Talent Development from the Perspective of Developmental Science: A Guide to Use-Inspired Research on Human Excellence [1st ed. 2023] 3031462041, 9783031462047

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David Yun Dai

Talent Development from the Perspective of Developmental Science A Guide to Use-Inspired Research on Human Excellence

Talent Development from the Perspective of Developmental Science

David Yun Dai

Talent Development from the Perspective of Developmental Science A Guide to Use-Inspired Research on Human Excellence

With Contributions by Yukang Xue and Qi Sun

David Yun Dai Department of Educational and Counseling Psychology University at Albany, State University of New York Albany, NY, USA

ISBN 978-3-031-46204-7    ISBN 978-3-031-46205-4 (eBook) https://doi.org/10.1007/978-3-031-46205-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

This book is dedicated to my three esteemed colleagues and dear friends, Rena (Subotnik), Paula (Olszewski-Kubilius), and Frank (Worrell) David Yun Dai

Preface

Whether the goal is to make eminent contributions in science and art, sport, and entrepreneurship or it is a mere endeavor to perfect one’s trade purely to draw personal satisfaction, talent development reflects a drive for excellence that is deeply rooted in the human surviving-thriving instinct as well as a longing for transcendence, personified in people such as Steve Jobs and Elon Musk. Talent development has gained currency as many blue-collar and even white-collar jobs are being replaced by the booming growth of artificial intelligence (AI), pushing for a profound change in the need for talent development as a way of maintaining dignity, self-expression, and self-perfection. However, research efforts to understand the nature and nurture of talent largely responsible for modern human civilization as we know of, albeit a long history since Galton’s (1869) investigation, has remained sporadic, spread out in several research traditions rather than a well-coordinated systematic endeavor. It is safe to say that, as a field of scholarship and research, it is far less developed compared to similar fields or branches of developmental science, such as developmental psychopathology or developmental criminology, fields that are aspiring to a disciplinary status. It can be argued the importance of talent development and human excellence, even from a positive psychology point of view, is of at least equal importance compared to painstaking efforts to prevent and rehabilitate individuals with mental disorders or criminal behaviors. For that matter, educators, policy makers, counseling psychologists, social workers, and parents, among others, are all stakeholders of the initiative for talent development (TD). Yet, as a field of scholarship and research, TD is still a new kid in the block, so to speak. It does not show up on a catalogue of subjects that features prominently developmental psychopathology or developmental and lifespan criminology. Even in psychology and developmental science, the topic of TD only occupies a marginal place, albeit an occasionally heightened attention here and there. Research on talent development remains an academic exercise in several small isolated circles, distributed in different disciplines, barely addressing the question of how knowledge of TD can be propagated from basic understandings to various applied domains to inform policy and practice, and how a system of infrastructure and support can be built to support talent development, an endeavor vii

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that amounts to niche constructions in cultural evolution for a variety of pathways to human excellence. This book is an attempt to remedy the situation. To foreshadow the purpose and scope of this book, TD is an emerging field of scholarship and research that is by nature cross-disciplinary, encompassing a wide range of disciplines related to human development, from biology, neural science, psychology, to education, sociology, cultural studies, and policy analysis regarding human development. Developmental science as a metatheoretical guide provides an epistemic scope wide enough to encompass all these disciplinary bases for systematical inquiry into talent TD, and defining research on TD as use-inspired research provides the main impetus for driving a research agenda. The purpose of the book is to provide a synthesis of what have been done in research on talent development, and what remains to be done in better orchestrating research efforts toward a coherent research agenda aiming to improve social conditions of TD, and, more pertinent to TD, the pursuit of excellence. To accomplish this goal, the book delineates major issues we need to address to make TD research in line with a developmental science perspective. It also attempts to explore how research methodology can be made commensurate with the complexities of human talent and TD. Of central concern is how different research traditions and theoretical perspectives (gifted and talented studies, expertise research, and studies of creative productivity in various domains) can forge an alliance under the guidance of developmental science. This is the only way to make research efforts better coordinated toward a coherent, systematic understanding of the nature of talent and TD. This is also the best way to develop use-inspired knowledge that can be translated into scientifically supported policy and practice that help build an infrastructure of support as well as pedagogical and technical support. Following this argument, the reader of this book can expect to find the following components intended to provide a guide to research on talent development with the above vision in mind: • A review of the history of research and various models and theories of talent development • A developmental science perspective from which the traditions of gifted and talented studies, expertise research, and studies of creative productivity in various domains can be integrated toward a coherent research agenda • A formulation of talent development research as use-inspired, with a research cycle from basic to applied research, comparable to a research agenda formulated by developmental psychopathology or developmental criminology • A breakdown of TD research into six types of research, each treated in a separate chapter, with a focus on its goals and methods, and how the six types of research together constitute a chain of reasoning and a research program as a whole • A summary of the current state of research on TD, followed by reflections on the epistemological and methodological issues of understanding human talent and excellence.

