprimates to forage “visually” for food items that they couldn’t see or could see only poorly—but were associated with things that they could see with minimal illumination in cluttered conditions.

Incorporating fossil evidence into Proposal 3 leads to: Proposal 4. Primate-on-primate competition drove expansion of primate-specific cortical areas during the late Eocene, independently in the major primate clades. Over the final 6 million years of the Eocene (~40–34 Ma), the atmosphere cooled by ~4°C, which decreased atmospheric moisture by ~30%. This phase of climate change caused rainforests to contract, which intensified competition among primates, birds, and arboreal rodents in the terminal-branch niche of dense forests. As Chapter 6 explained, before the extinctions of the Oligocene bottleneck (~34 Ma), primate-on-primate competition was much more common than in subsequent epochs. Competition among primate species, which had similar cortical maps, favored individuals that could: (1) reach more accurately under visual guidance; (2) grasp things better with hands and feet, also under visual guidance; and (3) make better foraging choices based on vision and memories about the visual appearance of favored foods. Selective forces thereby favored an expansion of the premotor, posterior parietal, and prefrontal cortex. These evolutionary changes drove primate brains into the modern prosimian size range, and the cortical maps of primates reflect these enhanced visual and visuomotor functions.

There are two scenarios for these expansions, which are not mutually exclusive. In the most likely one, heightened primateon-primate competition favored the enlargement of already-existing primate-specific areas. In the other, some or all these new areas emerged during the expansion. The late Eocene grade-shift launched anthropoids along an evolutionary trajectory that eventually produced the enormous cortex that characterizes modern platyrrhines and cercopithecoids, as well as the yetmore-enormous cortex of hominoids. Several aspects of Proposals 3 and 4 require additional explanation. Perhaps the most important thing is something missing from them: any mention of the social life of Eocene primates. Grub versus groups A large literature seeks to link social complexity with cortical expansion in primates, an idea called the social-brain hypothesis. It’s based on a correlation of brain size, encephalization quotients (EQs), or corticalization values with social complexity,26 especially the number of individuals that regularly interact with each other.27 Chapter 16 (“Groups, grub, and gray matter”) returns to this topic in the context of anthropoid evolution after the Oligocene bottleneck; this chapter concentrates on earlier primates. DeCasien et al.28 used phylogenetic statistics to model the relative influence of dietary and social specializations on EQ values, based on a large sample of modern primates. They concluded that the social-brain hypothesis fails to account for a significant amount of the variation in EQs. Instead, they favored dietary factors, such as a frugivory: a debate that’s often framed in terms of social intelligence versus ecological intelligence. Chapter 10 (“Cats go their own way”) reviewed evidence that the social-brain hypothesis also fails to account for cortical expansion in carnivores, provided that a phylogenetic sampling bias toward canines is corrected.29

Figure 15.3 Primate social and mating systems. The color of each circle or component of a circle indicates the social system of an ancestral species in this phylogenetic reconstruction. Adapted from A.R. DeCasien, S.A. Williams, and J.P. Higham, Primate brain size is predicted by diet but not sociality, Nature Ecology and Evolution 1, 0112, 2017, supplemental material.

The fossil evidence reviewed in Chapter 7 bears on the social versus ecological intelligence debate. This prospect might seem unlikely at first glance because social systems don’t fossilize. However, brain endocasts from fossil species constrain estimates about when EQ and corticalization values increased. By relating these estimates to a phylogenetic analysis of primate social systems, it’s possible to re-examine the social-brain hypothesis. Many primate species, including strepsirrhines, evolved complex social systems, which anthropoids did to a striking degree. Nevertheless, the prevalence of a solitary social life among modern prosimians indicates that all primates descend from ancestors that had relatively simple societies. Figure 15.3 presents a phylogenetic reconstruction of pre-Pleistocene social evolution.28 According to this analysis, ancestral strepsirrhines and haplorhines both had relatively simple societies, usually characterized as solitary.* Then, sometime during anthropoid evolution, and independently in certain strepsirrhine lineages, more complex societies evolved. This reconstruction of primate social evolution has two important implications: All modern primates descend from a long series of solitary species; and the complex societies of modern primates must have evolved independently of social systems in other mammals. These conclusions are important for understanding cortical evolution because they imply that the cognitive demands related to visually guided movement and vision-based foraging drove cortical expansion in late Eocene primates, not factors related to complex societies. It’s useful to consider prosimians and anthropoids separately. As a comparison of Figures 7.6 and 15.3 indicates, the late-Eocene upward grade-shift in the size of the cortex occurred before the advent of complex social systems in strepsirrhines, which was mainly an Oligocene and Miocene development according to the phylogenetic reconstruction of DeCasien et al.28 If that reconstruction is correct, then the social-brain hypothesis doesn’t apply to primates, as a whole, because it doesn’t explain the cortical grade-shifts in strepsirrhines. Correlations drawn from modern species might seem to rule it in, but the timing of cortical expansion rules it out: at least for strepsirrhines and probably for primates generally. However, the social-brain hypothesis might be valid for some primate clades and invalid for others, and both Chapter 16 (“Groups, grub, and gray matter”) and Chapter 17 (“Dreamers of the day”) return to this topic. Finding food In place of the social-brain hypothesis, DeCasien et al.28 advanced a statistical model in which dietary preferences account for primate encephalization. They emphasized frugivory, which is associated with larger brains in anthropoids. The complementary idea is that folivory is associated with smaller brains, examples of which include howler monkeys (Alouatta) in the New World and colobines in the Old World.30

However, folivory can entail much more complex and demanding foraging strategies than commonly assumed.31 In addition, species commonly classified as folivorous often eat a lot of fruit when it’s available. Rather than attributing brainsize reduction to folivory per se, a decrease in relative brain size has probably occurred in some (but not all) folivorous lineages depending on the details of their foraging strategies, ecological circumstances, and social systems.11,27 Proposal 1 (p. 283) suggests another way of looking at diet as a selective factor, and it has three related implications: 1. 2. 3.

When a species no longer benefited from the conjunctive representations generated by a given cortical area, its contraction or loss can contribute to a decrease in the EQ, and this probably happened in some folivorous primates. A dependence on hidden or distant resources can contribute to increases in the EQ when specific cortical representations contribute to foraging success,32 and this probably happened in some frugivorous and omnivorous primates. But a diet of plentiful items, such as leaves, needn’t lead to cortical contraction if the foraging strategies of a species continue to benefit from a broad variety and richness of conjunctive representations: to distinguish among leaves of different value, for example. Some folivores are very selective about the leaves they eat.

Based on their small body size during the Eocene, most primates were probably herbivores or insectivores, although gumivory (a diet of tree saps and gums) was also important for the smallest species. These diets, and especially folivory, are often associated with low cognitive demands. However, as point 3 emphasizes, the lack of extractive foraging doesn’t preclude a large cortex. Accordingly, the benefits of improved visuomotor control and more effective vision-based foraging choices could have driven cortical expansion regardless of a primate’s preferred foods. Contending with clutter Chapter 12 (“Oculomotor oversight”) discussed the FEF, with emphasis on homologies among primates, and it briefly mentioned pop-out as a perceptual phenomenon that could help overcome clutter in a terminal-branch niche. Of course, there’s much more to the FEF than that, and Schall has reviewed the relevant literature.33 Traditionally, the FEF has been viewed as a premotor area for controlling eye movements, mainly because electrical stimulation of this area generates saccades34,35 or smooth-pursuit eye movements.36 But the FEF is hardly unique in this regard; many premotor areas include oculomotor representations, as revealed by intracortical microstimulation.37,38,39 Its granular cytoarchitecture—the principal diagnostic trait of the new prefrontal areas that emerged at various times during primate evolution—supports its inclusion among the prefrontal areas. So, too, does the sparsity of projections from the FEF to the superior colliculus in galagos,40,41 on the assumption that it reflects a trait of early primates. An oculomotor area would be expected to have strong corticotectal projections, and the weakness of this projection in galagos (Chapter 13, “Shifting synapomorphies”) suggests that, initially, the FEF was not an oculomotor area. Instead, the FEF is best understood as a prefrontal area that controls search and attention functions.9,10 The FEF has connections with both the dorsal and ventral visual streams, so it can accentuate the processing of both visual metrics and attributes. As Eocene primates adapted to life in the terminal branches of angiosperm trees, the search and attention functions of the FEF improved their ability to find and keep track of valuable items interspersed among a clutter of material with no nutritional value, as viewed in dim light: during daytime or nighttime, with or without a fovea. The fovea evolved in haplorhines before anthropoids and tarsiers split: ~70–58 Ma (Figure 4.2). If this range of divergence dates is correct, then tarsiers and anthropoids entered the Eocene separately, both with a fovea. The fovea changed the FEF’s function in a significant way, which leads to a proposal that distinguishes overt and covert attention. Overt attention involves the foveation of visual stimuli, also known as visual fixation, gaze, and central vision; covert attention involves peripheral vision, which, in species lacking a fovea, probably applies to any part of visual space. Proposal 5. Control of overt attention is a novel function of the FEF, which evolved in Eocene anthropoids. In anthropoids, the prior emergence of the fovea in ancestral haplorhines, combined with diurnal foraging, led to the FEF performing a new function. As a primate-specific part of the prefrontal cortex, the original function of the FEF was to search for and maintain covert attention on visual goals. In anthropoids, a novel function derived from the ancestral one: control of overt attention, mediated in part via newly powerful corticotectal projections.42,43 Control of overt attention, of course, requires an oculomotor function, commonly known as visual fixation.

As discussed in Chapter 13 (“Shifting synapomorphies”), a strongly enhanced projection to the superior colliculus accompanied the development of denser connections between the FEF and visual-motion areas of the dorsal stream, such as area MT, which provided information about motion in central visual space. Corticotectal projections facilitated the orientation of overt attention toward an object that: (1) had been chosen as a goal for future action (encoded by the FEF); and (2) moved in central visual space as an animal’s head and body bobbed on flimsy branches (encoded by MT). Visions of value Chapter 12 (“Over the orb”) summarized some updated views about the function of another primate-specific part of the prefrontal cortex: PFo. According to one popular idea, PFo is said to function in stimulus–outcome associations in a general sense. In this context, the concept of state-space has come into vogue. This term refers to representations of all the behavioral variables that guide foraging choices. Recent work on macaque monkeys demonstrates that PFo performs a much more specific function: updating the valuation of nonfood items based on visual features, their association with food items, and current biological needs. Originally, this function was an adaptation for dim-light foraging in a cluttered visual space. Once it evolved, PFo provided Eocene primates with an important advantage: a means for assessing the current biological value of foraging choices based on the sight of nonfood items associated with hidden or poorly seen foods. In this way, Eocene primates could make foraging choices based on the things they could see best in dim light, and they could also predict the visual appearance of hidden food items. From one perspective, this function doesn’t seem to differ much from what the orbitofrontal cortex does in rodents. Like PFo in primates, these parts of the rodent cortex use memories to generate predictions about unseen outcomes (rewards) and

their specific sensory features (mostly taste and smell). Without taking evolution into account, it might seem like the difference between orbitofrontal cortex function in primates and rodents is trivial. Neuroscientists espousing such a view will probably acknowledge that vision is more important in primates than in rats and mice, but otherwise they contend that the PFo of primates plays much the same role as the orbitofrontal cortex of rodents. The problem with this opinion is that it ignores the evolutionary significance of vision’s predominance in primates, as well as the fact that all primates descend from a single species that specialized in dim-light, arboreal foraging. This evolutionary history changed the cortical mechanisms underlying foraging choices in a crucial way. Ancestral primates could forage “visually” for items that they couldn’t see. Thus, a primate-specific area, PFo, empowered primates to forage effectively in the dim light of dense rainforests, based on the desirability of food items concealed by clutter and darkness. The concept of foraging “visually” for hidden food items is challenging, but primates evolved other mechanisms of a similar nature. Here I cite four of them: (1) Neurophysiological studies have revealed that neurons in the premotor and posterior parietal cortex encode target and hand location in retinal coordinates. These representations require a recalculation (called updating in the literature) of target and hand position just before or after every saccade. On the surface, updating seems unnecessary because, when a primate makes a saccade, neither the hand nor the target of the planned reaching movement changes location. Updating representations of hand and target position is necessary after each saccade because both are represented in retinal coordinates. (2) When reaching in total darkness, people continue to use visual information. In the posterior parietal cortex, proprioceptive signals come into alignment with visual inputs. When aligned though motor adaptation, proprioceptive and visual signals both indicate the current location of the hand. In a sense, vision teaches the motor system how to make visually guided movements based on proprioception, even when reaching in dim light (or no light at all). (3) People use visual coordinates even when they reach toward localized sounds. The evidence is that they overshoot acoustic targets that lie in what would be their peripheral field of vision. These small errors result from distortions in peripheral vision, which needn’t have any effect on reaching toward acoustic targets. But they do. Thus, even though reaching occurs in total darkness, the movements remain visually guided. (4) Finally, congenitally blind people make straighter visually guided movements than people with typical eyesight. The latter have distortions of vision that lead to deviations from a straight trajectory. Because the motor system generates reaching movements that are straight in visual coordinates, when vision is slightly distorted, what looks straight deviates a little from the ideal trajectory. Congenitally blind individuals have never experienced these distortions, so their motor system can generate straighter movements: visually guided movements without vision.44 Chapter summary Fossil evidence indicates that an upward grade-shift in cortex size occurred during the late Eocene and that it occurred independently in the major primate lineages (Figure 7.6). This conclusion doesn’t imply that the new, primate-specific areas emerged then, although some of them might have. It’s more likely that new but small regions of cortex, which emerged in early crown primates, expanded under the selective pressures of the late Eocene. The late Eocene global cooling caused the contraction of tropical and subtropical forests, and in this ecological context a competitive advantage went to primates with: (1) improved pedal grasping mediated by the hindlimb M1 cortex; (2) improved guidance of reaching, grasping, and eye movements, controlled by primate-specific premotor and posterior parietal areas; (3) an ability to keep track of valuable objects and places in the terminal-branch niche, which depended on new visual and prefrontal areas, including the FEF; and (4) an enhanced capacity, which depended on PFo, for foraging visually for food items they couldn’t see very well, if at all, amid clutter in dim light. Primate-on-primate competition, which involved species that began with similar cortical maps, led to selection for larger cortical areas with representations that supported these functions. In the epigraph, money grows on trees, so everyone acquires immense wealth with minimal effort. The life of Eocene primates wasn’t quite that easy, but it would have seemed like paradise compared to what came later. After the Eocene, primate-on-primate competition became less common, but primates faced different challenges: intense seasonal variation and volatility in resources, combined with greater vulnerability to predators. The next chapter examines these plights and primate adaptations to them. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

Murray, E.A., Wise, S.P., & Graham, K.S. The Evolution of Memory Systems: Ancestors, Anatomy, and Adaptations (Oxford University Press, Oxford, 2017). Van Essen, D.C., Donahue, C.J., Coalson, T.S., Kennedy, H., Hayashi, T., & Glasser, M.F. Cerebral cortical folding, parcellation, and connectivity in humans, nonhuman primates, and mice. Proceedings of the National Academy of Science USA 116, 26173–80 (2019). DiNicola, L.M. & Buckner, R.L. Precision estimates of parallel distributed association networks: evidence for domain specialization and implications for evolution and development. Current Opinion in Behavioral Sciences 40, 120–9 (2021). Cisek, P. Evolution of behavioural control from chordates to primates. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 377, 2020.0522 (2021). Cisek, P. Resynthesizing behavior through phylogenetic refinement. Attention and Perceptual Psychophysics 81, 2265–87 (2019). Fuster, J.M. Upper processing stages of the perception-action cycle. Trends in Cognitive Sciences 8, 143–5 (2004). Buzsáki, G. The Brain from Inside Out (Oxford University Press, New York, 2019). Fine, J.M. & Hayden, B.Y. The whole prefrontal cortex is premotor cortex. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 377, 2020.0524 (2022). Passingham, R.E. Understanding the Prefrontal Cortex: Selective Advantage, Connectivity, and Neural Operations (Oxford University Press, Oxford, 2021). Passingham, R.E. & Wise, S.P. The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, and the Origin of Insight (Oxford University Press, Oxford, 2012). Perez-Barberia, F.J., Shultz, S., & Dunbar, R.I.M. Evidence for coevolution of sociality and relative brain size in three orders of mammals. Evolution 61, 2811–21 (2007). Dunbar, R.I.M. & Shultz, S. Why are there so many explanations for primate brain evolution? Philosophical Transactions of the Royal Society B: Biological Sciences 372, 20160244 (2017). Schilder, B.M., Petry, H.M., & Hof, P.R. Evolutionary shifts dramatically reorganized the human hippocampal complex. Journal of Comparative Neurology 528, 3143–70 (2020). Vanier, D.R., Sherwood, C.C., & Smaers, J.B. Distinct patterns of hippocampal and neocortical evolution in primates. Brain, Behavior and Evolution 93, 171–81 (2019). Patzke, N., Spocter, M.A., Karlsson, K.A., Bertelsen, M.F., Haagensen, M., Chawana, R., Streicher, S., Kaswera, C., Gilissen, E., Alagaili, A.N., Mohammed, O.B., Reep, R.L., Bennett, N.C., Siegel, J.M., Ihunwo, A.O., & Manger, P.R. In contrast to many other mammals, cetaceans have relatively small hippocampi that appear to lack adult neurogenesis. Brain Structure and Function 220, 361–83 (2015). Brusatte, S. The Rise and Reign of the Mammals: A New History from the Shadow of the Dinosaurs to Us (Mariner Books, New York, 2022). Allman, J.M. Evolving Brains (Freeman, New York, 2000). Preuss, T.M. Primate brain evolution in phylogenetic context. In: Evolution of Nervous Systems (ed. J.H. Kaas & T.M. Preuss) 3, 2–34 (Elsevier, Oxford, 2007). Preuss, T.M. The role of the neurosciences in primate evolutionary biology: historical commentary and prospectus. In: Primates and Their Relatives in Phylogenetic Perspective (ed. R.D. MacPhee) 333–62 (Plenum Press, New York, 1993).

20. Barton, R.A. Visual specialization and brain evolution in primates. Proceedings of the Royal Society B: Biological Sciences 265, 1933–7 (1998). 21. Bryant, K.L. & Preuss, T.M. A comparative perspective on the human temporal lobe. In: Digital Endocasts (ed. E. Bruner, O. Emiliano, & T. Naomichi) 239–58 (Springer, Japan, 2018). 22. Gurche, J.A. Early primate brain evolution. In: Primate Brain Evolution: Methods and Concepts (ed. E. Armstrong & D. Falk) 227–46 (Plenum, New York, 1982). 23. Radinsky, L. The fossil record of primate brain evolution. In: 49th James Arthur Lecture on the Evolution of the Human Brain, 1–27 (American Museum of Natural History, New York, 1979). 24. Radinsky, L. Primate brain evolution. American Scientist 63, 656–63 (1975). 25. Wong, P. & Kaas, J.H. Architectonic subdivisions of neocortex in the tree shrew (Tupaia belangeri). Anatomical Record (Hoboken) 292, 994–1027 (2009). 26. Dunbar, R.I.M. The social brain hypothesis and its implications for social evolution. Annals of Human Biology 36, 562–72 (2009). 27. Dunbar, R.I.M. & Shultz, S. Understanding primate brain evolution. Philosophical Transactions of the Royal Society of London, B: Biological Sciences 362, 649–58 (2007). 28. DeCasien, A.R., Williams, S.A., & Higham, J.P. Primate brain size is predicted by diet but not sociality. Nature Ecology and Evolution 1, 112 (2017). 29. Finarelli, J.A. & Flynn, J.J. Brain-size evolution and sociality in Carnivora. Proceedings of the National Academy of Science USA 106, 9345–9 (2009). 30. Allen, K.L. & Kay, R.F. Dietary quality and encephalization in platyrrhine primates. Proceedings in Biological Science 279, 715–21 (2012). 31. Sayres, K. On folivory, competition, and intelligence: generalism, overgeneralizations, and models of primate evolution. Primates 54, 111–24 (2013). 32. Parker, S.T. & Gibson, K.R. Object manipulation, tool use and sensorimotor intelligence as feeding adaptations in cebus monkeys and great apes. Journal of Human Evolution 6, 623–41 (1977). 33. Schall, J.D., Zinke, W., Cosman, J.D., Schall, M.S., Paré, M., & Pouget, P. On the evolution of the frontal eye field: comparisons of monkeys, apes, and humans. In: Evolution of Nervous Systems (ed. J.H. Kaas & T.M. Preuss) 4, 249–75 (Elsevier, Amsterdam, 2017). 34. Robinson, D.A. & Fuchs, A.F. Eye movements evoked by stimulation of frontal eye fields. Journal of Neurophysiology 32, 637–48 (1969). 35. Bruce, C.J., Goldberg, M.E., Bushnell, M.C., & Stanton, G.B. Primate frontal eye fields: II. Physiological and anatomical correlates of electrically evoked eye movements. Journal of Neurophysiology 54, 714–34 (1985). 36. Gottlieb, J.P., Bruce, C.J., & MacAvoy, M.G. Smooth eye movements elicited by microstimulation in the primate frontal eye field. Journal of Neurophysiology 69, 786–99 (1993). 37. Mitz, A.R. & Wise, S.P. The somatotopic organization of the supplementary motor area: intracortical microstimulation mapping. Journal of Neuroscience 7, 1010–21 (1987). 38. Fujii, N., Mushiake, H., & Tanji, J. Distribution of eye- and arm-movement-related neuronal activity in the SEF and in the SMA and pre-SMA of monkeys. Journal of Neurophysiology 87, 2158–66 (2002). 39. Fujii, N., Mushiake, H., & Tanji, J. An oculomotor representation area within the ventral premotor cortex. Proceedings of the National Academy of Science USA 95, 12034–7 (1998). 40. Baldwin, M.K. & Kaas, J.H. Cortical projections to the superior colliculus in prosimian galagos (Otolemur garnetti). Journal of Comparative Neurology 520, 2002–20 (2012). 41. Stepniewska, I., Pouget, P., & Kaas, J.H. Frontal eye field in prosimian galagos: intracortical microstimulation and tracing studies. Journal of Comparative Neurology 526, 626–52 (2018). 42. Sussman, R.W., Rasmussen, D.T., & Raven, P.H. Rethinking primate origins again. American Journal of Primatology 75, 95–106 (2013). 43. Cartmill, M. Rethinking primate origins. Science 184, 436–43 (1974). 44. Shadmehr, R. & Wise, S.P. The Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning (MIT Press, Cambridge MA, 2005).

* As mentioned earlier, this term is not meant to be taken literally. If any primates were strictly solitary, they would bear no progeny. Instead, it’s a relative description of animals that forage alone, for the most part. Solitary primates exhibit many social behaviors, including acoustic and olfactory communication, grooming, and infant care.