Preface

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Personal Acknowledgments Thanks are due to my two co-authors, Yukang Xue and Qi (Skyla) Sun, who are doctoral students in our Educational Psychology program, and who generously offered to help me with this book project. Yukang took my graduate seminar in 2019 on giftedness, talent, and creativity, and both of them have research and publication experiences on the topic of child/adolescent development and creativity. They were mainly responsible for writing three chapters (Chaps. 4, 7, and 9) under my close supervision and guidance. For each of these chapters, I provided conceptual structures and sources of research, as well as detailed feedback on the initial draft and many rounds of revisions they made. They also contributed to the writing of Chap. 10, especially the survey study reported in that chapter. They were also extensively involved in editorial assistance that made the manuscript presentable. I appreciate their contributions, though any error in the book is mine. Thanks are also due to Lan Lan, my graduate assistant and a doctoral student in our PhD program, who assisted me in the time-consuming task of proofreading the manuscript. The book project is partly supported by a grant from the Army Research Institute, Behavioral and Social Sciences (Grant No. W911NF-17-1-0236) to Dr. Ron Sun of RPI, and me, on a research project on motivation, learning, and performance, for which I served as a Co-PI. I have enjoyed the collaboration with Ron as the PI of the grant project (including more than a dozen of lunch meetings over the years). The quasi-micro-genetic studies of undergraduate students working on Raven’s progressive matrices generated some valuable insights into how reasoning and problem-­ solving persists (or conversely, breaks down), leading to either strong versus deteriorated performance, a truly micro-development of intellectual competence while on task. Thanks are also due to SUNY-Albany for granting me a sabbatical leave for the academic year of 2022, which allowed me to give undivided attention to this book project.   Last but not least, this book is dedicated to Rena, Paula, and Frank, whose tireless work on talent development research and practice has won my admiration, and whose generous support and encouragement for my work over the past two decades has been invaluable in our joint efforts to facilitate a paradigm shift in gifted education. They graciously involved me  for presentations at the inaugural and second American-European Summit on Talent Development in Washington, DC (2016) and Nuremberg, Germany (2018), respectively. The collaborative spirit that brought us together has also been instilled into this book. It is my hope that this book will contribute to the continuing scholarly discourse among all kindred spirits who, together, can move the field forward. Albany, NY, USA September 5, 2023

David Yun Dai

Special Acknowledgments

This work was partly supported by a grant to the senior author from Army Research Institute for Behavioral and Social Sciences (Grant No. W911NF-17-1-0236). The author was encouraged to freely express his opinions. Ideas presented in this book, therefore, do not necessarily represent those of the funding agency.

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1

Introduction: Talent Development as a Central Issue for the Twenty-First Century������������������������������������������������������������������    1 1.1 Rationale for This Book��������������������������������������������������������������������    3 1.2 The Social Imperative for Research on Talent Development������������    3 1.3 The Scientific Imperative for Research on Talent Development������    4 1.4 The Practical Imperative of Use-Inspired Research on Talent Development ��������������������������������������������������������������������    5 1.5 Purpose and Scope of This Book������������������������������������������������������    7 References��������������������������������������������������������������������������������������������������    8

2

 Existing Theories and Models of Talent Development��������������������������   11 2.1 Phenomena and Concepts: Scope of Talent Development Research��������������������������������������������������������������������������������������������   11 2.1.1 Relevant Terminology of Talent��������������������������������������������   12 2.1.2 Related Terminology of Talent Development ����������������������   15 2.2 A Brief History of Research on Talent Development (TD)��������������   19 2.3 Theoretical Models of Talent Development (TD)����������������������������   22 2.3.1 Component Models of Talent Development ������������������������   23 2.3.2 Developmental Process Models of Talent Development ������������������������������������������������������������������������   25 2.3.3 Developmental Systems Models of Talent Development ������������������������������������������������������������������������   26 2.4 Summary: Toward More Systematic Approach to Talent Development����������������������������������������������������������������������   29 References��������������������������������������������������������������������������������������������������   31

3

Conceptual Frameworks Guiding Research on Talent Development ��������������������������������������������������������������������������������������������   37 3.1 Why We Need a Developmental Science Framework����������������������   37 3.2 A Life-Span Developmental Systems and Talent Development ������   39 3.2.1 Developmental Science as a Metatheoretical Guide������������   40 3.2.2 Developmental Science as a Methodological Strategy ��������   44 xiii

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3.2.3 Summary ������������������������������������������������������������������������������   45 3.3 A Conception of the Research Cycle of TD Research����������������������   46 3.3.1 Phase I Research: Phenomena to Be Defined and Understood����������������������������������������������������������������������������   46 3.3.2 Phase II Research: Seeking Grounded Knowledge��������������   47 3.4 How the Cycle of the Three-Phase Research Agenda Works ����������   51 References��������������������������������������������������������������������������������������������������   52 4

Type 1 Research: Phenomenology, Definition, Classification, and Foundation����������������������������������������������������������������������������������������   57 4.1 Definition and Classification������������������������������������������������������������   58 4.1.1 Starting with the Immediate Phenomenology of Talents������������������������������������������������������������������������������   58 4.1.2 Creating a Framework for Developing Taxonomies of Talent Domains����������������������������������������������������������������   59 4.1.3 How Taxonomy and Framework Help to Chart Research on Specific Phenomena ����������������������������������������   61 4.2 Three Foundations����������������������������������������������������������������������������   61 4.2.1 Neurophysiological Foundation��������������������������������������������   62 4.2.2 Sociocultural Foundation������������������������������������������������������   63 4.2.3 Cognitive-Affective-Conative Foundation����������������������������   65 4.3 Developmental Manifestations of Talent������������������������������������������   67 4.3.1 The Foundational or Formative Phase of Talent Development (in Childhood)������������������������������������������������   68 4.3.2 The Transitional Phase of Talent Development (Typically During Adolescence) ������������������������������������������   68 4.3.3 The Advanced Phase of Talent Development (Typically Starting in Young Adulthood)������������������������������   69 4.4 Recommendations����������������������������������������������������������������������������   72 4.4.1 Foundational Issues Worth Exploring����������������������������������   72 4.4.2 Methodological Approaches and Options����������������������������   73 4.4.3 Addressing Research Questions Adequately������������������������   73 References��������������������������������������������������������������������������������������������������   73

5

Type 2 Research: Differential Learning and Divergent Development ��������������������������������������������������������������������������������������������   79 5.1 Differential Learning and Divergent Development: A Population-Based Perspective on Talent Development����������������   80 5.2 Research Traditions in This Line of Inquiry ������������������������������������   81 5.2.1 Behavioral and Molecular Genetics Research����������������������   82 5.2.2 Placement-Prediction Studies ����������������������������������������������   83 5.2.3 Expertise Development Research: Examining Proximal and Distal Determinants����������������������������������������   84 5.2.4 Modeling Long-Term Talent Development��������������������������   85 5.3 Contributions and Issues Regarding Type 2 Research����������������������   90 5.3.1 Contributions of Type 2 Research����������������������������������������   90 5.3.2 Issues on Type 2 Research����������������������������������������������������   92

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5.4 Recommendations����������������������������������������������������������������������������   95 5.4.1 Step 1. Mapping Out Major Parameters ������������������������������   95 5.4.2 Step 2. Negotiating a Good Nomothetic-Idiographic Balance by Which the Problem Chosen Can Be Tackled Properly��������������������������������������������������������������������������������   96 5.4.3 Step 3. Determining Implications of Having Interactive Systems of Specific Endogenous and Exogenous Components: Beyond Dichotomous Thinking����������������������   97 References��������������������������������������������������������������������������������������������������   97 6

Type 3 Research: Intrapersonal and Psychosocial Processes and Changes ��������������������������������������������������������������������������������������������  103 6.1 Rationale for Type 3 Research: Delineating Micro-Level Intrapersonal and Psychosocial Processes, Changes, and Transitions����������������������������������������������������������������������������������������  104 6.2 The Structure of Type 3 Research and Three Principles������������������  106 6.2.1 Principle 1: Situating TD in a Personal and Social Context to Reveal the Developmental Importance of TD to the Person as Well as the Social Institution Involved��������������������������������������������������������������������������������  106 6.2.2 Principle 2: Identifying the Connections the Person Makes to the World Through TD Every Step of the Way, and What Drives and Regulates TD in a Social and Personal Context������������������������������������������������������������  107 6.2.3 Principle 3: Specifies Developmental Changes (e.g., an Emergent Interest) and Transitions (e.g., From Interested Exploration to Committed TD Effort) over an Extended Period of Engagement or Proximal Processes ����������������������������������������������������������  108 6.3 A Capsule Review of the Past Research ������������������������������������������  109 6.3.1 Competence Development����������������������������������������������������  109 6.3.2 Interest Development������������������������������������������������������������  110 6.3.3 Identity Development and Commitment������������������������������  111 6.4 Recommendations����������������������������������������������������������������������������  115 6.4.1 Step 1: Framing and Structuring a Type 3 Research Study���������������������������������������������������������������������  116 6.4.2 Step 2. Paying Attention to Three Facets of TD ������������������  117 6.4.3 Step 3. Developing Methods and Designs with Attention to the Timescale and Social Scope of Interaction ����������������������������������������������������������������������������  118 6.4.4 Step 4. Interpretation and Articulation of the Significance��������������������������������������������������������������������������  119 References��������������������������������������������������������������������������������������������������  120

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 Type 4 Research: Developing Proximal Prediction Models ����������������  125 7.1 An Introduction to the Basic Principles of Type 4 Research������������  126 7.2 Successful Transitions at Critical Junctures of Talent Development ������������������������������������������������������������������������������������  128 7.2.1 The Emergence of Personal Action Space (PAS) and Characteristic Adaptation (CA) When One Person Transitions from the Foundational Phase to the Transitional Phase ������������������������������������������������������  130 7.2.2 The Transition from Characteristic Adaptation (CA) to Maximal Adaptation (MA) during the Crystallizing Phase����������������������������������������������������������  131 7.2.3 Moving Beyond Technical Proficiency to Create a Distinct Niche for Contributions����������������������������������������  131 7.2.4 Summary ������������������������������������������������������������������������������  132 7.3 Developing a Set of Predictors in Model Building: Achievement and Psychosocial Milestones as Predictors of a Successful Transition ����������������������������������������������������������������  133 7.3.1 Achievement Milestones and Psychosocial Milestones��������  134 7.3.2 Developmental Constraints ��������������������������������������������������  134 7.4 Predicting Talent Progression with Developmental Markers������������  135 7.4.1 The Variable Characteristics of Predictors and Outcomes�����������������������������������������������������������������������  135 7.4.2 Timing of Predictors and Transitions as a Critical Factor to Be Considered in Prediction Models ��������������������  136 7.5 Recommendations for Future Research��������������������������������������������  141 7.5.1 Step 1. Conceptualize the Targeted Problem������������������������  141 7.5.2 Step 2. Building a Prediction Model with Proper Considerations of Statistic Models That Fit with the Mature of the Data and Variables������������������������������������  141 7.5.3 Step 3. Interpreting the Results with Caution ����������������������  142 References��������������������������������������������������������������������������������������������������  142

8

 Type 5 Research: The Foundation and Technology of Talent Identification��������������������������������������������������������������������������������������������  145 8.1 The Differential, Developmental, and Sociocultural Foundations of Talent Identification ������������������������������������������������  146 8.1.1 The Differential Tradition: The Nomothetic-Idiographic Tension����������������������������������������������������������������������������������  146 8.1.2 Developmental Underpinnings of Talent������������������������������  148 8.1.3 Sociocultural Aspects of Talent Identification����������������������  149 8.2 Technical and Practical Considerations of Talent Identification������  149 8.2.1 Statistical, Practical, and Clinical Significance��������������������  150 8.2.2 Determination of Threshold Requirements: The Issue of Trade-Off Between False Negatives and False Positives����������������������������������������������������������������  151