16 Anthropoid augmentations Overview Long after the Eocene expansions, the cortex enlarged again during the Miocene, independently in three anthropoid lineages: platyrrhines, cercopithecoids, and hominoids. These grade-shifts didn’t occur because of complex social systems, frugivory, diurnal foraging, or predation threats per se. All these factors existed before the cortex enlarged. Instead, changes in and interactions among them drove cortical enlargement. In platyrrhines and cercopithecoids, the grade-shifts occurred during the middle-to-late Miocene, a time when global cooling stressed their forest habitats and caused resource volatility. In hominoids, which were monkey-size then, cortical expansion occurred during the early Miocene, a time of stable climate but also of new, more effective predators. In all three anthropoid clades—platyrrhines, cercopithecoids, and hominoids—individuals incurred a risk of predation during long-range foraging journeys. Accordingly, improvements in foraging choices, guided by representations in anthropoid-specific cortical areas, provided a selective advantage by decreasing the frequency of unproductive excursions, which incurred costs without benefits. out of nowhere the hyenas . . . see the hominid group. . . . [A] mother hesitates, and she and the youngster are the first victims. One of the males trying to draw off the hyenas . . . is soon overtaken and ripped apart by huge jaws. . . . Within 30 minutes the carcasses are completely consumed; little is left for the jackals or vultures from these relatively small prey. —Donna Hart and Robert W. Sussman, Man the Hunted, Westview Press, 2005, p. 250.

Introduction In this chapter, anthropoids aggregate; mobsters keep their enemies close; and an abduction occurs. But I begin with the epigraph. Ancestral anthropoids might have encountered hungry predators only rarely, but danger was ever-present (Box 16.1). Despite the risks, to obtain food they needed to traverse long distances, sometimes in open terrain. In order to gain insight into the driving forces behind cortical expansion, this chapter combines ideas about cortical maps in anthropoids (Chapter 13) with the fossil record, which constrains estimates about when the cortex enlarged (Chapter 8). Box 16.1 Predation on primates Hart1 (p. 27) introduced the topic of primate predation by noting that: “Erroneous assumptions concerning predation as a demographic variable find their way into published comments. One commonplace, but erroneous, assumption is that ‘mortality due to predation appears to be negligible’ (Dunbar, 1988, p. 53). Opinions have ranged from a belief that the role of predation in primate evolution is minimal . . . to theories that predation is a powerful force in shaping social patterns . . . .” Predation has been suggested as a driving force for large bodies, long interbirth intervals, and restriction to one or another layer of the forest ecosystem. One problem in understanding predation on primates is that field primatologists have often reported that it’s rare in the wild, sometimes as low as 1% of a population annually. Against this view, field studies that focused on predators have recorded a much higher rate of predation on primates, which differs geographically.1 Primate predators include raptors (hawks, eagles, and owls), carnivores (lions, tigers, wild dogs, jackals, weasels, hyenas, bears, racoons, civets, genets, and mongooses), and reptiles (crocodiles, snakes, and large lizards).32 The risks to primates vary by region. In Africa, for instance, members of the cat family (felids) and raptors kill many more primates than do members of the dog family (canids), hyenas, or snakes (among other reptiles). In the New World, felid predation is relatively rare. Instead, raptors account for the most primate kills there. In contrast, raptors take relatively few primates in Asia. Despite a differing mix of predators, the annual predation rate is similar in New World and Old World primates. Among diurnal species, the rate varies from ~4–7%.1 During the Miocene and Plio-Pleistocene, the roster of predators differed from modern ones, but primates of these epochs also faced serious risks. During the Vallesian crisis of ~10–9 Ma in Spain, two sabertoothed cats—one the size of a leopard (~40 kg), the other the size of a tiger (~150 kg)—hunted herbivores, including primates, in a densely wooded part of the ecosystem.2 With strong forelimbs and dewclaws (a rough analogy of primate thumbs), sabertoothed cats pinned prey before using their massive canines to transect the trachea, jugular, or carotid of their next meal. Sabertoothed cats hunted in more open woodlands over most of the world from ~15–2 Ma, including during the Vallesian crisis.2 In addition, giant bear dogs (Amphicyonidae), which weighed ~190 kg, also preyed on primates in open woodlands.3 In Europe, fossil macaques co-existed with carnivores, including canids and hyenas, but felids probably accounted for most predation on primates.4 To counter these threats, primates have evolved a variety of defense mechanisms, including crypsis (hiding); flight (especially leaping); predator-recognition mechanisms based on vision, audition, and olfaction (some of which are innate); social groups that emit alarm calls and respond by mobbing predators or escape; and living with other species that collectively provide protection (polyspecific associations).5 As the flow chart in Figure 6.5 indicates, seasonal resource volatility was a major challenge for anthropoids of the Oligocene and Miocene. The dramatic and abrupt cooling of the Eocene–Oligocene climatic transition (EOCT) led to largebodied anthropoids that foraged over an extensive territory and risked predation in the process. Eventually, anthropoids evolved a large cortex, complex social groups, a capacity for speedy locomotion, and specializations for predator detection, along with changes in life history (e.g., gestation periods, reproduction rate, longevity, and maturation times). But these changes didn’t happen all at once or for the same reasons.

Of the many anthropoid adaptations depicted in Figure 6.5, a large literature attempts to reduce the influences on cortical expansion to one or two, usually just one. Candidates have included life-history factors such as the energy that mothers invest in their offspring6 and the long lives that primates have.7,8 Particularly popular are ideas about the complexity of primate social systems, in general,7,9 or specific aspects of primates societies, such as monogamy.10 Physical abilities, such as the neural mechanisms needed to grasp and manipulate items,11 dietary preferences that make intensive demands on foraging behavior,12 and the habit of foraging in daylight over a large territory13 populated by predators14,15 have all been advanced as the single most important factor selecting for a large cortex. I begin with the most popular idea, which emphasizes social factors. Groups, grub, and gray matter Chapter 15 (“Grub versus groups”) introduced the social-brain hypothesis. It holds that the cognitive demands of complex primate societies selected for a larger cortex, which could better manage social interactions, represent the mental states of group members, identify individuals, and recognize their ranks in a social hierarchy. For the most part, this idea relies on correlations between brain or cortex size and group size in modern primates.

Figure 16.1 Relation between clique size and corticalization. Large plus signs show group means, color-coded according to the key. The dashed horizontal line displays the human corticalization value, without linking it to any clique size. Adapted from R.I.M. Dunbar and S. Shultz, Understanding primate brain evolution, Philosophical Transactions of the Royal Society of London, Series B: Biology 362, 649–58, 2007.© Oxford Publishing Limited

Figure 16.1 illustrates one such correlation, adapted from Dunbar and Shultz.9 It shows a group regression of corticalization on clique size: the number of individuals that interact regularly with each other. As Chapter 15 explained, most strepsirrhines have solitary social systems (Figure 15.3), and they also have a smaller brain and cortex than anthropoids (Figures 7.1 and 8.2A). Because anthropoids tend to live in large groups, any analysis that samples both strepsirrhines and anthropoids should find a correlation like the one illustrated in Figure 16.1. Like all correlations, inferences about causality require additional information. Here’s some additional information: Fossil evidence indicates that the timing of evolutionary changes doesn’t support the social-brain hypothesis. The key question is: Did big brains and complex societies evolve close to each other in time? The social-brain hypothesis predicts that they did, but fossil evidence suggests that they didn’t. Instead, complex social groups probably evolved long before the Miocene grade-shifts in cortex size.

• •

Cortical expansion. Chapter 8 presented fossil evidence indicating that the cortex and frontal lobe expanded independently in cercopithecoids and platyrrhines during the middle-to-late Miocene. The hominoid grade shift occurred during the early Miocene, also independently. The origin of anthropoid societies. Sexual dimorphism for body size and large, sexually dimorphic canine teeth reflect intensive competition among males for mating opportunities in multi-male, multi-female groups. These traits provide clues about when complex social and mating systems evolved in anthropoids. Fossil evidence for sexual dimorphism suggests that they emerged by the early Oligocene.16,17 Another estimate comes from body size. A large body size leads to longer foraging excursions (for patchy nutrients), which increases predation risks. Social groups mitigate these risks, so it’s possible that complex primate societies evolved at the same time as larger bodies and quadrupedal locomotion.18 Figure 6.4 shows the time-course of body-size growth during Oligocene. By the middle Oligocene, anthropoids had entered the several-kilogram range that persists for monkeys today, and small-bodied anthropoids died out.

Thus, both sexual dimorphism and large body mass point to an origin of complex social systems during the Oligocene, before the upward grade-shift in cortex size, which occurred during the Miocene. A case-in-point involves the African catarrhine Aegyptopithecus, which lived before the hominoid–cercopithecoid split (Figure 16.2). Aegyptopithecus was an anthropoid that lived during the early Oligocene, ~30 Ma. Individuals weighed 5–6 kg, but they had a brain- and cortex size at the prosimian grade, if that (Figure 8.2B), with a small frontal lobe (Figure 8.3B). According to Simons et al. (p. 8735),19 Aegyptopithecus “was highly sexually dimorphic in postcanine [premolar and molar] tooth size, craniofacial morphology, brain size, and body mass.” Males also had much larger canine teeth than females.19 As mentioned earlier, a high degree of body-size dimorphism and large, sexually dimorphic canine teeth correlate with intense male–male competition, which suggests that they lived in complex multi-male, multi-female societies.16,17 On this basis, Simons et al. (p. 8735)19 concluded that “it is now evident that hypotheses linking anthropoid neocortical expansion to diurnality, frugivory, and/or large social groups make little sense when these small-brained early anthropoids are taken into account.” The same conclusion applies to monogamy,10 longevity,7,8 manual dexterity,11 maternal investment,6 long-range foraging while using quadrupedal locomotion,13,14 and predator avoidance.13,14,15 As principal factors favoring a larger cortex, they all fail the timing test because these aspects of anthropoid life evolved before the cortex expanded into the modern anthropoid size range. A prosimian-size cortex, and the complement of areas that it contained, empowered anthropoid success for millions of years. Statistics and significance Chapter 15 (“Grub versus groups”) mentioned the work of DeCasien et al.,12 who tested both social- and ecological accounts of large encephalization-quotient (EQ) values and came down in favor of the latter, stressing dietary specializations. However, there are three problems, as I see it, with the literature on social versus ecological factors. First, EQ values or corticalization percentages only apply to the entire brain or cortex. They can’t detect the emergence of new areas that—while significant in functional terms—are too small to cause a statistically detectable increase in whole-brain or whole-cortex measures. Second, comparisons of modern species leave out something important: the lives of the extinct primates in which cortical expansions occurred. Studies based on modern species rarely consider the problems and opportunities faced by ancestral ones, which sometimes lived in ecosystems different from those of any modern species. And third, such analyses typically emphasize one factor, or on rare occasions two, and ignore or downplay all the others. For example, enlargement of ventral parts of the medial prefrontal cortex has been linked to complex foraging strategies and omnivory,20 as well as internally motivated behavior.21 These ideas seem to contradict the social-brain hypothesis. On the other hand, expansion of the temporal–parietal junction (TPJ) has been linked to increases in social complexity,22 which seems to support the social-brain hypothesis. And there are similar claims about areas involved in predator detection and avoidance, extractive foraging, and other selective factors. Proposal 1 (p. 283) suggests a different way of looking at such data, one that takes ancestral lives into account. Anthropoids have new and expanded cortical areas (Chapter 13) with novel conjunctive representations, some of which support foraging efficiency, predator avoidance, social contributions to fitness, and various combinations of these functions. Anthropoids exhibit many homologies in social behavior, which probably depend on homologous cortical areas23 and homologous transcortical networks.24,25,26,27,28 Regardless of whether these areas and networks expanded enough to change EQ- or corticalization values significantly (in the statistical sense), they improved the fitness of individuals significantly (in the biological sense). So, a form of the social-brain hypothesis—one that doesn’t rely on EQ values or corticalization percentages—might be valid even if other forms, which depend on such measures, are not. The same caution holds for ecological cognition. A small cortical area that contributes to detecting and discriminating the sights or sounds of feeding animals or predators could have provided significant survival advantages without causing a statistically significant change in whole-brain measures. The next section returns to this idea. In summary, fossil evidence indicates that anthropoids were once frugivorous, long-living, diurnal, dexterous, far-ranging, K-selective,* predator-avoiding, several-kilogram animals with complex social and mating systems, but with a brain and cortex of the size expected for a modern prosimian (or smaller). Something else, which occurred later, drove cortical enlargement. Climate and cortex Like the Eocene grade-shifts in earlier primates (Chapter 15), global climate change provides some clues about what this “something else” might have been, at least for some lineages. Proposal 6, which comes in the next section, posits that both the warming and cooling trends of the Miocene were important: warming for hominoids; cooling for platyrrhines and cercopithecoids.

Figure 16.2 Anthropoid synapomorphies. The color of each line corresponds to a grade of corticalization and encephalization, with synapomorphies and extinct anthropoids in italic type. Green arrows mark the onsets of three periods of global cooling (see Figure 4.4). The gray-shaded areas mark the periods of global cooling that began ~40 Ma and ~14 Ma. Abbreviations:Δ, change in a morphological trait; EOCT, Eocene–Oligocene climatic transition; Ma, million years ago; MMCT, middle Miocene climatic transition; P-P, Plio-Pleistocene.

Figure 16.2 replicates part of Figure 8.12 and adds selected synapomorphies. As noted earlier, cortical expansions occurred sometime after ~16–15 Ma in platyrrhines and cercopithecoids, and somewhat earlier in hominoids, before ~18 Ma, but not infinitely earlier because hominoids split from cercopithecoids ~26–23 Ma. As the figure illustrates, all three of these gradeshifts occurred in parallel and are therefore examples of size homoplasy. Figures 4.4 and 9.1 illustrate a long period of relative thermal stability during the Oligocene and early Miocene, followed by a warming trend that developed ~18 Ma: the middle Miocene climatic optimum (MMCO). Warm and moist conditions led to an expansion of tropical and subtropical forests at that time. Then, ~14 Ma, a period of global cooling called the middle Miocene climatic transition (MMCT) began, and forests began to contract, as they had during the late Eocene cooling of ~40–34 Ma. The MMCT lasted ~1.5 million years, and it nearly matched the Eocene–Oligocene climatic transition (EOCT) in amplitude. Like the EOCT, the MMCT increased the seasonal volatility of food production, and it caused a shift in ecosystems from dense tropical and subtropical forests to dry forests and open woodlands throughout most of North America, Europe, and Asia (Figure 6.7C). After the MMCT, global cooling decelerated until ~9.6 Ma, when it resumed. This later period of climate change prompted what’s called the Vallesian crisis, a time of rapidly changing ecosystems and the additional contraction of dense forests. African and Arabian catarrhines diversified during the period of stable or warming global temperatures before and during the MMCO. Hominoids emerged ~26–23 Ma, and they became larger animals, in the (monkey-like) several-kilogram range, ~20 Ma. Afterward, during the warm MMCO (~18–15 Ma), hominoids migrated into Europe and Asia,29,30 and from ~17–15 Ma they diversified extensively and evolved the traits that characterize modern apes. As they did, hominoids took their large, hominoid-size cortex with them and used it to exploit the lush rainforests that thrived during the MMCO.

The sequence in monkeys was different. Like hominoids, cercopithecoids diverged from their common catarrhine ancestor ~26–23 Ma. The cooling trends that began ~14 Ma (the MMCT) and the Vallesian crisis of ~9.6 Ma caused a major extinction of hominoids, which resulted in ape species becoming relatively rare, as they remain today. In contrast to the dwindling number of ape species, late-Miocene conditions fostered the radiation and diversification of the previously rare cercopithecoids, which spread widely in Europe, Asia, and Africa. As omnivores and ecological generalists, with high reproduction rates and a fast maturation rate compared to apes, cercopithecoids (especially cercopithecines) adapted successfully to life in dry forests and open woodlands.31 Cercopithecoids thus had a long “fuse” between their origins ~26– 23 Ma and their major radiations ~7 Ma (Figure 6.7). Something similar happened among South American platyrrhines.32 Figure 6.6 illustrates fossil evidence indicating that a shift in locomotion occurred at roughly the same time as the upward grade-shifts in hominoid, cercopithecoid, and platyrrhine cortex. Like the cortical expansions: (1) A transition to new modes of locomotion occurred earlier in hominoids than in monkeys; and (2) these evolutionary changes occurred independently in all three anthropoid clades.33 The ancestral condition, represented by Aegyptopithecus, involved flexible—but slow and cautious—movement over large branches. As Almécija et al.33 put it (p. 6): “Aegyptopithecus is best reconstructed as a cautious above-branch arboreal quadruped incorporating some climbing and leaping . . . ” For hominoids, changes in femur morphology marked a shift to enhanced hip abduction* and more upright locomotion (called antipronograde in the specialty literature); in cercopithecoids, it enhanced flexion and extension of the hip in support of speedier quadrupedal (cursorial) locomotion and leaping. Figure 16.2 situates these evolutionary changes, all of which involved new and speedier ways of moving long distances, on a chronogram of anthropoids. There’s one final point to introduce before propounding a proposal. Earlier chapters have emphasized the importance of vision in primates, but with the advent of the haplorhine fovea, this dominance developed to a new level in anthropoids. Perhaps the best way to illustrate it is with a field study of one cercopithecoid species, as summarized in The Neurobiology of the Prefrontal Cortex (p. 55):34 The daily life of one anthropoid species illustrates the importance of the fovea. Struhsaker35 . . . reported field studies of the red-tail monkey (Cercopithecus ascanius), a catarrhine monkey. He found that they spend 21% of each day scanning their visual world, presumably for fruits, insects, and predators. They spend an additional 17% of the day travelling from place to place to obtain resources, based presumably on what they have seen. Once they reach a fruitful location, their feeding time takes up 34% of their day. Taken together, the time spent looking around and acting on what they see amounts to more than 70% of their active time. Of the remaining hours, these monkeys spend 10% resting and 5% engaged in social interactions, such as grooming. The proportions will vary from species to species, but red-tail monkeys seem representative enough to make the point: with a new mechanism to gain better information at a distance, anthropoid primates look around a lot.

The previous section concluded that the advent of diurnality, frugivory, predator avoidance, arboreal quadrupedy, massive maternal investment in progeny, and large social groups can’t account for cortical enlargement in anthropoids. These features were all in place before the cortex expanded into the modern anthropoid size range, which leads to the proposal that takes up the remainder of this chapter. Principal proposal Proposal 6. The independent cortical grade-shifts in hominoids, platyrrhines, and cercopithecoids depended on expansion of anthropoid-specific areas, especially the prefrontal cortex. Areas and representations. Anthropoid-specific prefrontal areas evolved as adaptations for long-distance, vision-based foraging. The generation of behavioral goals by the dorsolateral (PFdl), dorsomedial (PFdm), ventrolateral (PFvl), and polar (PFp) prefrontal cortex reduced predation threats by speeding learning and applying 34 36 abstract behavior-guiding rules and strategies, thereby minimizing unproductive foraging choices. , Their unique representations varied among prefrontal areas, but they all combined visual contexts, goals, actions, and outcomes with other types of information. In addition, new visual and auditory areas processed and stored conjunctive representations that provided advantages in making visually and acoustically guided foraging choices at a distance. These areas contributed to anthropoid fitness by improving the detection of predators and distant signs of resources and by providing this information to the prefrontal cortex. Stands of trees and landscapes helped guide such choices, as did the sounds of feeding animals. Similar representations played a role in social behavior. Grade-shifts. The anthropoid-specific prefrontal areas evolved before the Miocene, but they were smaller in ancestral anthropoids than in modern ones.* Then, during the Miocene, vision-based foraging choices became more important as olfaction became less important. As a result, the granular prefrontal cortex expanded while the olfactory bulbs contracted—independently in platyrrhines, cercopithecoids, and hominoids (Figure 8.4C). The fossil record reveals that the frontal lobe expanded after ~16–15 Ma in platyrrhines and cercopithecoids and sometime between ~26–18 Ma in hominoids (Chapter 8), which suggests that the prefrontal cortex enlarged at those times. Thus, the timing and driving forces differed for monkeys and hominoids. Monkeys. During the evolution of cercopithecoids and platyrrhines, global cooling ~14–9 Ma caused rainforests to dry and contract. As their habitat changed, two influences contributed to cortical expansion, and they both resulted from daylight foraging under threat of predation: (1) Resource volatility in forests under stress led to longer and less productive foraging excursions; and (2) this put a premium on detecting resources and predators at a distance and reducing the frequency of unproductive choices. Accordingly, rather than diurnality, frugivory, predation threats, long-range foraging, or complex social groups per se, it was a complex web of changes in these factors, due in large part to global cooling, that drove the cortex of cercopithecoids and platyrrhines into the modern anthropoid size range. Skeletal changes that favored speedy (cursorial) locomotion accompanied cortical enlargement in both groups of monkeys (Figure 6.6). Hominoids. Early hominoid species evolved in a relatively stable climate ~26–18 Ma, when they faced new predators that had relatively large brains and bodies. Compared to ancestral anthropoids, they had become larger animals (in the several-kilogram range, like monkeys) with a new, more upright mode of locomotion by ~18 Ma. Hominoids resembled cercopithecoids then; most of the traits that characterize modern apes and hominins had yet to evolve. Nevertheless, also by ~18 Ma, the hominoid cortex had expanded into its modern size range (Figure 8.2B). It did so because a similar web of factors favored the same kinds of cortical representations as in cercopithecoids and platyrrhines. Then, a period of global warming ~18–15 Ma (the middle Miocene climatic optimum) opened new opportunities for exploiting tropical and subtropical forests, which thrived in wet and warm conditions. Hominoids radiated and migrated from Africa to Asia from ~17–15 Ma. Later, the global cooling of ~14–9 Ma caused extinctions that made apes relatively rare, as they remain today. (At about the same time, cercopithecoids thrived, diversified, and radiated in the Old World, as platyrrhines did in the New World.)