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8.2.3 Selectivity and Specificity as a Matter of Clinical Precision��������������������������������������������������������������������������������  153 8.3 Research Questions on Talent Identification from a Developmental Science Perspective��������������������������������������������������  154 8.3.1 The Issue of What to Identify and How to Assess����������������  155 8.3.2 When to Identify What: The Timing of TI as a Developmental Issue ������������������������������������������������������������  157 8.3.3 Talent Identification in the Larger Scheme of TD: Beyond the Selection/Placement Paradigm of TI ����������������  160 8.4 Recommendations for Designing a Study on Talent Identification ������������������������������������������������������������������������������������  163 8.4.1 Step 1. Putting TI in the Larger Context of TD, and Considering Foundational Issues and Practical Contexts Involved ����������������������������������������������������������������  163 8.4.2 Step 2. Selecting Identification Criteria and Determining Appropriate Techniques of Assessment Given the Characteristics of the Targeted Population����������  164 8.4.3 Step 3. Consider the Overall Design of a Study as to Whether It Can Answer the Research Questions Adequately Regarding the Substantive, Technical, and Strategical Aspects of an Identification Situation ����������������  164 References��������������������������������������������������������������������������������������������������  165 9

 Type 6 Research: Construction of Cultural Provisions and Interventions��������������������������������������������������������������������������������������������  171 9.1 Why Cultural Provisions and Interventions Are Essential for Talent Development��������������������������������������������������������������������  172 9.1.1 Sociocultural Factors Shape the Expression of Talent����������  173 9.1.2 When and Where of the Interaction of Individual and Sociocultural Factors Responsible for Talent Development: Macro-, Meso-, and Micro-Level Analyses��������������������������������������������������������������������������������  174 9.2 Implementing Cultural Provisions at Particular Developmental Junctures������������������������������������������������������������������  176 9.2.1 Competence, Interest, and Identity in the Foundational Phase: Developing Instruments and Habits��������������������������  177 9.2.2 Competence, Interest, and Identity in the Transitional Phase: Expanding One’s Personal Horizons and Developing an Enduring Interest������������������������������������������  178 9.2.3 Competence, Interest, and Identity in the Advanced Phase: Developing Cutting-Edge Competence and Carving Out a Niche for Personal Contributions ����������  179 9.2.4 Challenges of Studying Provisions and Interventions from a Developmental Science Perspective��������������������������  180

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Contents

9.3 A Review and Critique of Research Conducted During 2010–2020����������������������������������������������������������������������������  181 9.3.1 Studies on Developmental Responsiveness of Provisions and Interventions and Their Effects on Competence, Interest, and Identity in the Foundational Phase ������������������������������������������������������������������������������������  181 9.3.2 Studies on Developmental Responsiveness of Provisions and Interventions and Their Effects on Competence, Interest, and Identity Development in the Transitional Phase ������������������������������������������������������������������������������������  182 9.3.3 Studies of Provisions and Interventions in the Advanced Phase��������������������������������������������������������������������  183 9.3.4 Summary ������������������������������������������������������������������������������  184 9.4 Recommendations for Type 6 Research��������������������������������������������  187 9.4.1 Step 1. Situating a Study in a TD Context and Determining Its Main Rationale�������������������������������������������  187 9.4.2 Step 2. Deciding on the State of Research on the Issue and Decide What Methods Are Appropriate for a Fruitful Investigation����������������������������������������������������  187 9.4.3 Step 3. Addressing Theoretical Questions Rather Than Simply Asking Whether the Cultural Provisions or Psychological Interventions Are Practically “Effective”������  188 References��������������������������������������������������������������������������������������������������  188 10 The  Current State of Research and the Future Promise����������������������  191 10.1 A Survey Study of Extant Research Literature Between 2010 and 2020: Is Research Heading in the Right Direction?��������  192 10.2 How Well Are Key Issues of Developmental Diversity and Specificity Addressed Empirically? ����������������������������������������  196 10.2.1 Developmental Diversity (Divergence) and the Emergence of Domain-Specific Talent and Individuality������������������������������������������������������������������������  196 10.2.2 Developmental Specificity and Properly Situating Talent Development Research��������������������������������������������  197 10.3 Is Research of Different Foci Well Integrated to Address Developmental Complexity?����������������������������������������������������������  197 10.4 How Can Developmental Criminology and Developmental Psychopathology Teach Us About Research on Talent Development? ��������������������������������������������������������������������������������  200 10.4.1 Structural Commonalities Between TD Research and the Other Two Fields of Research��������������������������������  201 10.4.2 Distinct Features of Talent Development and Unique Considerations of TD Research������������������������������������������  204 10.4.3 Sum-Up: Silver Lining for a New Fledgling Field of Research������������������������������������������������������������������������������  206