That’s not a summary, it’s a proposal, which enables the expression of these ideas without the caveats required (rightfully) in academic writing: most likely; probably; on balance; etc. Like all such proposals, it requires explanation, beginning with its emphasis on predation risks (Box 16.1). Predation Others have pointed to predation threats as a driving force for primate brain evolution,15 but without the paleoneurological, paleoecological, and comparative neuroanatomical evidence incorporated into Proposal 6. One idea circulating in the neuroscience literature emphasizes snake predation to the exclusion of other factors. Predation was important, but anthropoids had experienced predation long before anthropoid brains expanded into their modern size range. There is, for example, evidence from the Fayum anthropoids of the early Oligocene that a predatory, cat-like species of creodonts preyed on these species. As discussed in Box 12.1, creodonts were predatory placental mammals related to carnivores and pangolins. Tooth marks matching creodont dentition were found in 9–10% of the primate fossils in the Fayum deposits.2 Depending on

the age and breeding status of the victims, this level of predation can exert strong selective pressures; and learning to limit risky foraging journeys surely counts as one such influence. However, as important as predation might have been, many additional ecological factors also contributed driving forces for cortical expansion. Areas In addition to whole-cortex measures such as the EQ, the functions of new and expanded areas provide clues about the selective factors at work. Because there were many such areas, it’s likely that there were many different driving forces. Figures 13.2 and 13.3 identify new anthropoid areas in the prefrontal, inferior temporal, and superior temporal cortex. They specialize in representing behavioral goals, mid-level visual conjunctions, and acoustic conjunctions, respectively, and some temporal areas represent polymodal conjunctions of auditory and visual signals. Inferior and superior temporal areas not only improved foraging choices in Miocene anthropoids, but they also contributed to social behavior, which influenced predator detection and avoidance.* A new somatosensory area, area 2, and a new subdivision of the primary motor cortex, M1c, improved the ability to judge the ripeness of fruits, many of which provided few visual or olfactory clues about their current nutritional value. Together, prefrontal cortex, inferior and superior temporal cortex, area 2, and M1c contributed to foraging efficiency and predator detection, with the prefrontal areas speeding learning in order to limit the number of poor, fruitless choices. Changes The most important word in Proposal 6 is “changes,” and it appears in the proposal’s key sentence: “rather than diurnality, frugivory, maternal investment, predation threats, long-range quadrupedal locomotion, or large social groups per se, it was a complex web of changes in these factors that drove the cortex of cercopithecoids and platyrrhines into the modern anthropoid size range.” Proposal 6 posits that global cooling during the middle-to-late Miocene (~14–9 Ma) caused many of these changes in cercopithecoids and platyrrhines, albeit indirectly and independently in the New- and Old Worlds. The warm and conducive conditions of the early Miocene led to similar changes among hominoids as they faced the arrival of new predators in Africa and Arabia before radiating, diversifying, and, in many cases, migrating to Asia. Complex social systems, frugivory, diurnal long-distance foraging, and predation threats all seem to have influenced cortical expansion because all these factors changed—in a complex, interactive way—during the Miocene. Prefrontal cortex Figure 16.3 summarizes several ideas explained and documented previously in The Evolution of Memory Systems.37 In Chapter 1, I said that I would have failed if readers needed to consult other sources to understand the text or figures in this book. However, this promise carved out an exception for Part V, and this is it. Several secondary sources support the ideas summarized here. In addition to The Evolution of Memory Systems, a recent book on the prefrontal cortex by Passingham, Understanding the Prefrontal Cortex,36 updates an earlier monograph we wrote together.34 Both books develop the case for the functional specializations of prefrontal areas posited in the proposal and summarized below. Experimental studies on macaque monkeys and humans, reviewed in these three sources,34,36,37 support the brief summaries of prefrontal cortex functions in the next paragraph.

Figure 16.3 The sources of visual feature conjunctions in frontal cortex: dorsal and ventral visual streams. The color-code matches in the macaque hemisphere at the top and the box diagram at the bottom. Abbreviations: ES, extrastriate visual cortex; FEF, frontal eye field (and other parts of area 8); PFdl, dorsolateral prefrontal cortex; PFdm, dorsomedial prefrontal cortex; PFo, granular orbital prefrontal cortex; PFp, polar prefrontal cortex; PFvl, ventrolateral prefrontal cortex; PM, premotor cortex; PP, posterior parietal cortex; V1, primary visual (striate) cortex. Adapted from E.A. Murray, S.P. Wise, and K.S. Graham, Evolution of Memory Systems: Ancestors, Anatomy, and Adaptations, Oxford University Press, Oxford, 2017.© Oxford Publishing Limited

Figure 16.3 relies on the representational perspective outlined in Chapter 15 (“Representations revisited”). It extends the idea of conjunctive representations, as illustrated in Figure 15.2, in two ways: from the dorsal and ventral visual streams of the parietal and temporal lobes, respectively, to the frontal lobe; and from conjunctions of visual features to combinations of information that transcend sensory features, such as goals and outcomes associated with specific values. The dorsal visual stream provides the granular prefrontal cortex with metric contexts: information about relative location, number, order, speed, duration, distance, etc. The ventral visual stream provides different parts of the granular prefrontal cortex with attribute (feature) contexts: patterns of colors and shapes that serve as signs of food at a distance; and the visual texture, glossiness, colors, and shapes of objects. These aspects of vision are incorporated into prefrontal representations of goals, actions that achieve these goals, and the outcomes of previous foraging choices in a similar context. Together, they generate visual affordances attached to outcomes and their value. These representations include the conjunction of goals with other information, such as: (1) a goal’s current desirability (the principal function of the granular orbital cortex, PFo); (2) a goal’s current availability (a function of the ventrolateral prefrontal cortex, PFvl); (3) a goal’s place in an ordered sequence of goals (a function of the dorsolateral prefrontal cortex, PFdl); (4) actions and outcomes associated with a goal, including effort costs (a function of the dorsomedial prefrontal cortex, PFdm); (5) a goal’s location in a visual scene (a function of the polar prefrontal cortex, PFp); and (6) a goal’s location in both visual and motor coordinates (a function of the premotor areas, PM). These functions provide specific adaptive advantages. When sensory inputs provide insufficient guidance for foraging choices, PFdm has the largest influence on the choice because it represents conjunctions of internal contexts and goals;36 when making choices based on visual metrics, PFdl has the largest influence because it represents conjunctions of quantitative external contexts and goals;38 and when making choices based on visual signs associated with food availability, PFvl has the dominant role because it represents conjunctions of qualitative external contexts, such as colors and shapes, with goals. In macaques, PFp contributes to one-trial learning that guides choices within a visual scene, a capacity that relies upon learning rapidly about objects and rules in novel behavioral situations.39 Considered together, these new anthropoid areas support an improved capacity for rapid learning about foraging choices, which reduces poor choices and therefore predation risks. Error reduction comes from rapid learning, often based on only one or two experiences, as well as from the application of abstract rules and strategies.40 Visual foraging The emphasis on foraging in Proposal 6 shouldn’t be construed as excluding social cognition, but I think that foraging-related predation threats generated stronger selective factors (Box 16.1), especially during the early phases of cortical expansion. As mentioned earlier, complex societies evolved before the cortical grade-shifts of the Miocene, but this doesn’t rule out a contribution of social factors later. As the cortex enlarged in response to foraging demands under threat of predation, social factors contributed because they benefited from some of the same conjunctive representations as foraging choices. Regardless, social life and predator avoidance are closely linked in anthropoids because social groups contribute to predator detection. This benefit comes at the cost of intragroup competition for resources, which many (but by no means all) anthropoids partially resolve via strict social hierarchies. Likewise, the focus on vision in this discussion shouldn’t be construed as excluding audition, but I think that vision played the predominant role. The inferior temporal cortex has strong connections with PFvl, and in this context it should come as no surprise that auditory areas in the superior temporal cortex also send axonal projections to the same part of the prefrontal cortex.41,42 Conjunctions of visual and auditory information have generated considerable interest in the literature because of their importance in social cognition and especially social signaling. However, combinations of sights and sounds are also important for making good foraging choices. Finally, Proposal 6 emphasizes the prefrontal cortex more than visual areas because, in cercopithecoids at least, the temporal cortex had already undergone much of its expansion during the early Eocene, before the upward grade-shifts of the middle-to-late Miocene (Chapter 7). Chapter summary None of the most commonly cited selective factors account for cortical expansion in anthropoids. The published proposals include social complexity, frugivory, predation, longevity, maternal investment, monogamy, long-range diurnal foraging journeys, and extractive foraging strategies, but they were all in place millions of years before the cortex reached the anthropoid and hominoid grades (Chapter 8). Instead, a complex web of changes in such factors selected for a larger cortex during the Miocene. In cercopithecoids and platyrrhines, ecological challenges of the middle-to-late Miocene led indirectly to cortical expansion. Global cooling placed tropical and subtropical forests under stress, and an increase in resource volatility led to an increase in unproductive foraging excursions, each of which entailed a risk of predation. Prefrontal areas improved foraging choices and thus reduced predation risks, so selection favored their expansion (Proposal 1, p. 283), sometime after ~16–15 Ma. Neural representations in new temporal areas, along with evolutionary changes in older visual areas, improved the identification of both visual and acoustic signs of food and predators, so they enlarged, too. For hominoids, the influences were different. Rather than pressures from cooling-stressed forests during the middle-to-late Miocene, ecological factors of the early Miocene contributed to cortical expansion in hominoids, by ~18 Ma. However, these ancestral species didn’t resemble modern apes. Instead, they were smaller and more agile than modern hominoids (gibbons excepted), with monkey-size bodies. As these hominoids coped with new predators in warm and moist rainforests, the challenges of long-range foraging and the threat of predation probably drove both group life and the upward grade-shift in cortical extent. The Miocene grade-shifts in platyrrhines and cercopithecoids have nothing to do with human evolution; they occurred in lineages that led to modern monkeys. The cortical expansion in Miocene hominoids is a different matter, however, and the next chapter turns to one group of their descendants. Most hominoids had become extinct by the end of the Miocene, which shows that more than a large, hominoid-grade cortex was needed to cope with shrinking rainforests. The resulting expansion

of open woodlands and savannas provided new opportunities, but also new risks. One group of hominoids exploited these opportunities and mitigated these risks, and we are their descendants. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42.

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* The term K-selective refers to species that produce relatively few progeny but have high levels of parental investment in offspring, which leads to higher survival-tomaturity rates than the alternative reproductive strategy, called r-selective. * Abduction, in this sense, doesn’t mean taking someone by force. It refers to the movement of the upper leg laterally. Forward and backward leg movements are called flexion and extension, respectively. * As mentioned earlier, it’s probably unknowable whether the entire complement of prefrontal areas existed in ancestral anthropoids before the platyrrhine–catarrhine split. In this book, I’ve taken what I consider to be the conservative view, which is that they did. Otherwise, areas with similar designations in platyrrhines and catarrhines, such as areas 9, 10, 11, 12, 45, and 46, would be homoplasies and not homologies. Perhaps, as Proposal 1 (p. 283) suggests, discrete areas matter less than the diversity of conjunctive representations. As Figure 13.6 indicates, neuronal assemblies subserving a given type of representation might be a small part of a recognized area or, in other species, they could be extensive enough to merit recognition as a separate area. * Mobsters, like those in The Godfather, keep their friends close but their enemies closer; monkeys prefer to keep a safe distance from enemy predators.

17 Pleistocene prizes Overview Cortical expansion accelerated in Pleistocene hominins, resuming a phylogenetic trend that began in anthropoids of the late Eocene and resumed in hominoids of the early Miocene. Many of the areas involved in this grade-shift were cortical innovations of anthropoids, including large parts of the granular prefrontal, posterior parietal, and temporal cortex. Novel representations in these areas supported the functions commonly suggested as drivers for cortical expansion in humans, such as social cooperation for hunting and gathering, tool manufacture, teaching and imitation, relational reasoning, spoken language, and episodic memory. However, two additional functions may have been more important: cultural knowledge, also known as semantic memory; and the limitless imagination of alternative futures, along with counterfactual pasts. The latter function, called constructive episodic simulation, depended on expansion of both the hippocampus and typically layered areas of cortex, especially prefrontal areas. Emergent properties included a human-specific sense of self and society, including large-scale, geographically dispersed cultures.

Introduction In this chapter, long-ago lives leave a legacy; chimpanzees bring the hammer down; and daydreamers pose a danger. But I begin with a topic of unremitting narcissism: the human cortex gazing lovingly at itself. I’m mindful of the countless articles and books devoted to the human cortex and its evolution, but this chapter differs from them in two principal ways. First, I focus on representations in the cortex, as advanced in Proposal 1 (p. 383). Second, rather than adopting the prevailing narrative about human societies before the Neolithic agricultural revolution (~10,000 years ago), I explore what insights might arise from a radical reconsideration of Paleolithic and Mesolithic cultures by Graeber and Wengrow1 (“Dreamers of the day”). Suggestions for selection A hunt through the literature enabled the gathering of possible selective factors favoring cortical expansion (Table 17.1). Modified from Passingham’s book on the prefrontal cortex,2 it collects some ideas that experts and novices alike have advanced as selective factors for the Plio–Pleistocene grade-shift in the human cortex. The original version applied to the prefrontal cortex, but it will do for the large, typically layered regions in the parietal and temporal lobes, as well. Table 17.1 Selective factors that might have contributed to cortical expansion in hominins. The list in each column is in alphabetical order, so any row-wise relationship is coincidental Ecology

Behavior & technology

Cognitive capacities

Climate: volatility/stability Environment: constraints/stress

Clothing Fire for cooking

Analogical, metaphorical, and relational reasoning Constructive episodic simulation (imagined events); mental time-travel

Global cooling (Ice Ages) Hominin competitors New environments, new shelters

Fire for protection Social cooperation for defense Social cooperation for foraging

Cross-domain cognition (multiple-demand cognition) Knowledge of shared intentions; a theory of mind; common knowledge Metacognition

New foods New predators

Spoken language Tools for hunting, processing foods

Prospection and mental trial-and-error (over a timespan of decades) Re-representation of oneself, others; autobiographical memory, sense of participation in current, past, and imagined events

New prey

Tools for making tools

Semantic generalizations, categories, and concepts; technological, biological, and social knowledge

Adapted from R. E. Passingham, Understanding the Prefrontal Cortex: Selective Advantage, Connectivity, and Neural Operations, Oxford University Press, Oxford, 2021.

This chapter addresses a selection of these possibilities in five sets: (1) cross-domain cognition; (2) relational reasoning; (3) cultural knowledge; (4) tool manufacture and use; and (5) social factors, including spoken language and representations of oneself and others. This section, “Suggestions for selection,” addresses the first four of these ideas; “Self and social systems” tackles the last one. Chapter 13 (“Guilt by association”) explained a little about the history of association cortex as a concept in neuroscience, and why it has made a comeback recently. The main reason is the recognition that typically layered areas, usually classified as homotypical association cortex, expanded the most during human evolution (Chapter 14). These areas have homologs in other anthropoids, but that doesn’t mean that they have the same function in human and nonhuman species. As Chapter 2 explained, homologs often change function during evolution. This concept is important because it bears on when human-specific cognitive capacities evolved. For example, macaques have homologs of cortical areas that compose what’s called the multiple-demand system in humans, a function related to general intelligence.3 As proposed by Duncan and his colleagues,4 multiple-demand cognition draws on information from all sensory modalities and cognitive domains. (The terms modality and domain are used in several conflicting ways in the neuroscience literature. Here, examples of sensory modalities are vision, audition, olfaction, etc.; examples of cognitive domains are social, technological, and biological knowledge, all of which are aspects of cultural, semantic memory.) If the macaque homologs perform the same functions as in humans, then multiple-demand cognition probably evolved during the Oligocene or earlier. If not, which I believe to be the case, then this cognitive capability evolved much more recently.

Five proposals in The Evolution of Memory Systems5 posit the latter, which implies that cortical homologs in macaques and humans changed function during hominoid, hominid, and hominin evolution. There we proposed that: (1) Multipledemand cognition required human-specific functional changes in the prefrontal and posterior parietal cortex;3 (2) posterior parietal areas that represented relative metrics in ancestral anthropoids transitioned to a role in relational reasoning in humans;6 (3) temporal areas that represented visual and acoustic categories in ancestral anthropoids adapted to a role in semantic memory in humans;5, 7 (4) the technological knowledge underlying tool manufacture and use required changes in parietofrontal and temperofrontal networks; and (5) changes in representations of oneself underlie hominin-specific social systems, as well as spoken language, autobiographical and episodic memory, and the limitless imagination of events: for both alternative futures and counterfactual pasts. My coauthors and I advanced these ideas in detail elsewhere,5 and I won’t repeat much of that here. Instead, I offer the following proposal, which covers much of the same ground: Proposal 7. Typically layered areas in ancestral anthropoids were exaptations for human-specific cognitive capacities. In humans, typically layered areas in the temporal, parietal, and frontal lobes—commonly called association cortex or homotypical association areas—form several transcortical networks that function in multiple-demand cognition (also known as cross-domain cognition).4,8 They represent relations across all cognitive domains3,4,8,9 and thereby empower humans to manipulate and transform relations in limitless, creative ways.10,11,12 These cognitive processes underlie relational reasoning, including analogical and metaphorical reasoning. Representations in typically layered areas also underlie semantic memory and technological knowledge. The homologous networks in ancestral anthropoids didn’t have those roles, but instead had representations involved in specialized sensory or motor functions. Posterior parietal areas represented visual information about relative distances, locations, durations, speeds, number, order, and other metrics, rather than their human function: generalized relationships such as analogies. Inferior temporal areas represented vision-based categories of objects, rather than their specialized human function: semantic generalization and categorization. Prefrontal areas represented immediate behavioral goals, along with contexts, actions, and outcomes associated with goals, rather than their human functions: species-specific representations of personal, long-term goals (intentions), self, and agency. As such, the typically layered cortical areas of ancestral anthropoids were exaptations for human-specific cognitive capacities that draw on multiple cognitive domains, relational reasoning, semantic memory (also known as cultural knowledge), and technological knowledge.

The next four sections address cross-domain cognition, relational reasoning, cultural (semantic) knowledge, and tool manufacture, respectively. Then I advance Proposals 8 and 9, which extend the discussion to self and society. Generalized genius In humans, individual differences in the size of the typically layered cortical areas correlate with both overall brain volume and cortex volume.13 These areas, which are the main ones that expanded during hominin evolution, also develop later in an individual’s life, with maturation times extending through adolescence into young adulthood.14 Resting-state covariance of functional magnetic-resonance imagining (fMRI) signals reveal strong interconnections among the typically layered areas,15 which form transcortical networks (Figure 17.1B). Thus, these areas are linked, not only in individual development (ontogeny) and evolutionary development (phylogeny), but also in connectivity and function.

Figure 17.1 Transcortical networks in humans. (A) Lobes of the human cortex. (B)The color of each region corresponds to the network labeled in the key to the right. (A) Reproduced from E.A. Murray, S.P. Wise, M.K.L. Baldwin, and K.S. Graham, The Evolutionary Road to Human Memory, Oxford University Press, Oxford, 2020. Artwork by Mary K. L. Baldwin. (B) Adapted from J.L. Ji et al., Mapping the human brain’s cortical-subcortical functional network organization, Neuroimage 185, 35–57, 2019.(b) © 2018 Elsevier Inc.

Most tellingly, fMRI signals increase and become correlated in typically layered areas as humans perform tasks that require representations from several cognitive domains.15 In The Evolution of Memory Systems, my coauthors and I referred to the typically layered areas of human cortex as having a “specialization for generalization,” which we proposed to have evolved during the Pleistocene.5 We used the phrase “explosive generalization” for these enhanced cognitive capacities, which promote relational, analogical, and metaphorical reasoning,6 multiple-demand4 and cross-domain15,16 cognition, and what’s often called general intelligence.4,8 All these observations support the idea that expansion of the typically layered areas led to human-specific cognitive functions. But they didn’t arise de novo, they developed from cognitive functions that had evolved earlier, in ancestral anthropoids. Some cognitive specializations of our species arose as the hominin cortex expanded into the modern human range, but others probably evolved in earlier hominoids, in which enlargements of typically layered areas (Figures 14.2D and 14.4) and a reorganization of pathways (Figure 14.6) occurred. But in a larger sense, the phylogeny of human-specific cognitive traits extends much more remotely in time: to the ancestral anthropoids in which new, typically layered areas emerged in the prefrontal, posterior parietal, and temporal cortex (Chapters 13 and 16). According to Proposal 7, they were exaptations for human-specific cognitive capabilities. Reasoning and relations A similar idea applies to the parietal lobe. My coauthors and I proposed that the posterior parietal cortex of anthropoids represents relative metrics such as locations, number, speeds, order, distances, and durations.6 Foraging choices favor larger and closer food items, and the greater in number the better. Such representations provide obvious advantages for frugivores and other anthropoids that forage for patchy and often-distant resources. The order in which an animal explores for resources is also important, especially when foraging in social groups: the best food gets eaten first. The fossil evidence reviewed in Chapter 8 indicates that the cortex of hominoids expanded during the early Miocene, and Chapter 16 pointed to improvements in foraging efficiency—which depend in part on metric representations—as a driving force for that phase of cortical expansion. Cortex and cultural knowledge Based on evidence from neurological patients with semantic dementia, it’s clear that the anterior temporal lobe supports the representations underlying semantic memory. These patients have a form of frontotemporal dementia in which the anterior

and ventral temporal lobe degenerates, leaving more caudal parts of the temporal lobe relatively intact. Fiber tracts connecting the frontal and temporal lobes deteriorate, such as the uncinate and arcuate fascicles (Figure 14.6B). As the temporal lobe degenerates, patients lose much of their knowledge about concepts and categories, along with other generalizations about the world and society. Their impairments occur across all sensory modalities and all cognitive domains. In The Evolution of Memory Systems,5 my coauthors and I proposed that typically layered areas in the temporal cortex adapted from the functions they performed in ancestral anthropoids. These functions involved conjunctions of visual features that underlie the categorization of visual inputs. Figure 15.2 illustrates how a hierarchical organization among temporal visual areas leads naturally to categorization. For example, the intermediate level of representation (“Vis ABC” in the figure) is the category of alligator-skin objects regardless of shape. From this exaptation, the homologous areas establish semantic memories in humans. Put another way, an extension of visual categorization led to the concepts, categories, and generalizations that underlie semantic memory, with a succession of abstractions generating progressively higher hierarchical levels of generalization. Aspects of these capacities could have evolved in earlier hominids (Figure 14.6). It seems likely that driving forces related to semantic memory contributed to the rostral extension of the anterior temporal lobe in humans (Figure 8.10),7 and the comparative evidence illustrated in Figure 14.4 supports the same conclusion. This idea becomes important later (“Dreamers of the day”), when I consider the size and complexity of Paleolithic cultures. Types of tools A large literature addresses the role of Paleolithic technology in cortical enlargement. Fossil evidence and artifacts associated with fossils form the foundation of this literature. Chapter 8 explained that the hominin cortex enlarged mostly during the Pleistocene. Figure 17.2 illustrates that grade-shift in two ways: absolute cortical volume and a phylogenetically corrected measure of relative brain size (the phylogenetic EQ).

Figure 17.2 Technological knowledge and cortical expansion. (A) Absolute size of the brain in hominins, superimposed on three tool-making traditions, which are indicated by background shading. Yellow rectangles indicate periods of global cooling (Ice Ages). The horizontal black bar indicates the time since the earliest Homo sapiens fossils (~315,000 years ago). (B) The phylogenetic encephalization quotient (EQ) of hominins as a function of time. Format as in Part A. (A) Adapted from I.T. Fiddes et al., Human-specific NOTCH2NL genes affect notch signalling and cortical neurogenesis, Cell 173, 1356-1369 e1322, 2018. (B) Adapted from H.P. Püschel et al., Divergence-time estimates for hominins provide insight into encephalization and body mass trends in human evolution, Nature Ecology and Evolution, 5, 808–19, 2021.(A) © 2018 Elsevier Inc

Both parts of the figure show the range of dates for three tool-making traditions: Lomekwian, Oldowan, and Acheulean. Tool use is not unique to humans, and among the many animals that use tools, the last common ancestor of chimpanzees and humans probably did so, as well.17 One of their descendants, chimpanzees, use a hammer-on-anvil technique to crack nuts. One stone serves as a controlled force aimed at a nut supported on another rock, which serves as a platform. A well-aimed blow transfers sufficient force to break open the nut’s husk or shell, exposing its nutrients. That’s tool use by any definition, and some nonhominid anthropoids do the same thing.18, 19 Although the chimpanzee–human common ancestor probably used simple tools, hominins took tool manufacture and use to a new level. No nonhuman species manufactures or uses the broad array of tools that composed the toolkits of the Pleistocene, but the earliest lithic tools were simpler. The first evidence for multiple-use tools for butchering prey and extracting bone marrow dates to the Pliocene, ~3.4–3.3 Ma,20, 21 a time when Australopithecus afarensis lived in Africa along with another hominin called Kenyanthropus platyops (classified by some experts as an australopithecine species). Humans (Homo species) had yet to emerge (Figure 8.7). One association of hominin fossils and artifacts links these simple,

Lomekwian tools to Kenyanthropus;21 another points to a relationship between cutting tools and Australopithecus.20 At first, tool manufacture probably didn’t differ much from the nut-cracking technique that chimpanzees use. Instead of a nut, a flaky stone “core” placed atop a hard stone “anvil” received the impact of a stone “hammer.” This procedure, called bipolar knapping, yields a variable array of sharp flakes that ancestral hominins could use for cutting and scraping. The more complex tools of the later, Oldowan tradition involve conchoidal (shell-like) flaking. These artifacts are associated with Homo habilis fossils from ~2.6 Ma. There’s little doubt that a complex toolkit, produced by tool-making tools, required considerable technological knowledge and visuomotor skill. Cortical areas supporting this knowledge and these skills probably account for some of the Pleistocene grade-shift. The Acheulean tool-making tradition involved yet-more-complex bifacial hand axes with hafting (for attachment to a shaft or sling for support and leverage), and these artifacts appear in association with fossils of Homo ergaster and Homo erectus22 from ~1.6 Ma, with a transition to a new phase ~600,000 years ago, perhaps with Homo heidelbergensis.23 Unambiguous evidence for controlled fire dates from ~1 Ma,24 so this cultural and technological development seems to have occurred while our ancestors used an Acheulean toolkit. It’s clear from Figure 17.2 that an intensive tool-making and tool-using culture, including the Oldowan and Acheulean traditions, accompanied a major cortical expansion. These activities would have benefited from improved representations related to visual affordances—the actions that can be performed on a visible object—and the areas housing such representations expanded during hominin evolution as pathways reorganized. A large neurological literature on apraxia describes what happens when these parietal–premotor areas and networks break down. Self and social systems Love and language Humans developed a complex social system that combined cooperation and resource sharing with decreased sexual dimorphism in body size. Social complexity and cooperativity have long been postulated as selective factors favoring a large human cortex: another aspect of the social-brain hypothesis. Until recently, most anthropologists believed that people lived in small bands of closely related individuals until the advent of agriculture during the Holocene (after ~11,700 years ago). A later section, “Dreamers of the day,” considers alternative ideas, but for now I limit the discussion to small-scale societies. Gathering and sharing food require social interactions, as does hunting, which is often a group activity in which shared intentions play an important role. Social cooperativity is difficult to date from the fossil record, but sexual dimorphism could be a useful proxy. The fossil record shows that the most recent common ancestor of humans and chimpanzees had a high level of sexual dimorphism; males were ~50% larger than females, by weight. In modern humans, males and females differ by only ~15%. A decrease in physical inequality between men and women probably reflects a social system that involved a new division of labor and sharing between the sexes, and it appears to have occurred, or at least accelerated, in Homo species from ~1.6 Ma.23 Figure 17.2B shows that, by then, some Homo species had relatively high EQ values, but they remained well below the modern level. Thus, it appears that some of the increase in EQ and most of the increase in absolute brain volume occurred after the emergence of social systems that included monogamous sharing, if that’s what reduced body-size dimorphism indicates. One aspect of social life, spoken language, contributed to cortical expansion, too, but the timing of its emergence remains uncertain. Dunbar25 estimated the origins of language, or at least a protolanguage, somewhere around 800,000–500,000 years ago. Genetic evidence remains equivocal. Despite an unconscionable amount of bombastic promotion, the so-called “language gene” FOXP2 is not specific to language at all.26 It affects lingual and orofacial coordination generally, probably via disruption of basal ganglia function. Accordingly, the dating of this gene’s emergence and fixing in hominins doesn’t indicate when language evolved, despite many claims that it does. Fossil evidence shows that vocal-tract morphology changed in humans ~800,000 years ago, and the ear canal changed ~500,000 years ago, which might indicate an increased importance of vocal communication in that time range.25 As illustrated in Figure 17.2B, most of the increase in EQ occurred before ~800,000–500,000 years ago, but the last phase happened afterward. Accordingly, if Dunbar’s estimates for the origins of language are even remotely accurate, most (but not all) cortical expansion occurred before spoken language emerged. The demands of speech and language contributed to cortical reorganization and a degree of corticalization, and Figure 14.6 illustrates changes in fiber tracts connecting the frontal and temporal cortex. Sense of self Several transcortical networks have been described in the human brain, based mainly on correlated fMRI signals.27 One of the networks goes by many names, but the most common one is the default-mode network (red in Figure 17.1B), so-called because its areas show decreased levels of fMRI activation when people perform tasks and increased activation when people rest in a scanner. The basic idea is that the default-mode network underlies mentation whereas other networks engage in task control.28, 29 However, this terminology had planted a misleading idea in the minds of many neuroscientists. The defaultmode network shows high levels of activation for tasks that require self-generated decisions, choices, and actions,2 so it’s wrong to think of its functions in terms of being on-task or off-task. Instead, the literature supports a distinction in terms of internal versus external guidance of thoughts and actions. A medial component of the default-mode network includes parts of the prefrontal and posterior parietal cortex, the cingulate cortex, and the hippocampus.30, 31 This group of areas is sometimes called the medial network for reasons both obvious and not so obvious. The not-so-obvious reason is that the hippocampus, which seems to be a laterally situated, temporal-lobe structure in primates, is both ontogenetically and phylogenetically the medial allocortex, as explained elsewhere.5 In this “elsewhere” publication, my coauthors and I make a big deal of representations and re-representations of oneself and other people, and its role in human evolution. The following proposal explains why:

Proposal 8. New forms of self-representation evolved in hominins. The medial network, which includes the medial prefrontal cortex, cingulate cortex, and hippocampus, contains much of the transcortical circuitry that underlies autobiographical memory and memories of events (episodic memory).5 The rostral parts of the medial network are in a position to abstract and generalize representations in more caudal areas, a concept that Lau and his colleagues call rerepresentation.32, 33 According to this idea, the rostral prefrontal areas successively re-represent one’s own actions, along with goals (commonly called intentions). A sense of self and agency emerges from these successive re-representations. Each hominin species evolved its own version of self-representation and the representation of others. These re-representations established the foundation for several functions: a theory of mind; common knowledge, including shared intentions; knowledge of social rules and goals; and metacognition. Because of its role in episodic and autobiographical memory, the medial network is also the foundation of several additional functions: a sense of participation in ongoing and remembered events, mental time travel, and a limitless ability to imagine events, which is called constructive episodic 5 simulation. The areas performing these functions include prefrontal areas that emerged in anthropoids (Figure 11.4), expanded in hominoids during the Miocene (Figure 8.2B), and expanded again in hominins during Pleistocene (Figure 14.4). The phylogenetically oldest part of the medial network, the hippocampus, also enlarged in Pleistocene hominins (Figure 14.7).

Social neuroscience is a burgeoning and active field, so I won’t try to review it here. Instead, I cite just a few of the results relevant to the medial network, with emphasis on the medial prefrontal cortex, anterior cingulate cortex, and hippocampus. For the present purposes, the main idea is that human-specific representations of oneself didn’t arise de novo in Homo species. Every animal that represents aspects of its own actions has an element of self-representation. In hominins, new and higher levels of re-representation led to a type of species-specific self-representation that characterizes and is unique to human cognition. For example, the anterior cingulate cortex is known to represent valuations that underlie economic choices, and these functions include social value.34 Neurophysiological studies in macaques have found that the discharge rates of its neurons encode social preferences and the vicarious reinforcement of conspecifics.35 In accord with single-neuron activity in this area, inactivation of the anterior cingulate cortex blocks the ability of macaques to make choices that benefit conspecifics.36 Comparative neuroanatomy shows that the anterior cingulate cortex is homologous across primates and other mammals (Figure 11.4A and C). Accordingly, as complex anthropoid societies evolved from the solitary systems of early primates (Figure 15.3), the cingulate cortex had been in place for tens of millions of years. In early primates, therefore, the cingulate cortex must have played a role in nonsocial behavior, such as foraging. Later, as complex social systems evolved in anthropoids and other primates, it became co-opted for social behavior.37 Something similar seems to have happened in rodents,37 but social evolution in primates occurred independently. Similarities between rodents and primates therefore reflect some combination of a common Euarchontoglires condition and convergent evolution. Functional MRI studies reveal an important role for the medial prefrontal cortex and the anterior cingulate cortex in social cognition. Unfortunately, terms like medial prefrontal cortex and anterior cingulate cortex have several different and conflicting meanings in the neuroscience literature. Here I include medial parts of area 9 (PFdm) and area 10 (PFp) in the medial prefrontal cortex, and I include the prelimbic (area 32) and infralimbic (area 25) cortex in the anterior cingulate cortex. To cite just two meta-analyses, fMRI signals increase when people judge other people or themselves, with two activation peaks: one in the pregenual anterior cingulate cortex (area 32), the other in the medial PFp (area 10).38 A strengthened correlation of fMRI signals occurs when people process social information, including in PFdm (area 9) and parts of the anterior cingulate cortex (subgenual area 25 and pregenual area 32).39 In addition, the degree of fMRI activation correlates with task performance when people engage in tests of autobiographical and social memory.39 A significant literature points to a role for PFp in long-range planning and prospection, which involves imagining oneself and one’s activities in both the near-term40 and far-distant future, sometimes decades away. Proposal 8 mentioned limitless constructive episodic simulations. The medial prefrontal cortex and the hippocampus contribute differently to this cognitive capacity. Lesions of the hippocampus degrade the details that populate imagined events but leave intact the ability to include oneself in such mental simulations. In contrast, lesions of the medial prefrontal cortex leave intact the imagination of fully formed and detailed events, but block the inclusion of oneself.41 It might seem counterintuitive, if not dangerous, for evolution to have constructed transcortical networks for real and imagined events that are intertwined. For example, the medial network establishes a sense of participation in ongoing events, as well as remembered and imagined ones, which creates a problem: the need to distinguish real events from imagined ones. This function is called reality monitoring, and its breakdown probably contributes to several mental health disorders, such as those characterized by hallucinations and intrusive thoughts.42 Imagined and real events depend on overlapping cortical structures because they rely on a similar cognitive process. For example, both need to demarcate the boundaries between events and structured sequences of events (their beginning and end, for example), and both involve occurrences within complex scenes.43,44,45 When people fail to recognize the fact that sensory memories, internal voices, or thoughts arise from their own brain, severe psychiatric disorders can result. There’s no direct way to know when, during hominin evolution, a modern sense of self and others developed. However, the Pleistocene expansion of the granular prefrontal cortex and other aspects of the medial network, such as the medial posterior parietal cortex and the hippocampus, probably reflect the increased importance of re-representations of oneself and others in the societies of archaic humans. And because of fossil evidence, we know roughly when that happened (Chapters 8 and 14). Dreamers of the day Those who dream by night in the dusty recesses of their minds wake up in the day to find it was vanity, but the dreamers of the day are dangerous . . . , for they may act their dreams with open eyes, to make it possible. —T.E. Lawrence, Seven Pillars of Wisdom, 1926

A consideration of self and society leads back to the social-brain hypothesis. So far, I‘ve considered this idea for primates as a whole (Chapter 15, “Grub versus groups”), for anthropoids (Chapter 16, “Groups, grub, and gray matter”), and for carnivores (Chapter 10, “Cats go their own way”). There wasn’t strong support for the social-brain hypothesis in any of those groups. However, a version of the social-brain hypothesis—one that focuses on hominin societies—might fare better. One approach, which I call the prevailing view, deals with social hierarchies at four levels of group size: tribes of ~1,500 people; mega-bands of ~500; communities of ~150; and bands of ~15–50.46,47,48 The community level is the size of what’s called Dunbar’s number, the postulated size of early human social groups. On this view, various assortments of bands are (or

were) drawn from a relatively fixed community of ~150 individuals, depending on the task at hand.47 According to the social-brain hypothesis, human cognition and human-specific emotions evolved to manage social cooperation and other relationships within Dunbar-number-size communities, and these social functions account for the cortical enlargement that occurred during the Pleistocene.49, 50 The prevailing view assumes that large-scale social systems emerged only within the past 10,000 years or so, which means that they couldn’t have played a role in selecting for a larger cortex or for the expansion of specific cortical regions. The human cortex probably reached its modern size and shape by ~130,000 years ago, and it certainly did so by 95,000 years ago (Chapter 8). According to most current thinking, it was only much later, after the Neolithic revolution (~10,000 years ago), that larger social systems developed. Then, people began a transition from a foraging culture of hunting and gathering to a life of farming and herding, which produced surpluses of grains, legumes, and animal products that supported permanent settlements of villages and cities. Accordingly, it’s assumed that social interactions were limited to small groups throughout the Pleistocene, corresponding to emergence and use of sophisticated Paleolithic and Mesolithic technologies (Figure 17.2). Ideas about cognitive evolution have therefore focused on the social demands of small groups, especially the social cooperativity required for successful hunts of large animals, the acquisition of knowledge about the plant products to seek, instructions conveyed from experienced toolmakers to novices, and the management of group politics. Ethnography and ensembles Recently, both cultural anthropologists and archaeologists have challenged the assumptions underlying the prevailing view. I begin with the anthropologists. Bird et al.51 based their critique on ethnographic studies. They rejected the premise that Pleistocene hunter-gatherers were limited to small-scale societies of ~150 individuals. The problem, in their opinion, is that proponents of the prevailing view fail to appreciate evidence showing that modern hunter-gatherer groups interact with a much larger network of people than usually assumed, distributed over much large territories and across a larger variety of timeframes. According to Bird et al. (p. 105), ethnographic studies show that: all human societies (including mobile foragers) are large and complex; all are tied together well beyond the local residential group, and well beyond a discrete community of ~150 members. Somewhat at odds with expectations . . . , foraging group size is shaped by dynamic legacies of land/resource use, where the social commitments to foraging and sharing shape the landscapes that feed-back on group size, and the composition of even the smallest group links members directly to many hundreds, even thousands of social interactors.

Bird et al. (p. 106) go on to say that the kinds of communities posited by the prevailing view: have no clear analogue in contemporary mobile hunter-gatherers. This general disconnect between traditional views of hunter-gatherer social organization and quantitative ethnographic evidence highlights an important weakness in current paleoanthropological/neurological models of the co-evolutionary relationships between human cognition, pro-sociality, and hunter-gatherer group size and organization.

By “paleoanthropological/neurological models,” I assume that Bird et al. are referring to the Pleistocene grade-shift in cortical volume and the social-brain account for it. Ancestral hunter-gatherers might have had different social systems than modern ones have, but the work of Bird et al. points to the possibility that cortical expansion could have occurred in societies larger than the prevailing view assumes. Obviously, if large-scale societies didn’t develop until the Holocene, which began 11,700 years ago, they couldn’t have driven cortical expansion during the Pleistocene. However, if Pleistocene people interacted within much larger populations than usually assumed, then the cognitive demands of such societies could have played a role in selecting for enlargement of the typically layered areas (and therefore of the cortex and the brain as a whole). And if this is the case, such societies were surely composed of individuals that had more distant genetic relationships with each other than small-scale bands and communities have. Perhaps cultural connections among populations numbering in the hundreds or thousands—and the associated cognitive demands—mattered as much as or more than relationships within Dunbar-number-size groups. According to Bird et al., the key to understanding the success of Pleistocene humans lies in the social dynamics of largescale groups and their ability to modify landscapes to improve foraging efficiency and success, long before the advent of settled farming and herding. As they put it (p. 106): “We hypothesize these large societies and anthropogenic landscapes are inherent features of the Pleistocene spread of modern humans.” Archaeology and assemblies In The Dawn of Everything, Graeber and Wengrow1 reach much the same conclusions based on archaeological evidence. They conclude that many of the complexities attributed to Neolithic village and urban life arose much earlier than the prevailing view assumes. In their opinion, the emergence of towns and cities, charismatic leadership, experts on esoteric knowledge, and technical exchanges among specialized artisans merely concentrated in a single location the kinds of complex social interactions that had long been a part of hunter-gatherer cultures, and they cite archaeological evidence indicating that such large-scale cultural areas arose much earlier than usually thought. Like the ethnographic analysis of Bird et al., the archaeological studies summarized by Graeber and Wengrow indicate that foraging cultures need not be simple, small-scale ones, and assumptions to the contrary have led theorists to underestimate the size and complexity of Paleolithic and Mesolithic societies. So, what is this archaeological evidence? Graeber and Wengrow1 marshal a broad range of studies showing that human foragers developed towns and large ceremonial spaces long before settled agriculture and herding emerged. Artifacts indicate that these groups included specialists in various crafts and esoteric knowledge, all of which predated the Neolithic age. In their view (p. 281): “Foragers may sometimes exist in small groups, but they do not—and probably have not ever—lived in small-scale societies.” Humans tend to live simultaneously on two scales of social complexity: “the 150-odd people they know personally, and inside imaginary structures” that include a much larger number of individuals.

Social imagination reimagined The key word in the last quotation is “imaginary,” which provides a clue about the social factors that might have influenced cortical expansion during the Pleistocene. The ability to function within geographically dispersed, large-scale societies requires a human-specific cognitive capacity: the limitless imagination of future events, also known as constructive episodic simulation (Proposal 8). As Lawrence of Arabia says in this section’s epigraph, day dreamers can change societies because they are able to imagine living in a way that differs from anything they have experienced. Only humans can use constructive episodic simulation to decide how to transform societies,52 and it seems likely that Pleistocene humans had the same capacity. As Boehm52 (p. 185) put it, this: requires a large brain, for future effects of present actions must be calculated in complicated ways . . . People intuitively understand the social systems in which they are embedded, and they create and uphold moral rules because they can predict the long-term effects of the absence of such rules—that is, what would take place if they were not enforced. This imaginativeness involves . . . an intuitive understanding of human nature—of the types of impulses and drives that are likely to cause trouble. Proscriptions against bullying and serious deception are universal precisely because of the conflict that they bring . . . . At the same time, people try to manipulate their social systems in prosocial directions: they regularly preach in favor of cooperation, generosity, and altruism, and they reward such behavior [italics original].

Put another way, only humans can imagine a society that’s beyond their experience—or anyone else’s for that matter. Instead of small groups that simply continue inherited traditions, human societies result from self-conscious arguments about the appropriate structure of society, and they involve large groups of individuals with a shared culture: much more extensive and much more dependent on constructive imagination than the prevailing view recognizes. The view of cultural evolution envisioned by Graeber and Wengrow suggests that large-group cultures arose in Paleolithic people. These cultures involved not only a sense of appropriate actions and attitudes, but also of moral values: a mental model of what kind of people a group is and what’s important to them. Cultural knowledge of this kind requires an assessment of the relative value of competing principles, along with imagining that moral values could change and, if so, what the consequences of such a social transformation might be. This kind of cognitive activity amounts to self-conscious political imagination, which leads to a final proposal: Proposal 9. Self-conscious political simulation—in the context of large-scale, geographically dispersed societies—drove cortical expansion during the Pleistocene. Uniquely human cognitive capacities empowered Paleolithic people to imagine themselves as part of a geographically extensive, transgenerational culture —one distinguished from the cultures of other people. This formulation of the social-brain hypothesis might account for a significant amount of the upward grade-shift in cortex that occurred during the most recent two million years of human evolution, which depended on an expansion of both the typically layered areas of neocortex and the hippocampus. These evolutionary developments supported both semantic (cultural) memory and constructive episodic simulation: the ability to imagine oneself as belonging to a large, culturally distinct and geographically extensive society.

Chapter summary Many selective factors favored the enormous cortex that humans have, and they had their greatest influence at different times and on different cortical areas. Most of these areas consist of typically layered cortex (often called homotypical association areas). In addition, the hippocampus expanded during hominin evolution, reversing a contraction trend that prevailed throughout most of anthropoid evolution. Five drivers of cortical enlargement seem to have been especially important: (1) a specialization for generalization across cognitive domains; (2) the ability to represent abstract relations, including those involved in relational, analogical, and metaphorical reasoning; (3) the evolution of spoken language, including the emergence of cultural (semantic) memory; (4) the manufacture of complex toolkits, including the representation of affordances required for such technological knowledge; and (5) the emergence of novel re-representation of oneself and others, which underlies human social systems that depended on cooperativity, shared resources, and the exchange of knowledge. All these evolutionary developments, not just one or two of them, contributed to the modern human cortex. The social-brain hypothesis has received the most attention, but a recent reconsideration of early human societies suggests that the prevailing version of this idea is based, in part, on faulty assumptions. Instead of small-scale groups of 150 people or less, interactions among large, geographically extensive social groups—numbering hundreds or thousands of individuals— could have favored the development of cognitive capacities beyond the ones useful in small groups. The demands of largescale societies required two human-specific cognitive capacities central to Proposal 8: (1) a capacity for limitless constructive episodic simulation, which is linked to new representations in the prefrontal cortex and the hippocampus; and (2) cultural (semantic) knowledge, which is associated with new representations in the anterior temporal lobe. Proposal 9 advances the idea that, as a result of these developments, self-conscious political imagination guided the construction of Paleocene societies, and selection for the neural representations underlying these functions contributed to the Pleistocene grade-shift in cortical size and complexity. References 1. 2. 3.