Contents

xix

Appendix: Search Terms Used, Relevant Journals, and Key Scholars Sampled������������������������������������������������������������������������������������������  207 Search Terms Used for Search on PsycInfo Database ������������������������   207 Relevant Journals Sampled������������������������������������������������������������������   207 Leading Researchers Sampled ������������������������������������������������������������   207 References��������������������������������������������������������������������������������������������������  207 11 Toward  an Epistemology of Talent Development and Human Excellence��������������������������������������������������������������������������������������������������  213 11.1 How Different Research Paradigms Address Developmental Diversity, Specificity, and Complexity��������������������������������������������  214 11.1.1 Addressing Developmental Diversity from a Parametric, Nomothetic Perspective��������������������������������  214 11.1.2 Dynamic Interaction Approaches����������������������������������������  216 11.1.3 A Focus on Emergence in Complex Systems ��������������������  216 11.2 The Changing Methodology in Studying Developmental Diversity������������������������������������������������������������������������������������������  217 11.2.1 From Variable-Centered to Person-Centered Approaches ������������������������������������������������������������������������  217 11.2.2 Person-Centered Approach I: Group Research Tracking Qualitative Different Patterns of Talent Trajectories�������������������������������������������������������������������������  220 11.2.3 Person-Centered Approach II: Field Research Identifying Distinct Contextualized Developmental Events and Patterns ������������������������������������������������������������  221 11.2.4 Person-Centered Approach III: Psycho-biographical Studies Mapping Long-Term Trajectories of TD and Creativity����������������������������������������������������������������������������  221 11.3 Seeking Developmental Specificity and Complexity in Explaining Developmental Diversity������������������������������������������  222 11.3.1 Developmental Specificity: Person/Process in Context/Time������������������������������������������������������������������  223 11.3.2 Developmental Complexity That Integrates the Biological, Psychosocial, and Existential ��������������������  224 11.4 Toward an Epistemology of Talent Development (TD) and Human Excellence ������������������������������������������������������������������  225 11.4.1 Three Features of Engagement That Promote Talent Development (TD)��������������������������������������������������������������  226 11.4.2 Three Ways of Divergence��������������������������������������������������  227 11.4.3 Three Levels of Emergence������������������������������������������������  228 11.4.4 Three Perspectives on Excellence��������������������������������������  230 11.4.5 Three Issues of Coherence��������������������������������������������������  231 11.5 Conclusion��������������������������������������������������������������������������������������  233 References��������������������������������������������������������������������������������������������������  233 Index������������������������������������������������������������������������������������������������������������������  239

About the Authors

David Yun Dai, Ph.D.,  is Professor of Educational Psychology and Methodology at the University at Albany, State University of New York. His research interests encompass giftedness, talent, and creativity. He has published 11 books and over 130 journal articles, book chapters, and other articles on general and educational psychology, talent development, and creativity. Yukang  Xue, M.S.,  is a Ph.D. candidate in the Department of Educational and Counseling Psychology at the University at Albany, State University of New York. His research is focused on creativity behavior and cyber behavior. Additionally, he conducts research related to methodologies and statistical analyses. Qi Sun, M.S.,  is a Ph.D. candidate of Educational Psychology and Methodology at the University at Albany, State University of New  York. Her research interests encompass creativity, talent development, language learning, and digital technology usage. She has published several articles and authored book chapter in both China and the United States.

xxi

List of Figures

Fig. 2.1

A conceptual foundation for ECT (Dai 2021)��������������������������������   28

Fig. 3.1

A taxonomy of six types of research organized as a three-phase cycle of TD research��������������������������������������������   46

Fig. 7.1

Results from a sample survival analysis ����������������������������������������  137

Fig. 8.1

Selectivity and specificity as two dimensions of talent identification ��������������������������������������������������������������������  151

Fig. 10.1 A survey study of TD Research between 2010 and 2020��������������  195 Fig. 10.2 A nested, multi-layered developmental system of human agencies. (Originally published in Dai 2017) ��������������������������������  198

xxiii

List of Tables

Table 1.1

Three categories of talent development (TD) research as per Stokes (1997)��������������������������������������������������������   6

Table 6.1

A matrix of developmental processes and personal, domain, and social contexts for type 3 research����������������������������������������  116

Table 8.1

A matrix of developmental benchmarks by developmental stages ������������������������������������������������������������������������������������������  158

Table 9.1

A matrix of provision categories by three TD phases ����������������  177

Table 10.1

Six main types or categories of research, subcategories, and sample studies����������������������������������������������������������������������  194

xxv

List of Demo Studies

Demo Study 4.1 Glaveanu et al. (2013) on Creative Action in Five Domains������������������������������������������������������������������   70 Demo Study 4.2 Amsel et al. (2022) on Differences Between Lawyers and Psychologists������������������������������������������������   71 Demo Study 5.1 Lubinski and Benbow’s (2006) on the SMPY Longitudinal Study ������������������������������������������������������������   87 Demo Study 5.2 Howard (2009) on Developmental Patterns of Talented Chess Players����������������������������������������������������������������������   88 Demo Study 6.1 Barron (2006) on Interest-Driven, Self-Sustained Learning������������������������������������������������������������������������������  112 Demo Study 6.2 MacNamara et al. (2008) on the Psychology of Developing Excellence ��������������������������������������������������  114 Demo Study 7.1 Güllich et al. (2019) on Olympic Super-Elite and Elite Athletes����������������������������������������������������������������  138 Demo Study 7.2 Habicht (2022) on German Psychologists’ Career Development ����������������������������������������������������������������������  139 Demo Study 8.1 Wai et al. (2009) on Spatial Ability for STEM Domains������������������������������������������������������������������������������  161 Demo Study 8.2 Aujla and Redding (2014) on Identification of Talented Young Dancers ������������������������������������������������  162 Demo Study 9.1 Stoeger et al. (2019) on Effects of Mentorship on STEM-Talented Girls����������������������������������������������������  184 Demo Study 9.2 Henriksen et al. (2019) on What Makes Successful Interventions in Sports��������������������������������������������������������  186