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18 Corticalization and composition Overview In modern primates, the cerebral cortex dwarfs the remainder of the brain: a dominance that developed in phases, mostly in Eocene crown primates, Miocene anthropoids, and Pleistocene hominins. In each case, a larger cortex evolved at a different time than the group’s characteristic skeletal traits, which points to different driving forces for bodies and brains. Notably, cortical expansion in Miocene hominoids occurred while they still closely resembled cercopithecoids, their sister group. The cortex also changed in composition. An extensive suite of primate- and anthropoid-specific areas emerged, many of which improved the efficiency of long-range foraging journeys. In Miocene monkeys and hominoids, new and faster forms of locomotion evolved in concert with a larger cortex. Later, in Pleistocene hominins, the enlargement of primate-specific cortical areas, along with the hippocampus, supported uniquely human cognitive capacities, including a prodigious accumulation of semantic generalizations and a limitless capacity to imagine the future and the past. Tyrannosaurus Rez Yes, I’ve survived All of the genocidal shit that killed So many in my tribe, And it is absurd That I’ve made a great career Out of nouns and verbs, But, look, It’s a miracle when any writer Sells even one damn book. So listen to me: I was conceived With twenty thousand years Of my ancestors’ stories Locked in my gray matter And flooding my marrow. So don’t think I’m flattered With your homily About how I must be Some kind of anomaly. I am my mother’s son. I am my father’s child. And they left me with a trust fund Of words, words, and words That exist in me Like dinosaurs live in birds.

—Sherman Alexie, You Don’t Have to Say You Love Me: A Memoir, Little, Brown & Company, 2017

Introduction In this chapter and in the Epilogue that follows, a primate pontificates; global cooling causes global warming; and I speak for the dead. But I begin by explaining why I chose this chapter’s epigraph. Like the poem, this book is partly about “words, words, and words”: specialized terms that foster an understanding of cortical evolution. It’s also about “gray matter” that “locked in” aspects of our ancestors’ lives. “What primates were” shaped “what primates are,” and long-ago lives left a legacy in the cortex of every primate, including ours. At every stage in the future advance of neuroscience, it will help to understand the lives of primates in which new kinds of cortical representations emerged and proliferated. Cortical chauvinism The authors of a recent paper1 on human brain evolution, which is laudable in many respects, framed their review in terms of cortical chauvinism. They (p. 1) aimed to “challenge the systemic mischaracterization of human cognition and behavior as a competition that pits phylogenetically recent cortical territories against evolutionarily ancient subcortical and cerebellar systems.”* They also rejected what they called the “flawed theoretical framework in which the evolution of association cortex is viewed as an isolated process, removed from the rest of the brain.” For readers unfamiliar with The Evolution of Memory Systems,2 this book might seem to adopt this “flawed . . . framework.” After all, I have deliberately focused on the cerebral cortex to the exclusion of other parts of the brain. However, the fact that the cortex acts in concert with the thalamus, basal ganglia, amygdala, and cerebellum, among other subcortical structures,* is not in doubt. The evolutionary developments traced in this book all have their counterparts in the subcortex. But there’s one statistic about the human brain that I consider to be a crucial clue about what’s important: ~80% of it consists of cerebral cortex. That’s not a “flawed theoretical framework,” that’s a fact. As for pitting “phylogenetically recent cortical territories against evolutionarily ancient subcortical and cerebellar systems,” I again refer readers to The Evolution of Memory Systems, which emphasizes the relationship between cortical evolution and concurrent developments in the basal ganglia and thalamus. For example, the granular prefrontal cortex projects to its territories in the striatum3 and thalamus,4 which are primate specializations in the same way that the granular prefrontal areas are.

Parts and primates This book has five parts: what primates are; what primates were; what primate cortex was; what primate cortex is; and why the cortex changed. What primates are To understand cortical evolution in primates, we need to know what primates are. For the present purposes, primates are mosaics of generalized mammalian features and specializations, including old cortical areas and new ones. What primates were Primates were once much like other eutherian mammals. Then primates began specializing, probably during the Paleocene. The principal primate specializations involve vision, grasping, and a combination of the two: visually guided grasping. These features originated as primates adapted to a life confined to trees in dense, dim tropical rainforests. Then, climate change began to stress their forest ecosystems. The effects of global cooling led to many changes in what primates were. What primate cortex was The cortex of primates was small, at first, much like that of other Euarchontoglires. Then, after the Eocene, primate species with a rodent-size cortex died out. Anthropoid cortex was, early on, much like that of other primates. Then, after the Miocene, anthropoids with a prosimian-size cortex died out. What primate cortex is The emergence and enlargement of specific cortical areas made the cortex what it is today. Different areas contributed to each of the major grade-shifts: • • •

During the Eocene, parietal–premotor networks, the frontal eye field, part of the orbitofrontal cortex, and visual areas led the way. During the Miocene, anthropoid-specific prefrontal areas drove frontal-lobe enlargement. During the Pleistocene, typically layered areas of the frontal, parietal, and temporal lobes—what’s often called homotypical association cortex—ballooned.

Why the cortex changed There’s no single factor that selected for the kinds of cortex that primates have today. Evolutionary changes occurred at different times in different primates for reasons specific to the ecology of each lineage in its time and place. That being said, early adaptations to foraging in the fine branches of dim, rainforest habitats played a major role in shaping primate brains, as did later adaptations to changes in primate ecosystems, often due to global cooling. Questions and conclusions Chapter 1 posed five questions about cortical expansion in primates, which this chapter addresses in three groups: • • •

When did it occur, and in which primates did it happen? Which areas contributed to expansion, and what ecological factors selected for them? How did cortical evolution in primates compare with what happened in other mammals?

Figure 18.1 illustrates some answers to the first two questions.

Figure 18.1 Cortical evolution in primates. (A) Global climate change. The dark line presents smoothed data from the light blue lines; green arrows mark the onsets of six global-cooling trends; and gray shading emphasizes the global cooling trends that began ~40 Ma and ~14 Ma. (B) The color of each line corresponds to a grade of cerebral cortex size as indicated in the key (lower left). Late Eocene grade-shifts brought Euprimate cortex into the modern prosimian range (yellow); middle-to-late Miocene gradeshifts brought cercopithecoid and platyrrhine brains into the modern anthropoid range (green); and in hominoids, an early Eocene grade-shift brought their cortex into the modern anthropoid range and then into the modern hominoid range (blue). After the panin–hominin split, hominin brains expanded into the modern human size range (dark blue). Each line represents several descendant lineages, many of which underwent independent upward and downward grade-shifts (not illustrated). Abbreviations:Δ, change in a morphological trait; FEF, frontal eye field; IT, inferior temporal cortex; M1, primary motor cortex; M1c, caudal primary motor cortex; Ma, million years ago; OB, olfactory bulb; PFo, granular orbital prefrontal cortex; PM, premotor cortex; PP, posterior parietal cortex; ST, superior temporal cortex.

When and in which Part III addressed the first pair of questions. Conclusions pertaining to Eocene grade-shifts are labeled with a 1; Miocene grade-shifts with a 2; and the Pleistocene grade-shift with a 3.

Eocene (1a) Euprimate brains increased in relative size and the percentage of neocortex during the Eocene, probably after ~40 Ma. This initial set of upward grade-shifts occurred long after the development of forward-facing eyes, grasping hands and feet, flat nails instead of pointy claws, and hindlimb-dominated locomotion, which were all in place no later than ~55 Ma and probably earlier. (1b) Prior to this first pulse of cortical expansion, primate brains remained in the Euarchontoglires range of relative size and cortical extent. In other words, early primates had a brain and a cortex of the size expected for rodents and many other mammals. (1c) The late-Eocene grade-shifts occurred independently in lorisiforms, lemuriforms, tarsiers, and anthropoids; and, most likely, independently in platyrrhines and catarrhines among anthropoids. (1d) The anthropoids in which the Eocene grade-shifts occurred were small, locally foraging animals, much like their contemporaries among the strepsirrhines and tarsiers. Miocene (2a) Cercopithecoid and platyrrhine cortex expanded into the modern anthropoid range after ~16–15 Ma, during the middleto-late Miocene, long after other anthropoid adaptations had evolved, such as larger bodies, a bony postorbital septum, and quadrupedal locomotion, not to mention the earlier emergence of the fovea in ancestral haplorhines. (2b) Prior to these pulses of cortical expansion, platyrrhine and cercopithecoid brains remained in the prosimian range of relative size and cortical extent. Put another way, early anthropoids had a brain and cortex of the size expected for lemurs and many other strepsirrhines. (2c) In hominoids, the upward grade-shift in cortical extent occurred earlier than in cercopithecoids and platyrrhines: before ~18 Ma and before most of the characteristic skeletal traits of hominoids evolved. Hominoids became large (severalkilogram) animals ~20 Ma and radiated ~17–15 Ma, eventually becoming yet larger (tens or hundreds of kilograms). Accordingly, ancestral hominoids evolved a relatively large cortex, even by primate standards, before they diversified and spread from Africa to Asia, while they still resembled cercopithecoids in size and most other skeletal traits. It was in early, agile, monkey-like hominoids that the cortex reached the modern anthropoid and hominoid grades. (2d) Upward grade-shifts in corticalization occurred independently in the three main anthropoid lineages: platyrrhines, cercopithecoids, and hominoids. (2e) Also independently, the frontal lobe expanded in all three lineages as the relative size of the olfactory bulbs decreased. Pleistocene (3a) Cortical expansion accelerated during hominin evolution, mainly during the Pleistocene, as Paleolithic tool-making traditions developed and sexual dimorphism decreased. The latter trait suggests the emergence of new social systems. (3b) In humans, cortical expansion accompanied a bulging of the frontal squama (forehead) and the parietal plate, along with a rostral and lateral expansion of the anterior temporal lobe. The first humans (Homo species) emerged ~3 Ma, and the earliest Homo sapiens fossils date to ~315,000 years ago, but the human brain reached its modern size and shape only ~130,000 years ago. Summary Among the direct ancestors of humans, the initial phase of cortical enlargement occurred in Eocene anthropoids (Chapter 7), and another followed in Miocene hominoids (Chapter 8). Both times, specific cortical areas expanded much more than others. Thus, along with ancestral hominins and early Homo species, the lives of earlier primate ancestors also contributed to the evolution of the massive human cortex, a legacy that’s at least 40 million years old. Independently, upward grade-shifts also occurred in the cortex of platyrrhines and cercopithecoids, including frontal-lobe expansion: developments that had no influence on the evolution of human brains. Which and why Parts IV and V addressed the second pair of questions: Which areas contributed to cortical expansion, and what ecological factors selected for them? Again, the conclusions that pertain to Eocene grade-shifts are labeled with a 1; Miocene gradeshifts with a 2; and the Pleistocene grade-shift with a 3.

Eocene (1a) Eocene Euprimates exploited the understory and terminal branches of dense tropical and subtropical forests, which extended to very high latitudes in the Northern Hemisphere. The forest canopy blocked most light, even in daytime, and nocturnal foraging occurred in especially dark conditions. As forests contracted during global cooling episodes, competition intensified. Primate species competed with other primate species during the Eocene, something that became less common afterward. Advantages went to individuals with improved cortical mechanisms for visually guided movements. New posterior parietal areas represented the location of biologically valuable items in visual coordinate frames appropriate for each major effector (eye, hand, foot, and mouth), and new premotor areas guided those effectors accurately to goals. Expansion of posterior parietal and premotor areas probably drove most of the late-Eocene gradeshifts. (1b) A new or vastly enlarged hindlimb representation in M1 contributed to pedal grasping, including the hindlimbdominated, leaping–grasping locomotion characteristic of primates. Leaping provided an effective means of escape from predators, among other advantages. (1c) New prefrontal areas, including granular parts of the orbital prefrontal cortex (PFo) and the frontal eye field (FEF), exploited the retinal specializations of Eocene primates by improving vision-based foraging: in the terminal branches of angiosperm trees and in dim illumination. PFo, via interactions with the amygdala, updated the valuations of visible objects based on their association with hidden or poorly seen food items. Its updating function reflected a predicted food’s current desirability based on its remembered visual appearance, incorporating the moment-to-moment biological needs of an individual. The FEF functioned in the search for and tracking of desirable items and visual patterns associated with them, and it did so via connections with both the dorsal and ventral visual streams. These transcortical networks implemented top-down attention directed toward valuable items against a background of low-value items, which was especially advantageous in the clutter of the terminal-branch niche. (1d) In Eocene anthropoids, the FEF acquired a new function: overt attention, which depends on the fovea. Anthropoids inherited the fovea from ancestral haplorhines, and during anthropoid evolution the FEF developed enhanced projections to the superior colliculus. Via these corticotectal outputs, overt attention orients the fovea toward foraging goals and other items of biological value. Miocene (2a) Anthropoids evolved by the early Eocene, but most remained small animals until after the global cooling of ~34 Ma, which initiated the Oligocene. New typically layered areas—more commonly known as homotypical association cortex —evolved in the temporal, parietal, and frontal lobes, including new granular prefrontal areas. A new component of the somatosensory cortex, area 2, also emerged, along with the development of new features within a caudal subdivision of the primary motor cortex (M1c). (2b) New prefrontal areas represented behavioral goals and the means to achieve them, and they reduced the number of dangerous foraging journeys by speeding learning and applying abstract behavioral rules and strategies to foraging choices.* Long-distance foraging journeys entailed a danger of predation, so any decrease in the number of unproductive excursions provided a significant survival advantage. Expansion of the granular prefrontal cortex led to enlargement of the frontal lobe in the three main anthropoid lineages—cercopithecoids, hominoids, and platyrrhines—independently. (2c) In both platyrrhine and cercopithecoid monkeys, the prefrontal cortex expanded as they coped with the contraction of dense forests during a middle-to-late Miocene global cooling trend (~14–9 Ma), a time when both groups independently developed a new, faster (cursorial) mode of quadrupedal locomotion. This new way of moving, combined with visionbased foraging, enabled monkeys to exploit open woodlands and savannas, especially in the Old World. (2d) Typically layered areas in the inferior temporal cortex represented the visual patterns of distant stands of trees and other features of landscapes. Typically layered areas in the superior temporal cortex represented the sounds of feeding animals, and similar representations contributed to social cognition. (2e) Posterior parietal areas functioned in association with premotor areas to represent reaching, grasping, feeding, and eye movements in a visual frame of reference based on the fovea. These areas processed and stored conjunctive representations that compose visual affordances: the available actions based on what an individual can see. The posterior parietal cortex also represented relational metrics, such as relative number, order, distances, durations, and speeds, which improved foraging efficiency via transcortical networks involving granular prefrontal areas. (2f) M1c in motor cortex acquired cutaneous inputs in anthropoids, especially from Meissner’s corpuscles, which contributed to the control of fine finger movements that manipulated foods, including fruits, and provided tactile information about their ripeness. Meisner’s corpuscles—mechanoreceptors that transduce skin stretching and deformation—are especially sensitive in primates. (2g) In hominoids, the prefrontal cortex enlarged during an early Miocene period of warm and stable temperatures, a time when a more upright (antipronograde) form of locomotion supported speedier, long-distance foraging. The cortex enlarged at a time when hominoids encountered new large-brain, large-body predators. After diversifying, radiating, and migrating into Asia ~17–15 Ma, a middle-to-late Miocene cooling trend (~14–9 Ma) led to the extinction of most hominoid species, as deciduous trees and open woodlands replaced fruit-bearing trees in their habitats.

Pleistocene (3a) In hominins, the enlargement of typically layered areas, which had evolved in earlier primates, drove the cortex into the modern human size range, mostly during the Pleistocene. The expanded regions included the medial and inferior parietal cortex; the anterior, superior, and inferior temporal lobe; the anterior insular cortex; and the granular prefrontal cortex, including the lateral part of the polar prefrontal cortex. The latter contributes to a uniquely human form of cognition: planning over a limitless timeframe, often years or decades in the future. Prefrontal expansion exceeded that of the parietal and temporal cortex, albeit modestly. (3b) As typically layered areas expanded in hominids and hominins, they contributed to several transcortical networks. Among them, the default-mode network includes the medial prefrontal cortex, cingulate cortex, and hippocampus. The medial prefrontal cortex generates species-specific re-representations of oneself and others, which then become available to the entire network. Conjunctive representations of oneself and events establish such uniquely human cognitive functions as autobiographical memory and constructive episodic simulation: the limitless imagination of past and future events, featuring oneself and one’s social group as participants. (3c) In most anthropoid lineages, the hippocampus contracted in relative terms, but during human evolution that trend reversed strongly. Something made the hippocampus more important to humans than to our anthropoid ancestors, including hominid ones. Proposal 8 (p. 326) posits that the expansion of the hippocampus reflected the importance of transcortical networks that generated, processed, and stored representations of oneself and others (Chapter 17). Combined with limitless prospection (point 3a) and constructive episodic simulation (point 3b), evolving hominins could evaluate a potential course of action far into the future. The same neural processes and representations enabled hominins to re-experience previous events (mental time-travel) and imagine how an alternative (counterfactual) course of action might have produced a different outcome.* (3d) Connections between the temporal and frontal lobes changed during human evolution, as the arcuate fascicle extended to connect more of the temporal lobe with the frontal lobe. A related reorganization of the posterior temporal cortex enhanced the integration of auditory and visual information, which supported spoken language as well as both teaching and learning about tool manufacture. (3e) The temporal cortex transitioned from representing middle-level conjunctions that identify visual categories to representing the generalizations, concepts, and categories that compose semantic memory. (3f) The posterior parietal cortex transitioned from representing relative metrics (e.g., more, farther, longer, faster, first) to representing relations more generally, which underlies analogical and metaphorical reasoning. (3g) Human societies have long been regarded as a major driving force for cortical expansion, but the size and complexity of Pleistocene social systems may have been seriously underestimated. According to the prevailing version of the socialbrain hypothesis, the cognitive demands of cooperation among members of nomadic hunter–gatherer groups—for foraging, resource management, and predator defense—were important influences on the Pleistocene grade-shift. The assumption underlying this idea is that, as pre-agricultural people, Pleistocene humans interacted only within small bands or communities: 150 people or fewer. However, the group size and territorial scope of Pleistocene societies may have been much larger than generally believed. Archeological evidence indicates that towns, ceremonial spaces, specialized artisans, and experts in a wide range of esoteric knowledge predated the Neolithic revolution and therefore preceded the development of agriculture and cities. According to this view: (1) Cultural areas comprised several hundred social interactors who lived simultaneously in both small groups and larger ones, which had extensive geographical and cross-generational scope; (2) Pleistocene people imagined themselves as part of these large-scale Paleolithic cultures; (3) this form of self-conscious political imagination and cultural identity relied on uniquely human capacities for rerepresentation of oneself, constructive episodic simulation (point 3b), and semantic generalization (point 3e); (4) size increases in the prefrontal cortex and hippocampus reflect an increased importance of self-representation and constructive imagination, while expansion of the anterior temporal lobe reflects an enhancement of semantic knowledge; and (5) these functions depended on new, species-specific representations in the prefrontal cortex, hippocampus, and temporal lobe. Summary The first take-home message is this: To understand cortical evolution in primates, it’s essential to consider innovative cortical representations, which emerged at various times in the distant past. It’s also important to understand behavioral specializations in terms of such representations, in addition to their more abstract counterparts, such as social complexity, foraging strategies, predation threats, longevity, tool-related affordances, etc. Some of these factors were more important than others, but I suspect that they all played some role in what the cortex has become in primates, including humans. However, they did so because specialized representations improved fitness, not because of some generic advantage of a larger cortex. Figure 18.2 presents a simplified version of Figure 18.1. Both illustrations indicate the primates in which the cortex expanded, roughly when these grade-shifts occurred, and some of the cortical areas that emerged during primate evolution.

Figure 18.2 Cortical evolution in primates: simplified. In the format of Figure 18.1. Abbreviations: FEF, frontal eye field; IT, inferior temporal cortex; M1, primary motor cortex; M1c, caudal primary motor cortex; Ma, million years ago; OB, olfactory bulb; PFo, granular orbital prefrontal cortex; PM, premotor cortex; PP, posterior parietal cortex; P-P, Plio-Pleistocene; ST, superior temporal cortex.

A multitude of mammals Chapter 10 addressed the third question: How did cortical evolution in primates compare with what happened in other mammals? Toothed whales (Odontoceti), a clade that includes oceanic dolphins, most closely resembled primates in the degree and timing of cortical expansion, but the cortex also enlarged in several other mammalian orders.5 In ungulates and carnivores, these evolutionary changes occurred more recently than in cetaceans and primates. Nevertheless, the dietary and environmental-complexity correlates of a large cortex point to similar selective factors in primates and carnivores.6 In both orders, several lineages evolved a large cortex independently (Figures 8.11 and 10.4). Two quotations from Chapter 2, both from Cachel,7 capture the second take-home message of this book: “homoplasy is rampant in the animal world” and “convergent evolution is ubiquitous.” Instead of intelligence: representations Inevitably, discussions of evolution leave some people wanting more. For instance, the late Pope Benedict XVI lamented the lack of purpose in evolution: “We are not some casual and meaningless product of evolution,” he pontificated. An ongoing race for survival without any ends, not to mention a lack of divine intervention, doesn’t satisfy the former Primate of Italy. Likewise, some readers might want something that’s missing from this book: a single account for the massive human cortex and for the evolution of language and the other impressive cognitive capacities of our species: a theory of mind; mental time-travel; limitless mental simulation of past and future events; analogical, metaphorical, and relational reasoning; and prospection decades into the future. Many influences have contributed to the human cortex and its cognitive capacities, not just one or two. Unfortunately for enthusiasts of parsimony, cortical evolution in primates is more complex than any single account can capture. But there’s gain in this pain: an opportunity to tackle age-old questions about the human brain in new ways. More specifically, the lives and circumstances of ancestral primates provide clues about the functions of our cortical areas and their representations: ancestors that lived at various times, tens of millions of years apart; in diverse lineages; on different continents; and in myriad ecological circumstances. It was those ancient lives that established a foundation for the brainy descendants that primates eventually became. Some cortical areas and their neural representations became more important and expanded during the late Eocene (Chapters 12 and 15), and a different set enlarged during the Miocene (Chapters 13 and 16). Hominins inherited both groups of areas, which served as exaptations for human-specific cognitive capacities (Chapters 14 and 17). Many readers will have noticed that words like intelligence and consciousness have made only rare appearances in this book. The neuroscience literature is full of suggestions that a large cortex underlies intelligence, advanced cognition, and higher brain functions. In a sense, such ideas are undoubtedly true—and that’s part of the problem. These ill-defined terms can cover almost anything. Whatever such weighty words mean, they’re insufficiently precise to tell us much about the adaptive advantages that specific cortical representations provided to our ancestors. In rejecting explanations of the large

human cortex solely in terms of intelligence, I don’t deny the existence of general intelligence or the idea that a new form of generalized reasoning emerged during human evolution. These concepts are worthy of consideration when attached to specific ancestors, at a given time, in a particular place, and in ecological conditions that existed then and there. My coauthors and I have made several proposals along such lines, as summarized in Chapter 17. Our book, The Evolution of Memory Systems,2 presents these ideas in detail, and some of them are repeated here. Regardless of the fate of our specific proposals, the idea that the lives of our primate ancestors left a legacy in our cortex is beyond doubt. Whatever words like intelligence and consciousness mean, we owe these capacities to a long line of primate ancestors and the innovative representations that emerged in their cerebral cortex. Chapter summary New and enlarged cortical areas were not the only important developments during primate evolution. Cortical plesiomorphies (old areas) developed new features (such as “blobs” in V1, “stripes” in V2, and cutaneous specializations in caudal M1) and many changes occurred in subcortical structures. But the most dramatic development was the emergence and subsequent expansion of new, typically layered neocortical areas, more commonly known as homotypical association cortex. Some of these areas are primate specializations, common to both strepsirrhines and anthropoids; others are anthropoid specializations. These areas, and especially the granular prefrontal cortex, expanded preferentially during the Miocene, when the cortex first entered the modern anthropoid range; and they—along with the hippocampus—enlarged again during the Pleistocene, when humans developed Paleolithic cultures that depended on three crucial evolutionary innovations: species-specific representations of self and society; representations that empowered a temporally limitless ability to imagine alternative futures and counterfactual pasts; and representations underlying the generalizations, categories, and concepts that compose our cultural knowledge. References 1. 2. 3. 4. 5. 6. 7.

Chin, R., Chang, S.W.C., & Holmes, A.J. Beyond cortex: the evolution of the human brain. Psychological Reviews 130(2), 285–307 (2022). Murray, E.A., Wise, S.P., & Graham, K.S. The Evolution of Memory Systems: Ancestors, Anatomy, and Adaptations (Oxford University Press, Oxford, 2017). Balsters, J.H., Zerbi, V., Sallet, J., Wenderoth, N., & Mars, R.B. Primate homologs of mouse cortico-striatal circuits. Elife 9, e53680 (2020). Baldwin, M.L.K. & Wise, S.P. Evolution of frontal cortex and thalamus in primates. In: The Cerebral Cortex and Thalamus (ed. W.M. Usrey & S.M. Sherman) 596–607 (Oxford University Press, New York, 2024). Bertrand, O.C., Shelley, S.L., Williamson, T.E., Wible, J.R., Chester, S.G.B., Flynn, J.J., Holbrook, L.T., Lyson, T.R., Meng, J., Miller, I.M., Püschel, H.P., Smith, T., Spaulding, M., Tseng, Z.J., & Brusatte, S.L. Brawn before brains in placental mammals after the end-Cretaceous extinction. Science 376, 80–5 (2022). Lynch, L.M. & Allen, K.L. Relative brain volume of carnivorans has evolved in correlation with environmental and dietary variables differentially among clades. Brain, Behavior and Evolution 97, 284–97 (2022). Cachel, S. Fossil Primates (Cambridge University Press, Cambridge, 2015).

* The authors reject opinions related to MacLean’s triune brain theory, which Chapter 11 (“Lamentation of the lizard”) also debunks. * The amygdala is mostly but not entirely subcortical. Its cortical “nuclei” are allocortical structures, which are contiguous with piriform, hippocampal, and allocortical transition areas. * Note that this does not imply that behavioral rules or problem-solving strategies are unique to anthropoids or that anthropoids always make good choices. It means only that the granular prefrontal cortex improves such functions and thus reduces the frequency of poor choices. * A Canadian television series, Being Erica, combined two of the themes of point 3c: time travel and re-imagining autobiographical events. The protagonist was a 30something underachiever. A “therapist” with magic powers found her at a low point in her life and promised that he could send her back in time to correct the life choices that she’d been regretting ever since. Her determination to avoid repeating her past mistakes invariably made matters worse, but it helped her make better decisions in the present.

Epilogue Primates and petroleum In The Prize, Daniel Yergin traces the history of fossil fuels, which have generated both prosperity and perils. But petroleum is not the only prize buried in strata deposited by bygone ecosystems. Fossil primates are down there, too, and they reveal a great deal about cortical evolution. By combining comparative and functional neuroanatomy with the fossil record, an ironic conclusion becomes clear: The past 40 million years of intermittent global cooling caused global warming. On the surface, that statement seems ridiculous. How could global cooling cause global warming? The answer involves a biological intermediary: large, typically layered cortical areas with powerful conjunctive representations. People with that prize began polluting the air with combustion byproducts ~300 years ago (about the same time that Linnaeus recognized that people are primates). That’s how global cooling caused global warming. This book has emphasized global climate change as a major, if indirect, selective force favoring cortical enlargement in several primate lineages. For obvious reasons, global warming is a hot topic these days, but this prospect wouldn’t have bothered primates of the late Eocene, early Oligocene, or middle-to-late Miocene. I hesitate to speak for the dead, especially dead that couldn’t speak, but I think that many of these ancestors would have welcomed global warming. They lived during cooling trends, and regardless of the absolute temperature of their times, the relative chill threatened their survival. Primates and prizes The first primates weren’t affected by global cooling, and it’s with them that this ending begins. As the Dodo says in the book’s epigraph: “EVERYBODY has won, and all must have prizes.” Modern primates have a prize cerebral cortex because their ancestors lived a tree-top life in a hot-house world: the ecosystem in which primates originated and upon which they depended for millions of years. At latitudes known for brutal cold and treeless tundra today, luxurious rainforests thrived along with primates (not to mention alligators). For a while, primates prospered, too. Then, cooling trends began ~50 Ma and ~40 Ma, during the Eocene. Global cooling decreased atmospheric moisture and caused the contraction and drying of forests. Primate species competed with many other primate species then, as well as with arboreal rodents and birds. In several primate lineages, evolving independently, a larger cerebral cortex resulted from these struggles. Then, ~34 Ma, global cooling got much worse, quickly. Many species died out, but some primates squeezed through that evolutionary bottleneck. Later, three lineages—platyrrhines, catarrhines, and hominoids—gave rise to large-bodied anthropoids: in the several-kilogram range. The cortex enlarged again, but that happened much later: after ~16–15 Ma in platyrrhines and cercopithecoids, and only a little earlier in hominoids (~26–18 Ma), while they still resembled cercopithecoids in most respects. Changes in the cortex produced platyrrhines, cercopithecoids, and hominoids empowered to survive predators while they foraged in daylight over large distances, often for fruit. Novel representations in enlarged prefrontal and temporal areas improved their ability to make good foraging choices at a distance, which mitigated predation threats by reducing the frequency of unproductive journeys. They also evolved complex social groups, partly as a defense against predation, sometimes with hierarchies that regulated intragroup competition for resources and mating opportunities, and sometimes via egalitarian social systems that depended more on cooperation than on competition. Despite their impressive cerebral cortex, many hominoids fared poorly after their initial radiation ~17–15 Ma. By ~9 Ma, during the late Miocene, most had succumbed to an aridification of forests and the replacement of their favored fruit trees with deciduous species: another consequence of global cooling. Almost everyone knows the rest of the story. One branch of the hominid family tree, hominins, fared much better. As our hominin ancestors adapted to life in a cooler world of savannas and open woodlands, the cortex enlarged yet again, and during the Pleistocene it attained its current size and shape. In the frontal, temporal, and parietal lobes, the typically layered cortical areas expanded the most, which is how they became typical. Because of countless ancient lives—from late-Eocene primates onward—selection favored the evolution of a massive mental machine, one composed of cortical representations that originated in our ancestors and exist in us “Like dinosaurs live in birds.”

Crucial glossary

Adaptive radiation An evolutionary process that produces relatively rapid diversification and speciation within a clade Analogy A feature that evolved independently in two or more species and performs the same function Anthropoid Monkeys, humans, and apes (formally Anthropoidea or Simii) Catarrhine Old World monkeys, apes, and humans (formally Catarrhini) Cenozoic See Figure 4.1 Cercopithecoid Old World monkeys, including cercopithecines and colobines (formally Cercopithecoidea) Clade A group of species that descended from a single founding species, and only those species Colugo A member of the order Dermoptera, also known as flying lemurs Convergent evolution The evolution of similar traits from different starting conditions; sometimes a synonym for independent evolution Cretaceous See Figure 4.1 Crown group All living members of a clade, plus fossils nested in that group Cytoarchitectonics Studies of the pattern and distribution of neuronal cell bodies, as observed in Nissl-stained sections Diagnostic trait A trait that identifies either a taxon or a homologous brain structure Diverge, divergence The division of a progenitor species into two descendant species Eocene See Figure 4.1, table in reference figure Euarchontan Tree shrews, colugos, and primates Euarchontoglires Rodents, lagomorphs (rabbits, hares, and pikas), tree shrews, colugos, and primates Euprimate A clade that includes all modern primates but excludes a group of stem primates called plesiadapiforms Evolutionary tree A diagram showing the evolutionary relationship among a group of species, equivalent to phylogenetic tree Evolutionary trend A consistent direction of phylogenetic change in a trait over time Exaptation An evolutionary development that enables different evolutionary innovations in the future Glires Lagomorphs (rabbits, hares, and pikas) and rodents Grade A level of size or complexity observed in an organism or organ Grade-shift A change in grade during evolution Haplorhine Anthropoids and tarsiers (formally Haplorhini) Hominid Great apes and humans (formally Hominidae) Hominin Nonpanin descendants of the chimpanzee–human ancestral population (formally Hominini) Hominoid Apes (gibbons, orangutans, gorillas, chimpanzees, and bonobos) and humans (formally Hominoidea) Homology A trait inherited from a common ancestor in two or more species Homoplasy A similar trait that evolved independently in two or more species Independent evolution Convergent or parallel evolution, produces homoplasies Last common ancestor The most recent species ancestral to two or more species, also called the most recent common ancestor LCA An abbreviation for last common ancestor Ma An abbreviation for million years ago Mesozoic See Figure 4.1 Miocene See Figure 4.1, table in reference figure

Monophyletic group A clade: the entire group of species that descended from a founding species and only those species Monotreme Egg-laying mammals: platypuses and echidnas Myeloarchitectonics Studies of the pattern and distribution of stained myelinated fibers Oligocene See Figure 4.1, table in reference figure Paleocene See Figure 4.1, table in reference figure Parallel evolution The evolution of similar traits independently, beginning from a similar condition Paraphyletic group A group of species that includes their last common ancestor and some, but not all, of its descendants Phylogenetic reversal An evolutionary change that results in something resembling an ancestral condition; also known as a phyletic reversal Phylogenetic tree A diagram showing the evolutionary relationship among a group of species, equivalent to evolutionary tree Platyrrhine New World monkeys, indigenous to South America (formally Platyrrhini) Pleistocene See Figure 4.1, table in reference figure Plesiadapiform A group of extinct primates near the primate stem Plesiomorphy Shared primitive trait; a trait that characterizes a clade, but which has been inherited from a species ancestral to that clade Pliocene See Figure 4.1, table in reference figure Prosimian Tarsiers and strepsirrhines Sister group A group of species most closely related to another group of species Speciation An evolutionary process that yields new species Split A synonym for divergence Stem group A group of species near the origin of a clade Strepsirrhine Most prosimians, including lemurs, galagos, and lorises (formally Strepsirrhini) Synapomorphy A shared derived trait; an innovative trait that characterizes a clade and that has been inherited from a common ancestor Taxon, taxa A group (taxon) or groups (taxa) of organisms, the basis for taxonomy; also known as biological systematics

Extended glossary

Adapiform A group of Euprimates that lived from the Eocene through the Miocene; either stem Euprimates or stem strepsirrhines Adaptive advantage A trait that promotes fitness and thereby contributes to the survival of a species Affordance Visual aspects of an object or surface indicating what actions are available Allocortex Three-layer cortex Amniote Birds, reptiles, and mammals. Angiosperm Flowering plants Artiodactyl Even-toed ungulates Australopithecine A group of hominins closely related to humans Basal A species near the stem (origin) of a clade Carnivora Informally called carnivores, a group of mammals that includes the dog and cat families, along with pinnipeds (seals, sea lions, etc.) Cercopithecine A clade of Old World, cercopithecoid monkeys, sister group of the colobines Chronogram A phylogenetic tree plotted on a timescale Cladistics The classification of organisms in terms of clades Cladogram A diagram of relationships among a group of species Coordinate frame Spatial system with an origin and axes that specifies locations Colobine A clade of cercopithecoid monkeys, sister group of the cercopithecines Competitive exclusion When one group of species drives another group out of a niche by outcompeting them for resources or safety Corticalization 1. An increase in the percentage of the brain occupied by neocortex; 2. the percentage of brain occupied by neocortex Cynodont A group of therapsids that includes an ancestor of mammals Diagnostic trait A heritable property (trait, character, or feature) of an organism that enables its identification as a member of a particular taxon Diastema A gap between teeth EQ Encephalization quotient Ecosystem An integrated system of biological organisms and their physical habitat Encephalization 1. An increase in brain size relative to body mass; 2. A measure of brain size relative to body mass Encephalization quotient The ratio of observed brain size to the size predicted for its body mass Endocast An impression, either physical or virtual, of the inner table of the skull; reveals size, shape, and sulcal patterns Eulipotyphla A clade of placental mammals including hedgehogs and some shrews and moles (but not all shrews or moles) Eutherian Placental mammal Extrinsic coordinate Spatial locations relative to the outside world Fitness The likelihood of an organism to transmit its genes to future generations Folivory A preference for eating leaves Forest A habitat with more than 80% cover by the canopy of trees Fovea A specialized part of the retina that has a high density of color receptors and contributes to high-acuity central vision Frugivory A preference for eating fruit

Genetic drift Changes in the genome that do not reflect specific selective pressures Gnathostome A group of vertebrates with jaws, paired appendages, and a cerebellum; includes jawed fish, amphibians, reptiles, mammals, and birds Grassland A habitat with few, if any, trees Gumivory A preference for consuming the gums, saps, and resins exuded or extracted from trees Habit In biology, a genetically programmed trait; in psychology, stimulus–response associations or unattended actions Habitat The natural environment of an organism Hallux The first and, in humans, the largest digit on the foot; also known as the big toe Herbivory A preference for eating vegetation Home range Territory traversed regularly by an individual over an extended period of time Homeothermy Regulation of internal body temperature with in a set range; warm blooded; also known as endothermy IQ Intelligence quotient Insectivory A preference for preying on insects, also known as faunivory Intelligence quotient A score on a test assumed (controversially) to reflect general intelligence Lagomorph A group of rabbit-like animals that includes hares and pikas Lemuriform A group of strepsirrhines related to lemurs Life history The properties of a species or group of species, including reproductive rate, lifespan, maturation patterns and timing, and parental investment Lorisiform A group of strepsirrhines related to lorises, including galagos (also known as bushbabies) Marsupial Also known as metatherians, the sister group of placental mammals within the clade of therian mammals Metacarpal Bones linking the fingers to the wrist Metatarsal Bones linking the toes to the ankle Metatherian Marsupials Model species A species used in biomedical research Molecular phylogeny Reconstructions of phylogeny via analysis of changes in the genome or other molecular traits Multituberculate An extinct group of nontherian mammals Murine rodent A mouse-like clade of rodents that includes laboratory rats and mice Mutatis mutandis Regarding a prior statement, making changes as necessary to make a subsequent statement true Neocortex Cortex with several layers that develops inside-out, which means that the neurons born earlier migrate to the deeper layers Neoteleost A clade of ray-finned fishes that emerged at roughly the same time as birds Niche A set of environmental conditions Niche partitioning Exploitation of different components of an ecosystem by different species Olfactory epithelium A layer of cells containing olfactory receptors Omnivory The lack of a strong preference for a specific kind of food; generalized feeding habits Ontogeny Individual development, embryogenesis; contrasts with phylogeny, which refers to evolutionary development Omomyiform A group of Euprimates that lived from the Eocene through the Miocene; either stem Euprimates or stem haplorhines, related to tarsiers Outgroup A clade used for comparison with the clade under consideration Paleoecology 1. Ecosystems of the past; 2. the study of past ecosystems Panin Nonhominin descendants of the chimpanzee–human ancestral population, includes chimpanzees and bonobos Pedal Pertaining to the foot Phalange A bone in a finger or toe Phenological Seasonal or other temporal cycles, such as circadian ones Phylogenetic statistics Statistical analysis that takes evolutionary relationships into account

Phylogeny 1. Evolutionary development; 2. evolution Phylogram A depiction of evolutionary relationships that depicts changes in a trait or set of traits Poikilothermy Regulation of body temperature by behavioral means; cold blooded; also known as ectothermy Pollex The first digit of the forelimb; the thumb or its homolog Polymorphic trichromacy A form of three-color vision that depends on variation in one of two opsin genes; a sexlinked trait Postcranial Any part of the body other than the head Prefrontal cortex The frontal neocortex rostral to motor and premotor areas Procumbent incisors Front teeth that project forward from the mouth Routine trichromacy A form of three-color vision that depends on three opsin genes; contrasts with polymorphic trichromacy Savanna A mixed grassland–woodland habitat; contrasts with forest Scala naturae The ranking of taxa based on similarity with humans; also known as the scale of nature, phyletic scale, and phylogenetic scale Scandentia Tree shrews Selective pressure An aspect of an ecosystem that favors the genetic transmission of specific traits to progeny; also known as a driving force Speciation The origin of species, usually by the splitting of two populations of one species into two Speciose A lineage that diversified into many species Stand A cluster of trees Synapsid A group of amniotes that includes an ancestor of therapsids, cynodonts, and mammals Systematics The classification of organisms according to an established hierarchy and naming system (nomenclature). Taphonomy 1. The study of fossil formation; 2. the process of fossil formation Tarsal Pertaining to the ankle Taxonomy A system of names and classification; also known as systematics Therapsid A group of synapsids that includes an ancestor of cynodonts and mammals Therian Eutherian mammals and marsupials Trichromacy Three-color vision; also known as full-color vision Turbinal bones A thin bone covered with tissue that contributes to regulating nasal airflow; important for olfaction Ungulate A paraphyletic group of mammals with hooves; includes perissodactyls and artiodactyls, the latter being cetartiodactyls Woodland A habit with 40–80% canopy coverage by the arbor of trees

Index

For the benefit of digital users, indexed terms that span two pages (e.g., 52–53) may, on occasion, appear on only one of those pages. Tables, figures, and boxes are indicated by t, f, and b following the page number A1 cortex 187f, 195, 196f, 197, 212f, 218f, 219f, 232f, 234f absolute brain size 51–52, 52f accessory olfactory bulbs 73 Acheulean tools 137f, 322–23, 324 action maps 217, 219, 228, 240–41, 241f, 242, 290 acuity, visual 72, 122, 207, 222, 237b, 237, 239f see also fovea; retina adapiforms 65f, 85, 117, 134f, 173f Adapis 64, 65f, 117f, 117, 118f, 118–19, 121f, 133, 134f, 144f Darwinius 65f, 69 Notharctus 65f, 117f, 117–18, 133, 134f, 144f Adapis 64, 65f, 117f, 117, 118f, 118–19, 121f, 133, 134f, 144f adaptation to dim-light conditions 83–84, 208, 222 adaptive radiation 17, 18f, 79b, 86, 96–97, 97f, 129, 149, 151, 156, 159, 172–73 Aegyptopithecus 18f, 92f, 95f, 127f, 128–29, 131f, 132, 134f, 144f, 304f, 304–5, 307 aerobic metabolism 161–63 affordance 10, 205, 219, 283, 332, 346 Africa 75f, 90–91, 97f, 128–29, 156 agency 319–20, 326 AGm see medial agranular cortex agranular cortex 188–89, 219f, 234 agranular prefrontal cortex 191, 233, 235f anterior cingulate cortex 198, 199, 254, 326, 327 infralimbic cortex 189f, 198 prelimbic cortex 189f, 198, 215, 254 AIP see posterior parietal cortex alligators 83–84, 350 allocortex 111, 190f, 196f, 197–99, 282–83, 286f, 326 allometry 12, 143f, 143, 171f, 252, 322 Alouatta see howler monkeys amalgams 179, 187–88, 189f, 191, 192–93, 199 amniotes 27–28, 162f, 165b, 197 Amphicyonidae, amphicyonids see bear dogs amygdala 194, 214, 338, 343 analogy 16, 20–26, 73, 181 ancestral species 17–18, 22, 24, 137, 180, 255, 268b, 293f ancestral anthropoids 180, 254–55, 300–3, 309, 319–20, 322 ancestral haplorhines 207–8, 295, 341, 343 ancestral hominoids 149, 341–42 ancestral mammals 64, 164, 166–67, 187–88, 269–70 angiosperms 66, 79b, 80, 94 angiosperm trees 58, 79b, 79, 83, 288–90, 295, 343 ankles 70, 81b Antarctica 88, 166 anteaters 35f, 37f, 166–67 anterior cingulate cortex 198, 199, 254, 326–27 anterior insular cortex 199, 260f, 261–62, 271, 344–45 anterior intraparietal area (AIP) 241f, 242, 247 anterior temporal lobe 140, 142f, 144–45, 246, 264–65, 266, 322, 332–33, 342, 346 antipronograde locomotion 230, 304f, 307, 339 apes bonobos 42f, 42–43, 138f, 138, 140f, 252, 257f, 258f chimpanzees 42f, 97f, 114, 136f, 137f, 138f, 140f, 142f, 239f, 256f, 257f, 258f, 260f, 265f, 267–68, 269f, 304f, 340f, 347f gibbons 41f, 42f, 52f, 95f, 143f, 173f, 239f, 257f, 258f, 267 gorillas 95f, 136f, 137f, 138f, 140f, 239f, 252, 256f, 258f, 264, 265f, 268, 269f, 270–71 orangutans 25b, 42f, 96, 239f, 252, 257f, 258f, 267–68 Arabia 90–91, 229, 310–11 arboreality 38f arboreal quadrupedal locomotion 78, 94f, 95, 96, 102, 154, 230, 307, 308 arboreal rodents 63, 67, 68–69, 81–83, 84–85, 86, 101, 291, 350–51 Archicebus 84–85 architectonic analyses 253–54, 259 Arctic 59f, 84, 86 arcuate fascicle 251, 264, 265f, 322, 345 arcuate sulcus 131f, 132, 189f Ardipithecus 137f, 138f area 1 283 area 2 238, 239f, 240, 347f area 5 240, 243–44 area 9 322, 327 area 10 233, 254, 271, 327 area 11 233, 237–38 area 13 212–13, 237–38 area 24 189f, 198 area 25 198, 327 area 32 198, 254, 327 area 46 233, 240b area 47 233 ARHGAP see genetic mechanisms