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Chapter 1

Introduction: Talent Development as a Central Issue for the Twenty-First Century

Talented individuals who made extraordinary contributions have consistently held a prominent place in the public sphere, encompassing celebrated athletes, artists, scientists, and now star entrepreneurs. This conspicuous presence has not only captivated public attention but has also consistently beckoned researchers due to its intrinsic intellectual curiosity. One illustrative case that stands out is the intrigue surrounding Albert Einstein’s brain (Diamond et  al. 1985; Men et  al. 2014). Numerous popular books have dissected the mechanics behind the achievements of these societal “outliers” (Gladwell 2011). The allure of exceptional human excellence and talent has been a subject of fascination in both popular media and intellectual circles for centuries. However, despite occasional appearances in news articles or on social media platforms, talent development leading to human excellence has never assumed a central position within scientific discourse, despite its central contributions to modern human civilization. Significance of talent development extends beyond mere creation of spectacle, as it constitutes a fundamental cornerstone of nation-building, long-term economic development, and the advancement of society as a whole. As Csikszentmihalyi and Robinson (1986) argued, “talent is not just the expression of a personal trait, but the fulfillment of a cultural potential” (p. 283). Certainly, talent and talent development (TD) has drawn attention from multiple research traditions. These include investigations into the gifted and talented (Tannenbaum 1983, 1997; Gagné 1985, 2020), explorations of the neuropsychology of exceptional minds (Obler and Fein 1988), examinations of expertise (Ericsson 2006), and studies on the emergence and evolution of creative productivity (Sawyer 2012; Simonton 1999, 2018). These diverse traditions emerge from biological, psychological, and educational domains, encompassing differential psychology (as seen in gifted and talented studies and genetic/neural-physiological research), cognitive psychology (typified by expertise research), and developmental perspectives that delve into the processes shaping high-level performance across the life span

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_1

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1  Introduction: Talent Development as a Central Issue for the Twenty-First Century

(Gruber 1986; Sawyer 2012; Simonton 2018). However, these research paradigms remain somewhat disparate, lacking a coherent theoretical framework and a unified research agenda. Furthermore, their focus tends to be restricted to a select few prominent domains, rather than embracing a wide spectrum of talent trajectories and routes to excellence within the broader context of human development. Consequently, research on TD spreads across several research communities, each with its own tradition, foci, and ontological convictions and methodology. For example, a distinct group of scholars, from Terman (1925) to Gagné (2020), have devoted their career to gifted and talented children with the conviction that they are biologically privileged in some fundamental way. Those who focus on long-term trajectories of creative productivity in specific domains using psychometric or histriometric predictors also tend to bias toward a “trait” account of TD (e.g., Lubinski and Benbow 2006, 2021; Simonton 1999). Conversely, scholars from the cognitive and sociocultural traditions, in contrast to the differential and psychometric tradition, look into expertise and creative productivity in adulthood and try to trace its proximal developmental mechanisms and processes for explanation (e.g., Ericsson and Williams 2007; Gruber 1986; Sawyer 2012; Weisberg 2006). Further differences can be found not only in terms of different phases of TD under investigation but also in terms of different domains involved. For instance, gifted and talented studies are heavily academically oriented given their concerns over “gifted and talented education,” and expertise research heavily focusing sports, chess, and many performance domains, given their amenability to scientific observation and experimentation. Each tradition brings its own biases, convictions, and methodology into play; no wonder that the field of research on high potential and TD is full of competing claims and contradictory thoughts, lacking in conceptual clarity and theoretical coherence (Dai and Chen 2013). One way to forge a synthesis and integration is breaking institutional barriers among these research traditions, which is beyond the scope of this book. Another way is to develop a unifying conceptual framework for TD, which is the attempt and focus of this book. The reason why TD research does not enjoy the status of developmental criminology (Farrington 2003) or developmental psychopathology (Cicchetti and Toth 2009) is that efforts to understand TD and human excellence are going in many directions for reasons explained above, and the lack of coordinated efforts has plagued the field in terms of building a conceptual edifice and accumulated research that can weave together different foci and in different disciplinary efforts. A major assumption driving this book is that developmental science as a meta-level synthesis would help build coherence and resolve discrepancies. The following is the gist of what developmental science framework is about: Developmental science…describes a general orientation for linking concepts and findings of hitherto disparate areas of developmental inquiry, and it emphasizes the dynamic interplay of processes across time frames, levels of analysis, and contexts… In this perspective, the phenomena of individual functioning are viewed at multiple levels—from the sub-­ systems of genetics, neurobiology, and hormones to those of families, social networks, communities, and cultures. (The Carolina Symposium on Human Development 1996, p. 1)

1.2  The Social Imperative for Research on Talent Development

3

As a first approximation, underpinnings of talent and TD can be seen as ranging from the biological to the cultural provisions and social support, with learning and developmental processes mediating the developmental potential to cultural excellence and accomplishments. As mentioned earlier, some researchers may focus on stable individual differences, and others cognitive-affective and developmental processes, and still others educational strategies or learning ecology. A coherent TD research agenda in light of developmental science would stipulate that these separate research traditions should no longer be satisfied with a patched quilt they each contribute to, but attempt to weave a seamless fabric of conceptualizations and interpretations. A developmental science perspective would cast a variety of TD research in the larger context of life-span individual and human development by integrating biological, cognitive, psychosocial, educational, and social-cultural aspects of the pursuit of excellence. The complementarity of different research traditions is appreciated, and their respective contributions to the larger scheme of TD will be recognized and integrated in a unifying perspective on the nature and nurture of human potential and excellence (Dai and Sternberg 2021).

1.1 Rationale for This Book Developing such a common agenda as delineated above is surely a long shot, but pursuing a vision of a science of human excellence that aspires to the same status of developmental criminology or developmental psychopathology is a worthy undertaking, just like more than 20 years ago Seligman and Csikszentmihalyi (2000) brought positive psychology to the public scene. We might one day see a field of research on TD and excellence, not as a fragmentary and inconsequential academic exercise, but as part of developmental science, with much better coordinated research efforts that can inform social and educational policy and practice and make them scientifically more compelling, socially more equitable, and educationally more productive (Dai 2016).