artiodactyls 21–22, 37f, 84–85, 118f, 119, 167, 168f, 169–70, 172–73 Asia 26–27, 54–55, 80, 83, 90–91, 306, 309, 344 association cortex see typically layered cortical areas atmospheric moisture x, 100, 291, 350–51 attention 185–86, 205, 215–16, 223, 228, 237, 241–42, 281, 295, 343 audition 63–64, 164, 244, 301b, 313, 319 auditory areas, auditory cortex xi, 64, 187f, 193, 196f, 218f, 219f, 228, 232f, 234f, 243, 260f, 287, 308, 313 Australia 36, 75f, 166 australopithecines 31, 42f, 42–43, 89f, 99, 137, 140f Australopithecus 89f, 136f, 137f, 137, 138f, 139, 144f, 269f, 304f, 323–24 Australopithecus afarensis 89f, 124, 136f, 137, 138f, 269f, 324 autobiographical memory 318t, 326, 345 avian see birds aye-ayes 39, 41f, 91b baboons 41f, 41, 42f, 73, 88, 95f, 97f, 98, 131f, 239f, 256f Baldwin, Mary xi, 10f, 38f, 153f, 184f, 196f, 212f, 215, 216, 217–18, 218f, 234f, 236, 321f baleen whales 112, 167, 168f, 286–87 basal ganglia 194, 325, 338 basal primates 109–10, 217 bats ix, 33, 34f, 35f, 120f, 126f, 168f, 181 bumblebee bats 25b echolocation 27–28, 193, 286–87 bear dogs 171f, 301b bears ix, 37f, 166–67, 170–71, 301b Bergmann’s rule 56 binocular visual field 72, 207, 222 bipedal locomotion 158, 304f birds 53–54, 67, 162f, 163b brains of 164–65 body size 25b, 26b, 26, 49, 52f, 55–56, 93f, 94f, 110f, 127f, 143f, 143, 157f, 171f, 231, 303 bonobos (Pan paniscus) 42f, 42–43, 138f, 138, 140f, 252, 257f, 258f bony septum see postorbital septum Boyer, Doug 70 brain size 51, 52f, 110f, 110–11, 119, 120f, 127f, 134f, 143f, 143, 157f, 171f, 293f brain volume 116, 117f, 127f, 136f, 143f, 157f Brodmann, Korbinian 189f, 245–46, 255, 281–82 Brodmann numbers 240b, 240 Brusatte, Steve 66, 69, 84–85, 111–12 C4 photosynthetic pathway 99–100 Cachel, Susan 23, 66–67, 88, 94, 95, 99, 128–29, 230, 347 canids 170–71, 301b canine teeth 138f, 158, 207b, 303, 304–5 canopy, rainforest 58, 79–80, 83–84, 206–7, 208, 222, 342–43 capuchin monkeys 20f, 40, 41f, 95f, 131f, 132, 256f see also cebus monkeys capybaras 81b carnivores amphicyonids, bear dogs 171f, 301b canids, dog family 170–71, 301b cheetahs 69 creodonts 207, 309–10 felids, cat family 167, 171f, 301b hyenas 171f, 300, 301b lions 166–67, 301b mongooses 171f, 171–72, 301b mustelids 170–72 procyonids 171–72 raccoons 210b saber-tooth tigers 166–67, 301b tigers 68–69, 166–67, 301b ursids (bears) 37f, 166–67, 170–71, 301b wolves 166–67 Carpolestes 65f, 67, 70 Castorocauda 164 catarrhines (Old World anthropoids) 31, 38f, 38, 40–41, 41f, 42f, 42–43, 52f, 65f, 68f, 73, 102, 121f, 126f, 127f, 128–30, 131f, 132, 133, 134f, 143f, 144f, 153f, 182f, 232f, 235–36, 239f, 241f, 256f, 258f, 270–71, 293f, 304f, 340f, 347f categorization 246, 319–20, 322 cathemeral foraging 163 CBLN see genetic mechanisms ceboid monkeys see cebus monkeys Cebuella see pygmy marmosets cebus monkeys 132, 215, 238, 239f, 254, 265–66, 266f Cedromus see rodents cell types 7–8, 150, 151, 164–65, 283 Cenozoic 50, 54, 58, 59f, 153f, 158 Central America 26, 91b central sulcus 131f, 132, 189f central vision 72–73, 207, 209–10, 222, 223 fovea 207, 222, 295 cercopithecines 40–41, 52f, 143f, 258f, 307 cercopithecoids 38f, 40–41, 41f, 52f, 65f, 89f, 95f, 95, 98, 102, 127f, 129, 131f, 153f, 156, 235–36, 239f, 256f, 257f, 258f, 293f, 303f, 304f, 307, 308–9, 340f, 347f Cercopithecus ascanius see red-tail monkey cerebellar cortex 261 cerebello-thalamocortical projections 261 cerebellum 7–8, 196f, 338 cetaceans archaic ungulates 54–55 baleen whales 112, 167, 168f, 286–87 dolphins 37f, 118f, 160, 161, 168f, 169 toothed whales, Odontoceti 167, 168f, 286–87 cetartiodactyls see artiodactyls; cetaceans cheetahs 69 Chilecebus 65f, 126–28 chimpanzees 42f, 97f, 114, 136f, 137f, 138f, 140f, 142f, 239f, 256f, 257f, 258f, 260f, 265f, 267–68, 269f, 304f, 340f, 347f

Chiroptera see bats chronogram 19, 20f, 52f, 90f, 95f, 97f, 118f, 168f, 184f, 241f cingulate cortex 189f, 326, 327, 345 circadian patterns 11, 92–93 Cisek, Paul 284b cladistics 16–30, 32 clades 17–19, 33–39, 81b, 97f, 113–15, 143f, 143 cladists 35–36, 40 claws 67, 68–69, 85 climate change 88, 94f, 97f, 99, 289–91, 306–8, 340f early Eocene climatic optimum (EECO) 58, 59f Eocene–Oligocene climatic transition (EOCT) 59f, 59, 60, 88, 91, 92f, 96, 101, 102, 153f, 154, 229, 302, 304f, 306 global cooling 59f, 60, 94f, 94, 101, 130–33, 136f, 153f, 229–31, 304f, 309, 318t, 323f global warming 84, 309–10, 350 Ice Ages 60, 99, 136f, 138f, 155–56, 318t, 323f middle Eocene climatic optimum (MECO) 59f, 59, 85 middle Miocene climatic optimum (MMCO) 59f, 59, 96–97, 98, 306–7, 309 middle Miocene climatic transition (MMCT) 59f, 59–60, 304f, 306, 307 Paleocene–Eocene thermal maximum (PETM) 58, 59f, 84, 85 cochlea 64, 286–87 cognitive domains 319–20, 322, 332 colobines 41, 52f, 143f, 229–30, 258f color vision 163b, 208 cones 208–163 opsins 208 colugos 33–34, 34f, 35f, 37f, 38f, 41f, 52f, 66–67, 68–69, 90f, 182f, 184f common marmosets 20f, 131f competitive exclusion 81–83, 101 cones 208–163 conjunctive representations 245–46, 283, 285, 286f, 294, 305–6, 308, 311–12, 313, 344, 345, 350 consciousness 348 constructive episodic simulation 317, 318t, 326, 327, 331, 332–33, 345, 346 continental drift 60 convergent evolution 23, 89f, 161, 167b, 181 see also homoplasy cooling trend see climate change coordinate frame 72, 205, 209–10, 228, 241–42, 247, 290, 342–43 coronal suture 139, 140f corpus callosum 189f, 259, 260f, 261 cortical expansion 136f, 144f, 151, 152, 155–56, 269f, 303, 304f, 323f, 330, 342, 346 corticalization 111, 118f, 118–19, 121f, 144f, 153f, 156, 173f, 303f, 304f cortical maps 181, 187f, 212f, 232f, 233, 234f, 291 cortical network 192, 270 cortical volume 12, 322, 330 corticospinal projections 216–17, 238 corticostriatal projections 261 corticotectal projections see superior colliculus corvids see crows Coryphodon 169–70 covert attention 295 creodonts 207b, 309–10 crepuscular foraging 163, 208, 230–31 cross-domain cognition 318t crown group 17, 18f, 43, 66 crows 181 crypsis 301b cultural knowledge 318, 319–20, 324–32 cursorial quadrupedal locomotion 90f, 102, 230, 307, 309, 344 cutaneous receptors 73, 238 Meissner’s corpuscles 73, 239f, 240, 247, 344 cynodonts 161–62, 162f, 164, 165–66 cytoarchitectonic analysis 259 cytoarchitectonic types of neocortex 234 cytoarchitectonics 188–89, 259, 261–62 Darwinius 65f, 69 Daubentonia see aye-ayes deciduous trees 151, 344 deer 166–67, 169 default-mode network 325 deforestation x, 58, 78, 101, 154, 247 Delphinoidea see cetaceans; dolphins dendritic spines 150, 151, 270–71 dense forests 68, 79–80, 222, 291 density of neurons 72, 221–22, 267f, 267 dentary–squamosal joint 164 dentate gyrus 287 dentition 67, 68f, 80, 94f, 309–10 canine teeth 138f, 158, 207b, 303, 304–5 incisors 68f, 68, 80, 82 premolars 67, 68, 80, 207b depth perception viii, 72 Dermoptera see colugos desirability valuations 213–14, 285, 296, 343 dewclaws 301b diagnostic trait 19, 21–23, 188, 191, 294 diagonal sequence–diagonally coupled gait (DS–DC) 71, 74, 81b diapsids 162f, 164–65 diastema 68, 80, 82 dichromatic vision 208, 222 diet frugivory 38f, 229–30, 239f folivory 41, 86, 130, 293–94 omnivory 305, 307 digestion 58, 164 dim-light vision 83–84, 88, 151, 207–8, 209 dinosaurs 53, 54–55

diurnal foraging 78, 96, 244, 295, 300, 313–14 divergence times 51, 52f, 97f, 121f, 138f, 138, 289 divergent hallux 58, 109, 209 dodos x, 4–5 dogs 170–71, 301b dolphins 37f, 118f, 160, 161, 168f, 169 dorsal visual stream 240, 245, 287, 290, 311–12 dorsolateral prefrontal cortex 189f, 233, 311–12, 312f dorsomedial prefrontal cortex 189f, 220, 233, 270, 271, 312f DS–DC gait 71, 74, 81b dual-origins theory 194–95, 197, 198 duckbilled platypus xi, 166–67 Dunbar, Robin 170, 301b, 302, 325, 328, 330 duplication hubs 268 dysgranular cortex 188–89, 232f, 234 early Eocene climatic optimum (EECO) 58, 59f early Euprimates 33, 66, 68–70, 71, 72–73, 74, 85, 86, 101, 207 Archicebus 84–85 Teilhardina 84–85 early mammals 161–62, 163, 166–67, 172, 174, 196f Castorocauda 164 Hadrocodium 166 early primates see plesiadapiforms; early Euprimates ears 78, 102, 164 echolocation bats 27–28, 193, 286–87 dolphins 169, 286–87 ecological generalists 307 ecological intelligence 291–93, 305–283 ecosystems 53, 79, 80, 303 Ekembo 95f, 96 elephants 35f, 37f, 239f elephant shrews 33, 34f, 35f, 35–36, 37f embodied (enacted) cognition 284b encephalization 110f, 110–11, 117f, 120f, 121f, 137f, 143, 144f, 153f, 162f, 168f, 170f, 304f encephalization quotient (EQ) 110–11, 117f, 126f, 137f, 138f, 162f, 168f, 170f, 173f, 232f, 269f, 291–92, 323f end-Cretaceous extinction 53, 54–55, 56–57, 79b, 79, 80, 163, 169 endocasts 115f, 117f, 119, 121f, 132, 139–40, 141f, 157f, 173f, 174, 235–36, 263f Eocene anthropoids 88–89, 92–93, 222, 228–29, 295, 343 Eocene–Oligocene climatic transition (EOCT) 59f, 59, 60, 88, 91, 92f, 96, 101, 102, 153f, 154, 229, 302, 304f, 306 Eocene primates 81–82, 151, 154, 206, 207b, 207, 213, 214–15, 217, 219, 221, 292–93, 295, 297, 343 episodic memory 181, 317, 319, 326 EQ (encephalization quotient) 110–11, 117f, 126f, 137f, 138f, 162f, 168f, 170f, 173f, 232f, 269f, 291–92, 323f Euarchonta/Euarchontans colugos 33–34, 34f, 35f, 37f, 38f, 41f, 52f, 66–67, 68–69, 90f, 182f, 184f tree shrews 33–34, 34f, 35f, 36, 37f, 37–38, 41f, 52f, 72, 90f, 126f, 134f, 182f, 183, 184f, 184–85, 212f, 215–19, 218f, 219f, 220–21, 222, 232f, 235f Euarchontoglires see lagomorphs; rabbits; rodents eulaminate cortex see typically layered cortical areas Eulipotyphla see hedgehogs; moles Euprimates 38f, 52f, 65f, 65–66, 68, 71, 72–73, 83–87, 91, 115f, 117f, 117, 118f, 118–19, 121f, 134f, 173f, 212f, 342–43 Eurasia 75f, 80, 83, 88, 96–97 eutherians 37f, 162f, 166, 168f, 169 evolutionary bottleneck 229, 231 evolutionary trend 20f, 26b, 89–90, 161–62, 195, 247, 287 exaptation 24, 319–20, 322, 348 extinct primates See adapiforms Chilecebus Ekembo Hispanopithecus Homunculus omomyiforms Oreopithecus Parapithecus plesiadapiforms Proconsul Rooneyia Victoriapithecus extractive foraging 294, 305, 313–14 extrastriate cortex 37–38, 72, 220–21, 287, 288–89, 290 eyes, forward-facing 19, 38f, 65f, 71–73, 75–74, 89–90, 121f, 163b fall-back foods 154–55 farming 329, 330 Fayum depression 91 feet 21–22, 68, 209–10 felids 167, 171f, 171, 301b femur 95f, 95, 230, 307 figs 150, 238 fine-branch niche 58, 68, 74 finger movements 238, 247, 344 fingernails 38f, 68–69, 71, 74, 154, 157–58, 174 fingerprints 73 flowers 94, 221, 229–30, 246–47, 289–90 flying lemurs see colugos fMRI see neuroimaging folivory 41, 86, 130, 293–94 foraging cathemeral 163 crepuscular 163, 208, 230–31 diurnal 78, 96, 244, 295, 300, 313–14 foraging choices 135, 214, 228, 238, 243–44, 295–96, 300, 308, 310, 311–12, 313, 321, 350–51 nocturnal 164, 207–8, 342–43 forests see canopy; deforestation; dense forest; rainforest; tropical forest forward-facing eyes 19, 38f, 65f, 71–73, 75–74, 89–90, 121f, 163b

fossil primates see extinct primates fovea 38f, 65f, 73, 121f, 163b, 237, 238, 242, 295, 340f, 344 central vision 207, 222, 295 overt attention 295, 343 frontal cortex 36, 127f, 189f, 191–92, 212f, 231–33, 234f, 235f, 257f, 258f, 263f, 312f see also anterior cingulate cortex; area 9; area 10; area 11; area 13; area 24; area 25; area 32; area 46; area 47; frontal eye field; frontal lobe; motor cortex; orbitofrontal cortex; premotor cortex frontal eye field 36–37, 189f, 205, 211–12, 215–16, 218f, 220–21, 222–23, 233, 234f, 235f, 236–38, 241–42, 290, 294–95, 297, 312f, 339, 340f, 343, 347f frontal lobe 130, 131f, 132, 139–40, 140f, 190f, 231, 232f, 233, 244–45, 256f, 257f, 258f, 259–61, 262–63, 343–44, 345 frontal squama 139–40, 141f, 342 frugivory 38f, 229–30, 239f functional homology 23–24 functional neuroimaging 10f, 115f, 117f, 134f, 254, 320, 325, 327 galagos 38f, 39, 41f, 52f, 65f, 71, 92f, 143f, 182–83, 184, 212f, 217–20, 218f, 219f, 232f, 234f, 235f, 236–38, 241f geladas 42f, 97f, 239f gene duplications 251, 267–68, 270 gene expression 7–8, 164–65, 253–54, 270 general intelligence 319, 320, 348 genetic developmental programs 4 genetic drift 51, 282 genetic mechanisms ARHGAP 270 gene duplications 251, 267–68, 270 gene expression 7–8, 164–65, 253–54, 270 HAR 270 HSD 269f, 270–71 Hox 268b NOTCH 136f, 141f, 268–77 SRGAP 269f, 269–70 gibbons 41f, 42f, 52f, 95f, 143f, 173f, 239f, 257f, 258f, 267 Gigantopithecus blacki see orangutans glaciation/glacial period 60, 97f, 99, 136f, 155 see also Ice Ages global cooling 59f, 60, 94f, 94, 101, 130–33, 136f, 153f, 229–31, 304f, 309, 318t, 323f global warming 84, 309–10, 350 gnathostomes 21, 24, 36, 43 Goldman-Rakic, Patricia 36, 132–33, 233 gorillas 95f, 136f, 137f, 138f, 140f, 239f, 252, 256f, 258f, 264, 265f, 268, 269f, 270–71 Gould, Stephen Jay 27, 31, 49, 53, 61, 114 grades 17, 113–15, 124–48, 153f, 313–14, 341 see also grade-shift grade-shift 117f, 117–18, 118f, 121f, 121, 127f, 129–30, 143f, 144f, 152, 153f, 168f, 169, 170f, 171f, 263f, 300, 306, 308–9, 340f granular cortex 188–89, 191, 234 granular orbital prefrontal cortex 233, 312f, 340f, 347f grasping 38f, 65f, 68–70, 72, 86, 121f, 209–11, 210b, 218f, 239f, 241f, 290 manipulating objects 86, 228 manipulation 210b, 239f manual dexterity 6, 235, 304–5 grasslands 58, 99–100, 136–37 see also open woodlands; savanna great apes bonobos 42f, 42–43, 138f, 138, 140f, 252, 257f, 258f chimpanzees 42f, 97f, 114, 136f, 137f, 138f, 140f, 142f, 239f, 256f, 257f, 258f, 260f, 265f, 267–68, 269f, 304f, 340f, 347f gorillas 95f, 136f, 137f, 138f, 140f, 239f, 252, 256f, 258f, 264, 265f, 268, 269f, 270–71 orangutans 25b, 42f, 96, 239f, 252, 257f, 258f, 267–68 Greenland 57f, 83 guenons 41, 42f, 128–29 guinea pigs 81b gyral patterns 197 gyrification 170, 270 Hadrocodium 166 Hadropithecus 88, 89f hair 283 hallux (big toe) 70, 138f, 158, 209, 304f hands 38f, 68, 69–70, 209–10 haplorhines 38f, 38, 40–42, 41f, 52f, 65f, 85, 90f, 117f, 121f, 134f, 143f, 144f, 244, 256f, 293f, 295, 340f HAR 270 hawks 301b hedgehogs 37f, 118f hemispheric specialization 7–8 herbivores 51, 66, 80, 91, 92–93, 294, 301b high-acuity visual signals 222, 237 hindlimbs 71, 210–11, 217 hippocampus 44, 194, 196f, 265–66, 266f, 271, 286–87, 326, 327, 345, 346 hippopotamuses 167, 168f, 169 Hispanopithecus 129 Holocene 85, 89f, 98, 99, 324, 333 homeothermy 161–63, 163b, 168f hominids 41f, 127f, 129–30, 140f, 143f, 257f, 258f, 345 see also great apes Hominini-specific deletion 269f, 270–71 hominins 42f, 43, 89f, 97f, 98–100, 102, 139–40, 143f, 153f, 269f, 318t, 323f, 326 Ardipithecus 137f, 138f Australopithecus 89f, 136f, 137f, 137, 138f, 139, 144f, 269f, 304f, 323–24 Australopithecus afarensis 89f, 124, 136f, 137, 138f, 269f, 324 Kenyanthropus 323–24 Paranthropus 137f, 138f, 141f Plio-Pleistocene 136f, 136–42, 137f, 138f hominoids 38f, 41f, 42f, 52f, 65f, 89f, 95f, 96–98, 102, 115f, 127f, 129–30, 134f, 140f, 143f, 144f, 150, 153f, 159, 239f, 241f, 256f, 257f, 258f, 293f, 300, 304f, 306– 7, 308, 309, 314, 340f, 341–42, 344, 347f, 351 see also apes Homo erectus 25b, 137f, 138f, 141f, 142f, 269f, 324 Homo ergaster 324 Homo habilis 137f, 137, 138f, 269f, 270, 324 Homo heidelbergensis 136f, 137f, 138f, 141f, 142f, 269f, 324 homologous trait 26 Homo neanderthalensis 136f, 137f, 139–40, 140f, 141f, 142f, 269f homoplasy 23, 89f, 161 180 convergent evolution 23, 89f, 161, 167b, 181