1.2 The Social Imperative for Research on Talent Development Imagine a foreseeable future when all manual labor and replicable intellectual work (e.g., prediction models of weather forecast or stock performance prediction) is replaced by AI technology and robots; someone without a strong irreplaceable skillset (i.e., talent) would have a hard time finding a viable job and live a decent life. Framed more positively, we might soon find ourselves living in a world occupied by a variety of talents who are finding and carving out their own niches in an ever more demanding job market. In such a world, TD reigns supreme in three ways. First, TD

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1  Introduction: Talent Development as a Central Issue for the Twenty-First Century

is no longer confined to a limited number of fields (e.g., sports, arts, academics, and the high-tech sector) but permeates all aspects of education, professions, and human (capital) development. Second, opportunities for TD are no longer restricted to a tiny proportion of individuals deemed “elite” or “gifted and talented,” but have to be made accessible to the majority of the willing. Third, in the final analysis, TD is not just meant to “earn a living” or “make a name” for oneself; rather, it would become part of one’s life style for a more enriched, meaningful life. In other words, the pursuit of excellence is a telos in itself, very much like many retired elders nowadays use TD to seek personal enrichment and fulfillment. This way, TD and the pursuit of excellence in a personally meaningful way will become a prevalent means to personal fulfillment in a post-industrial age, an age of information, technology, and artificial intelligence.

1.3 The Scientific Imperative for Research on Talent Development However, how much do we know about TD? How do we debunk the myth that talent is possessed only by very few? How can we encourage  youths  to cultivate their distinct strengths and interests for the sake of long-term TD and life fulfillment? What are thresholds requirements for various domains of human endeavor? What does it take to develop one’s talent every step of the way? How can one cultivate a particular social niche, regardless of its cultural prestige? What kinds of resources, tools, and support are needed to make this happen? What benefits would such an initiative at TD and pursuit of excellence bring to individuals and society alike? What could be the norms, values, and ethics for such a social initiative? A wide array of questions present challenges for researchers who are concerned with education, human capital development, and individual self-actualization on all fronts of human endeavor. To be sure, we have accumulated much knowledge about these questions in the past century. However, we are not even close to what can be achieved. We cannot claim to achieve a firm status of science if we are still like the blind man, in the well-known fable, trying to figure out what an elephant looks like by touching various parts of the elephant. If we do not make TD and human excellence a unified field of research, we have no chance of even getting close to what developmental criminology or developmental psychopathology has achieved, both of which are use-inspired, guided by a coherent agenda, with a well-developed methodological toolbox, capable of contributing to the well-being of society as well as individuals.

1.4  The Practical Imperative of Use-Inspired Research on Talent Development

5

1.4 The Practical Imperative of Use-Inspired Research on Talent Development We can imagine that some research like natural science or archaeology as driven by pure academic interest or intellectual curiosity, and other research, such as biomedical science or psychopathology, as fundamentally driven by its potential benefits to human conditions and well-being. We can also consider some domains or disciplines of research, such as child development, life-span human development, education, as inherently value-laden or normative endeavor; that is, the purpose of the research is not merely to understand what it is (i.e., its natural states), but what is possible with opportunities, cultural resources, social support, and proper interventions (hence, human development). Talent development, a large part of which involves education and training, is not something genetically programmed to naturally unfold through development but the result of individuals’ transactional experiences with educational and cultural provisions and extended personal engagement over time (i.e., proximal processes as per Bronfenbrenner and Ceci 1994). If TD is by nature the fulfillment of a cultural potential (Csikszentmihalyi 1996), or a personal initiative in pursuit of excellence, then research on TD can be seen as an interdisciplinary effort to understand developmental potentials and achievements in terms of possibilities and constraints from biological, sociocultural, and developmental points of view. For that matter, it is an integral in the TD research agenda to understanding and developing educational conditions and creating social-cultural infrastructures that can help individuals cultivate talent potential and accommodate a variety of talent trajectories and pathways. In short, TD research is inherently use-inspired. To be sure, some research on “natural talent” or the exceptional brain could lean toward natural science. Perhaps the volume edited by Obler and Fein (1988) fits the category. Stokes (1997) categorized research along two dimensions: seeking fundamental understanding and considerations of use in applied settings. In a 2  ×  2 matrix, Stokes labeled any research as “pure” basic research if it seeks fundamental understanding without concerns over its practical utilities; for the convenience of memory, Stokes assigned it to Bohr’s Quadrant. A fascination with Einstein’s brain structure (Diamond et al. 1985; Men et al. 2014) mentioned in the beginning of this chapter might be an example, though it cannot be taken for granted the brain anatomy is purely biologically constitutional without the influence of long-term intellectual engagement, as structural changes in the brain can result from long-term cultivation of some skills (Schlaug 2001). Note that basic research, even “pure” in its intent, might eventually lead to revolution in the practical world just like quantum mechanics represented by Niels Bohr or Albert Einstein’s relativity theory. Stokes (1997) considered “pure” applied or application research as Edison’s Quadrant. Obviously, most research on gifted and talented education fits this category, as it is intended to improve the policy and practices of education. When researchers are seeking both deep understanding while considering its potential use,

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1  Introduction: Talent Development as a Central Issue for the Twenty-First Century

they are conducting use-inspired basic research. Stokes labeled it Pasteur’s Quadrant. Much of TD research, especially the one seeking deep understanding of talent as reflecting developmental diversity or as undergoing micro- and macro-­ level developmental processes and changes, falls into Pasteur’s Quadrant. As an approximation, we might consider TD research that attempts to understand the basic nature of talent or talent potential (e.g., Obler and Fein 1988) as belonging to Bohr’s Quadrant. A justification for the classification is that this kind of research is often descriptive in nature without an intent to inform practice. We consider all TD research of differential, developmental, and sociocultural slants as fitting Pasteur’s Quadrant (i.e., use-inspired basic research), given its implications for appropriate and timely education and training as well as cultural selection and social support. Finally, we categorize all research aiming to develop human-made systems of TD (e.g., education and TD infrastructure) and thus fitting the category of pure applied or application research (Edison’s Quadrant). The breakdowns of these categories of TD research are presented in Table 1.1. A caveat is in order that such “ontological innovations” can generate insights into the nature of talent itself. For example, the Flynn Effect, the population-wide improvement on IQ performance over decades prompted researchers to reflect their initial understanding of IQ scores as genetically determined.