independent evolution 23, 82, 180–81 parallel evolution 23, 180 Homo sapiens 131f, 137f, 138f, 139–40, 141f, 142f, 156, 269f, 323f, 341 homotypical cortex see typically layered cortical areas Homunculus 65f, 95f, 95–96, 127f, 130, 131f, 144f, 153f, 304f howler monkeys 95f, 112, 126, 131f, 293 Hox genes 268b HSD 269f, 270–71 hunter-gatherers 329 hybridization 42–43, 51, 98–99, 136–37, 138f hyenas 171f, 300, 301b Ice Ages 60, 99, 136f, 138f, 155–56, 318t, 323f see also glaciation/glacial period Ignacius see plesiadapiforms incisors 68f, 68, 80 independent evolution 23, 82, 180–81 see also homoplasy inferior longitudinal fascicle 264–65, 265f inferior precentral sulcus 139, 140f inferior temporal cortex 94f, 192, 265–66, 288f, 290–91, 313, 340f, 344, 347f infralimbic cortex 189f, 198 insectivory 56, 69, 86 intelligence 112–13, 252, 347–48 internal (inner) granular layer 188, 190 intracortical microstimulation 215, 216, 218f, 241f, 294 intragroup competition 96, 231, 313, 350–51 intraparietal cortex 241–42 Ischyromys 172, 173f see also rodents jaws 21, 24 Jurassic 35f, 164 juxtallocortex 198 Kaas, Jon xi, 186, 195, 233, 240, 243, 253 kangaroos 166–67, 174 Kenyanthropus 323–24 keystone traits 19, 22–23 koniocortex 188–89, 190f Krubitzer, Leah 185–86, 193, 238 K-type retinal ganglion cells 209, 222, 237b lagomorphs 37f, 38f, 41f, 52f, 126f, 182f see also rabbits language 24, 163b, 265f, 318, 318t, 321f, 324–25, 348 lateral geniculate nucleus 209, 237b lateral intraparietal cortex (LIP) 187f, 241f, 242, 247 lateral sulcus 116, 120–21, 189f, 220, 243 leaping 70, 71, 81b, 210–11, 307, 343 leaping–grasping 70–71, 72, 210–11, 216 predator avoidance 86, 205, 301b lemuriforms see aye-ayes; lemurs lemurs 38f, 39, 41f, 52f, 65f, 88, 89f, 134f, 143f, 256f, 284b, 340f Hadropithecus 88, 89f pygmy mouse lemur (Microcebus) 25b, 85, 115f, 116 ring-tailed lemurs 243–44 life history 55–56, 215–16, 302 lifespan 119, 120f limbic cortex 196f, 198, 282 lions 166–67, 301b LIP see posterior parietal cortex lizards 162–63, 194, 301b locomotion 55–56, 71, 92–93, 95, 219, 230, 303, 307, 341 see also leaping long-distance foraging 96, 228, 310–11, 315, 344 longevity 119, 120f lorises 38f, 41f, 52f, 65f, 92f, 143f lorisiforms see galagos; lorises lunate sulcus 131f, 189f macaques/Macaca 37f, 41f, 42f, 97f, 114, 187f, 189f, 191–92, 214, 235f, 239f, 241f, 254, 256f, 260f, 262–65, 265f MacLean, Paul 194 Madagascar 88, 90f, 91b male–male competition 304–5 mammaliaforms see mammalian evolution mammalian evolution 35f, 161, 162f, 163, 164, 195 Castorocauda 164 cynodonts 161–62, 162f, 164, 165–66 Hadrocodium 166 middle ear 24, 50, 73 Morganucodon 162f ossicles 24–25, 64, 73, 162f, 164 synapsids 161–62, 162f, 164–65 therapsids 161–62, 162f, 165–66 mammalian phylogeny 36 mammary glands 164 mangabeys see cercopithecoids manipulating objects 86, 228 manipulation 210b, 239f manual dexterity 6, 235, 304–5 manual grasping see grasping marmosets 19, 20f, 26b, 37f, 41f, 131f, 239f, 256f, 260f marsupials 35f, 81b, 126f, 162f, 165–67, 167b mass extinction 53–54, 58, 79b, 79–80 maternal investment 6, 119, 304–5, 307–8, 313–14 MECO (middle Eocene climatic optimum) 59f, 59, 85 medial agranular cortex (AGm) 219f medial intraparietal area (MIP) 187f, 241f, 241–42, 247 medial longitudinal fascicle 265f medial network 326–240, 327–28

mediodorsal nucleus of the thalamus 191 Meissner’s corpuscles 73, 239f, 240, 247, 344 mesocortex 196f, 198–99 Mesolithic 137f, 317, 329, 330 Mesozoic 50f, 54, 60, 63–64, 81b, 165–67 metaphorical reasoning 319–20, 332, 346 metrics 346 mice 37f, 38f, 41f, 186, 187f, 189f, 254, 260f Microcebus see lemurs Microchoerus 65f, 115f, 116, 117f, 117 Microsyops see plesiadapiforms middle ear 24, 50, 73 see also ossicles middle Eocene climatic optimum (MECO) 59f, 59, 85 middle Miocene climatic optimum (MMCO) 59f, 59, 96–97, 98, 306–7, 309 middle Miocene climatic transition (MMCT) 59f, 59–60, 304f, 306, 307 migration 90f, 90–91, 91b, 138f Miocene 35f, 52f, 59f, 59–60, 65f, 87–101, 93f, 95f, 125–36, 134f, 137f, 144f, 153f, 156, 168f, 170f, 231, 263f, 266f, 269f, 293f, 304f, 306, 313–14, 323f, 340f, 341– 42, 343–44, 347f Miopithecus see talapoin monkeys MIP see posterior parietal cortex M–L cones 208 MMCO (middle Miocene climatic optimum) 59f, 59, 96–97, 98, 306–7, 309 MMCT (middle Miocene climatic transition) 59f, 59–60, 304f, 306, 307 molecular clock 50–52 moles 35–36, 37f mongooses 171f, 171–72, 301b monito del monte 81b, 166 monogamy 6, 302, 304–5, 313–14 monophyletic group 17–19, 18f, 29, 40 monotremes 35f, 81b, 162f, 165–67, 195 motor cortex 28, 187f, 189f, 211, 212f, 215, 216–19, 218f, 219f, 222–23, 232f, 233, 234f, 238, 240, 241f, 247, 259, 290, 297, 310, 340f, 343, 344, 347f, 348–49 motor programs 290 motor systems 69–70, 212f, 234f mouse lemur see lemurs mouth 21, 218f, 241f MT cortex 245 M-type retinal ganglion cells 209, 237b multiple-demand cognition 254–55, 318t mustelids 170–72 myelin 233, 259, 260f, 267 Mysticeti see cetaceans navigation 169, 265–66, 286–87 Neanderthals see Homo neanderthalensis Necrolemur 65f, 118f, 118–19, 134f, 154 neocortex cytoarchitectonic types of 234 Neolithic 317, 328–29, 330, 346 neuroimaging 253–54, 259, 285b niche partitioning 69 nomadic hunter–gatherer bands 346 North America 54–55, 60, 75f, 80, 83, 207b Northern Hemisphere 36, 84–85, 98–99, 342–43 NOTCH gene see genetic mechanisms Notharctus 65f, 117f, 117–18, 133, 134f, 144f occipital lobe/occipital cortex 116, 130, 139, 264 ocular disparity 290, 294 oculomotor areas 215 Odontoceti see cetaceans odor receptors 64 Oldowan tools 137f, 269–70, 322–23, 324 olfaction 85, 133, 135, 164, 244 olfactory bulbs 64, 73, 85, 94, 94f, 113, 116, 129, 133–35, 134f, 144f, 163b, 189f, 230, 244, 308–9, 340f, 342, 347f Oligocene 52f, 65f, 87–96, 92f, 93f, 118f, 121f, 126–28, 144f, 153f, 169–70, 173f, 266f, 291, 293f, 303–5, 304f, 340f, 347f Eocene–Oligocene climatic transition (EOCT) 59f, 59, 60, 88, 91, 92f, 96, 101, 102, 153f, 154, 229, 302, 304f, 306 Oligocene bottleneck 91, 92f omnivory/omnivores 305, 307 omomyiforms Microchoerus 65f, 115f, 116, 117f, 117 Necrolemur 65f, 118f, 118–19, 134f, 154 Tetonius 65f, 117f, 117–18, 134f open woodlands 98, 99–101, 129, 301b, 306, 344, 351 opsins 208 orangutans 25b, 42f, 96, 239f, 252, 257f, 258f, 267–68 orbitofrontal cortex 189f, 205, 211–15, 212f, 219f, 222–23, 233, 234f, 236, 237–38, 247, 285, 290–91, 295–97, 311–12, 312f, 339, 340f, 343, 347f Oreopithecus 173f ossicles 24–25, 64, 73, 162f, 164 Otolemur see galagos overt attention 295, 343 fovea 295, 343 owl monkeys 41f, 95f, 131f, 207–8, 215, 229, 234f, 239f owls 207b Paleocene 34f, 35–36, 52f, 57f, 59f, 65f, 79–83, 118f, 121f, 153f, 162f, 166, 207b, 266f, 293f, 340f, 347f Paleocene eutherians 160, 162f, 166–67, 169 Paleocene primates 55, 79 Paleocene–Eocene thermal maximum (PETM) 58, 59f, 84, 85 Paleolithic 95f, 138f, 327, 342 paleontology 10, 11, 49–62, 114, 132 Pandemonium 67 pangolins 35f, 37f, 207b, 309–10 panins bonobos 42f, 42–43, 138f, 138, 140f, 252, 257f, 258f chimpanzees 42f, 97f, 114, 136f, 137f, 138f, 140f, 142f, 239f, 256f, 257f, 258f, 260f, 265f, 267–68, 269f, 304f, 340f, 347f Pan paniscus see bonobos

Pan troglodytes see chimpanzees papillary ridges 63, 73, 74, 238 see also fingerprints parallel evolution 23, 180 see also homoplasy Paramys 172, 173f Paranthropus 137f, 138f, 141f paraphyletic group 17–18, 18f, 38f, 40, 41–42, 42f, 65–66, 168f, 257f Parapithecus 65f, 90f, 92f, 126–28, 127f, 131f, 133, 134f, 144f parietal cortex see posterior parietal cortex; somatosensory cortex parietal–premotor networks 216, 223, 290, 339 Passingham, Richard xi, 4, 315, 318 pedal grasping 65f, 69, 70, 71, 209, 297, 343 periallocortex 196f, 198 peripheral vision 72–73, 295, 296–97 perirhinal cortex 286f, 287, 288f, 291 perissodactyls 21–22, 37f, 84–85 PETM (Paleocene–Eocene thermal maximum) 58, 59f, 84, 85 phalanges 70, 210b photoreceptors 207–8, 209 phyletic dwarfism 20f phylogenetic analysis 26b, 40, 130–32, 133, 161, 183–84, 263–64, 292 phylogenetic scale ix, 27 phylogenetic trend 139, 244, 246, 317 pigs x, 21–22, 37f, 168f piriform cortex 27–28, 111, 195, 196f, 197 placental mammals x, 12, 28, 33–34, 34f, 36, 37f, 37–38, 54, 60, 80, 162f, 208, 309–10 platyrrhines 38f, 38, 40, 41f, 52f, 65f, 90f, 93f, 95f, 98, 102, 118f, 121f, 126f, 130, 131f, 132, 134f, 143f, 144f, 153f, 156, 167b, 182f, 229, 232f, 235–36, 239f, 241f, 256f, 257f, 258f, 293f, 303f, 304f, 308–9, 340f, 347f, 350–51 Pleistocene 35f, 50f, 50, 52f, 59f, 90f, 95f, 96–101, 97f, 136–42, 140f, 144f, 153f, 157f, 168f, 266f, 269f, 304f, 317–35, 321f, 323f, 342, 344–46, 347f plesiadapiforms 19, 60, 64–66, 65f, 67–68, 70, 79–83, 90f, 110f, 117f, 118f, 118–19, 121f, 172 Carpolestes 65f, 67, 70 Pandemonium 67 Plesiadapis 64, 65f, 67, 68f, 70, 118f Purgatorius 54–55, 65f, 66, 67, 70, 90f Torrejonia 65f, 70 Plesiadapis 64, 65f, 67, 68f, 70, 118f Pliocene 35f, 90f, 97f, 99, 136f, 137f, 138f, 140f, 144f, 153f, 269f, 323f Plio-Pleistocene 95f, 96–101, 136–42, 168f, 266f, 301b, 304f poikilotherms 162–63 polar prefrontal cortex 233, 260f, 312f, 345 pollex (thumb) 94f, 239f, 268b, 301b polymorphic trichromacy 38f, 304f polyphyletic group 18, 35–36, 174, 207b pop-out 72–73, 216, 294 postcranial skeletal traits 152 posterior parietal cortex anterior intraparietal area (AIP) 241f, 242, 247 area 5 243–44 lateral intraparietal cortex (LIP) 187f, 241f, 242, 247 medial intraparietal area (MIP) 187f, 241f, 241–42, 247 ventral intraparietal cortex (VIP) 187f, 241f, 241–42, 247 postorbital septum 94f, 341 predation 94f, 94, 120f, 154–55, 172–74, 301b, 308, 309–10 predator avoidance 6, 313 leaping 86, 205, 301b prefrontal cortex 36, 41f, 119, 135, 183–84, 188–89, 189f, 192–93, 234f, 255–61, 256f, 257f, 258f, 260f, 263f, 308–9, 311–13, 326 prelimbic cortex 189f, 198, 215, 254 premolars 67, 68, 80, 207b premotor cortex 189f, 217–18, 218f, 233, 240–42, 312f, 340f, 347f Preuss, Todd xi, 36, 134–35, 233, 243 primary motor cortex see motor cortex primate innovations 29, 69–70 primate-on-primate competition 87, 206, 291 primate predators see predation primate social systems 292, 302 principal component analysis 56–57, 57b, 57f, 95f, 95–96, 127f, 130, 139, 140f, 141f principal sulcus 6, 131f, 132, 189f prizes vii, 12, 350 problem-solving ability 252 Proconsul 65f, 127f, 129, 144f, 153f, 155, 173f, 304f procumbent incisors 80, 82 procyonids 171–72 proisocortex 198 proposals 283, 288–89, 290, 291–67, 295, 308–9, 319–20, 326, 332 prosimians 17–18, 38f, 40, 83, 110f, 111, 117f, 144f, 264–65, 266f prospection 321f, 327, 345, 348 pterosaurs 53, 80 P-type retinal ganglion cells 222, 237b pulvinar 185, 220–21 Purgatorius 54–55, 65f, 66, 67, 70, 90f pygmy marmosets 19, 20f, 131f pygmy mouse lemur (Microcebus) 25b, 85, 115f, 116 pygmy shrews 25b pythons 207b quadrupedal locomotion 94, 95–78, 158, 303, 304–5, 310–11, 341, 344 arboreal 78, 94f, 95, 96, 102, 154, 230, 307, 308 cursorial 90f, 102, 230, 307, 309, 344 rabbits 35f, 35–36, 37f, 38f, 41f raccoons 210b rainforests x, 49, 58, 85 raptors 71, 207b rats 36, 37f, 38f, 41f, 185–86, 189f, 191–92, 210b, 212f, 218f, 219f, 235f reaching 72, 112–13, 192, 209–10, 210b, 216–20, 218f, 241f, 241–42, 290, 296–97 see also grasping reality monitoring 327–28 red-tail monkey 307–8

relational reasoning 318, 319–20, 348 relative brain size 51, 52f, 119, 120f, 126, 130, 133, 137, 169–70, 171f, 172, 252 see also encephalization quotient (EQ) relative olfactory-bulb size 133, 134f replica-in-miniature 187–88, 189f, 191–92, 193–94, 199, 242 reproductive rate 55–56 reptiles dinosaurs 53, 54–55 lizards 162–63, 194, 301b pterosaurs 53, 80 sauropsids 162f, 164–65 snakes 22, 207b, 213, 301b re-representations 326, 328, 345 retina 72–73, 207, 208, 221, 237 retinal ganglion cells 209, 222, 237b, 237 retinotectal projections 72, 181, 237 retinoic acid 270–71 retinotopic organization/maps 243 retrosplenial cortex 198, 260f rhesus monkeys see macaques/Macaca rhodopsin 208 ring-and-core structure 196f, 197 ring-tailed lemurs 243–44 rodents capybaras 81b Cedromus 172, 173f Ischyromys 172, 173f mice 37f, 38f, 41f, 186, 187f, 189f, 254, 260f Paramys 172, 173f rats 36, 37f, 38f, 41f, 185–86, 189f, 191–92, 210b, 212f, 218f, 219f, 235f squirrels 37f, 41f, 67, 81b, 118f, 118–19, 173f rods 208 Rooneyia 10f, 65f, 68f, 116–17, 117f, 134f, 154 S1 cortex 187f, 195, 196f, 212f, 218f, 219f, 232f S2 cortex 187f, 195–97, 212f, 219f, 234f saber-tooth tigers 166–67, 301b saccades 294 Saguinus see tamarins satiation/satiety 214, 228–29, 231, 237–38, 247 sauropsids 162f, 164–65 savanna 58, 97f, 99–100, 155 savanna monkeys see cercopithecoids scala naturae ix, 26–27, 114, 181 Scandentia see tree shrews S cones 208 seasonal resource volatility/seasonal variation 11, 94, 100, 290, 297, 302 seeds 53–54, 68, 79b, 80, 82, 289–90 Seiffert, Erik 51, 54, 90–91, 121 selective factor/selective pressures 6, 11, 83, 87, 94f, 94, 96, 150, 268b, 313, 318, 318t self representation of 324–32 semantic generalization 318t, 320, 336, 346 semantic memory 261–62, 319–20 sensory modalities 245, 283, 319, 322 sexual dimorphism 303, 324–25, 342 Simonsius see Parapithecus sister group 17, 18f, 18–19, 41f, 181, 207b skeletal traits 129–30, 152, 159, 209–10, 341–42 small-branch niche see fine-branch niche small-scale societies 324, 329, 330–31 Smilodectes see extinct primates smooth-pursuit eye movements 294 snakes 22, 207b, 213, 301b snouts 74, 85 social-brain hypothesis 113, 170, 291–93, 302–3, 305, 326–27, 328, 332–33, 346 social cognition 206, 270, 313, 327 social cooperation 97–98, 318t, 328 social intelligence see ecological intelligence; social-brain hypothesis social signaling 313 social systems 6, 96, 113, 151, 206, 292–94, 302, 303, 324–32, 342, 346 somatic sensation 63–64, 161 somatosensory areas/cortex 27–28, 64, 185–86, 187f, 196f, 212f, 216, 218f, 238, 240, 343 South America 36, 40, 75f, 81b, 90–91, 91b, 125, 166, 167b, 207b spatial memory 135, 191–92 speciation 17, 18–19, 54, 156 speech 24, 163b, 261–62, 325 spider monkeys 41f, 95f, 131f, 132, 217, 239f spinal cord 22, 32, 185–86, 217, 238 squirrel monkeys 20f, 40, 95f, 131f, 239f, 241f, 266f squirrels 37f, 41f, 67, 81b, 118f, 118–19, 173f SRGAP see genetic mechanisms stem group 17, 18f stem primates 54, 57f, 64, 66, 70, 115f, 152, 289 stereopsis 79–80 stone tools 137f, 269–70 see also Acheulean tools; Oldowan tools strepsirrhines 38f, 39–40, 41f, 52f, 65f, 73, 85, 89f, 90f, 92f, 117f, 118f, 121f, 125, 126f, 127f, 134f, 143f, 144f, 182f, 207, 231–33, 232f, 234f, 235f, 241f, 244, 256f, 292–93, 293f, 303f, 340f striate cortex see V1 cortex striatum 7–8, 261, 338 structural neuroimaging see neuroimaging sulcal patterns 132, 139, 262, 263f sulci 131f, 132–33, 212f, 235–36, 260f superior colliculus 116, 209, 215, 236–37, 237b superior temporal cortex 243, 245, 310, 340f, 342, 347f superior temporal polysensory area 245 sweepstakes migration 90–91

Sylvian sulcus see lateral sulcus synapsids 161–62, 162f, 164–65 synaptic density 270–71 synaptogenesis 270–71 tactile “fovea” 73, 238 tactile sensation 73 tail 39, 67, 102, 217 talapoin monkeys 41, 42f, 112 tamarins 20f, 26b, 41f, 131f tapetum lucidum 207–8 tarsiers 27, 38f, 40, 41f, 52f, 65f, 89–90, 90f, 121f, 126f, 127f, 143f, 144f, 153f, 182f, 232f, 233, 237, 295, 340f, 347f taxonomy 31, 32–33, 36–37, 38–39, 242 technological knowledge 319–20, 323f, 324, 332 teeth see dentition Teilhardina 84–85 see also early Euprimates temperate latitudes 84, 101 temporal cortex/temporal lobe 94f, 116, 144f, 243, 245, 247, 258f, 260f, 261–62, 264, 265f, 313, 321f, 322, 345, 347f temporal–parietal junction 305 tenrecs 34f, 35–36, 37f, 162f terminal arbor 58 terminal-branch milieu see fine-branch niche Tetonius 65f, 117f, 117–18, 134f tetrapods 21–22, 36 thalamus 189–90, 209, 212f, 234f, 237b, 338 theory of mind 254–55, 265f, 318t, 326, 348 therapsids 161–62, 162f, 165–66 therians 36, 162f, 195 tigers 68–69, 166–67, 301b time travel/time machine vii, 150–52, 326 titi monkeys 40, 41f, 52f, 95f, 131f, 239f toenails 60, 68–69, 70, 71, 87 tools see stone tools toothed whales, Odontoceti 167, 168f, 286–87 Torrejonia 65f, 70 transcortical network 10–11, 192, 219f, 240, 270, 271, 283, 305–6, 319–20, 321f, 345 tree shrews 33–34, 34f, 35f, 36, 37f, 41f, 52f, 90f, 125, 126f, 134f, 182f, 183, 184f, 184–85, 215–19, 218f, 219f, 220–21, 222, 232f, 235f Triassic 161–62 trichromacy 38f, 94f, 208, 304f triune-brain theory 180, 194 tropical forests 58, 86, 99–100, 101, 150, 297, 306, 309, 314, 342–43 tundra 60, 350 Tupaia see tree shrews turbinal bones 164 tympanic bulla 73 typically layered cortical areas 188–89, 319–20, 350, 351 Tyrannosaurus rex 111 uncinate fascicle 265f ungulates see artiodactyls; perissodactyls upward grade-shifts 121, 124–25, 127f, 144f, 144, 152, 153f, 154, 155, 171f, 289, 307, 337, 342 ursids (bears) ix, 37f, 166–67, 170–71, 301b V1 cortex 40, 234f, 257f V2 cortex 222, 234f V3 cortex 212f, 219f, 220, 234f Vallesian crisis 59–60, 97–98, 165b, 306, 307 ventral intraparietal cortex (VIP) 187f, 241f, 241–42, 247 ventral visual stream 185, 193, 215, 245, 287, 288f, 311–12, 312f ventrolateral prefrontal cortex 189f, 213, 233, 311–12, 312f vervet monkeys 41f, 42f, 97f, 256f Victoriapithecus 65f, 95f, 95–96, 127f, 128–29, 131f, 132, 133, 134f, 144f, 153f, 304f VIP see posterior parietal cortex visual acuity see acuity visual adaptations 68, 69–70, 73, 74, 208, 222 visual affordances 219, 283, 290, 311–12, 326, 344 visual cortex 10, 94, 133–34, 188, 220, 222, 234f, 243, 244 see also dorsal visual stream; inferior temporal cortex; MT cortex; perirhinal cortex; posterior parietal cortex; occipital lobe/occipital cortex; V1 cortex; V2 cortex; V3 cortex; ventral visual stream visual fixation 246–47, 295 visually guided movement 290, 292–93, 296–97, 342–43 visual memories 221 visual metrics 287, 295, 312–13 visual representations 221, 288–89, 312f visual scenes 265–66 visual submodalities 287, 288f visual texture 286f, 287, 288f, 311–12, 312f vomeronasal organ 73 weathering 59, 88 whales see cetaceans wolves 166–67 wombats 162f, 166–67 woodlands 58, 98, 100, 301b, 306, 344