Table 1.1  Three categories of talent development (TD) research as per Stokes (1997) Categories of research TD research Pure basic research Understand the affordances and constraints of a domain (Bohr’s Quadrant) Define the biological and cultural roots of talent Compare one talent domain with others Compare the talented with the less talented or the average Understand the social-cultural contexts for talent development Understand the genesis of talent from its developmental manifestations Use-inspired basic Map out developmental corridors, trajectories, pathways toward excellence research Estimate differential distributions of talent (Pasteur’s Explicate developmental processes, changes, and transitions; identify Quadrant) facilitating and impeding factors and conditions Identify differential and developmental markers, milestone achievements, and psychosocial events at critical junctures of TD Develop refined prediction models Explore the conceptual foundation for talent identification Explore educational and sociocultural foundation of TD in terms of goals and priorities, infrastructure building, educational policy Pure applied Develop and evaluate the practice of talent identification and explore research innovative assessment tools and systems (Edison’s Develop and evaluate the efficacy of various “ontological innovations” of Quadrant) TD, such as pedagogy and technical support Develop and evaluate a supportive infrastructure for TD

1.5  Purpose and Scope of This Book

7

1.5 Purpose and Scope of This Book Using a developmental science framework, the purpose of this book is to provide a unifying framework for guiding research efforts, identifying and bridging the gap between what we already know and what we have yet to know about TD in a wide range of manifestations of human excellence and accomplishments. This book presents a scope and sequence of research that would allow researchers to locate their niches and situate contributions in a broad spectrum of differential, developmental, and educational issues, while still maintain a broad perspective afforded by developmental science, regardless of whether they are working with children or adults, or whether their focus is sports and arts, or sciences and entrepreneurship. This book is intended to present a coherent framework and a research agenda that are built on and integrating many traditions of TD studies. Issues warranting research range from the nature of talent potential (individual differences and cultural support) to developmental processes and changes leading to various ways, forms, and levels of human excellence as we know of. These issues call for theoretical explanations as well as involve proper “cultural designs,” technical and practical, in making the developmental process viable and effective. This book is meant to facilitate efforts of researchers and graduate students who might be new to TD by mapping out different phases and types of research so that choosing an appropriate research topic focus is made easier (e.g., the right levels of analysis, the right analytic tools and research design given a phenomenon under investigation). As articulated above, as a guide to TD research, two overarching principles are applied. The first is a developmental science framework (Cairns et  al. 1996; Bronfenbrenner and Evan 2000), treating human development as a fundamentally a multi-level phenomenon subject to different levels of analysis. Particularly relevant to TD, the principle highlights three features of development: (a) developmental diversity is the norm, not an exception; in other words, divergent trajectories and pathways are prevalent for both biological and social-cultural reasons; (b) developmental specificity has to be honored by looking into contextualized proximal processes leading to specific developmental changes, which cannot be determined a priori; this is in sharp contrast to a normative approach that treats development in a decontextualized manner as a “natural occurrence,” which will happen sooner or later; and (c) developmental complexity is respected by looking across levels of analysis (biological and educational, personal and sociocultural, micro-level, short-­ term changes and macro-level, long-term changes) in an effort to generate an integrated account of developmental processes and changes. The second principle for guiding research, as adopted by this book, is producing use-inspired knowledge (Stokes 1997), treating TD research as involving a distinct “design” component aiming to promote positive development (Lerner 2004). In the context of TD, a “cultural design” can be seamless embedded in real life so that no one is even aware of its presence (e.g., values and belief systems in natural language as well as cultural artifacts, such as a violin or a studio, that are inherently cultural), but it can also exist as a more salient feature of one’s environment (e.g., music

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education or training). The use-inspired principle is predicated on the assumption that all TD is one way or another culturally scaffolded by people, things, or symbol systems to significantly enhance human functioning (Dai 2020). Accordingly, this book is organized into three parts as follows: • Integrating several lines of research on TD so that any effort can be cast in a larger scheme of things to see its significance and contributions to the whole body of research. This is done by developing a framework of research cycle and a taxonomy of research types that are connected rather than discrete (see Chaps. 2 and 3). • Summarizing what we know, and what we do not know so that future research efforts can be better informed. This is achieved by devoting chapters to tackling specific types of research as thoroughly as possible in its conceptual and technical issues, and then connecting them to broader themes and issues in TD research (see Chaps. 4, 5, 6, 7, 8, and 9). • Discussing strengths and weaknesses of the extant research on TD by looking for distinct contributions as well as the weakest links in the research cycle, and by looking at conceptualizations and research methods as compared to those in developmental psychopathology and developmental criminology (see Chaps. 10 and 11). What have been done in the past century in the area of TD and human excellence? More importantly, how can TD research be elevated to a new level of sophistication, with more coordinated research agendas and more theoretical coherence in light of developmental science framework? These two issues will be elaborated in Chaps. 2 and 3, respectively.

References Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nurture reconceptualized in developmental perspective: A bio-ecological model. Psychological Review, 101, 568–586. Bronfenbrenner, U., & Evan, G. W. (2000). Developmental science in the 21st century: Emerging questions, theoretical models, research designs and empirical findings. Social Development, 9, 115–125. Csikszentmihalyi, M. (1996). Creativity: Flow and the psychology of discovery and invention. HarperCollins. Csikszentmihalyi, M., & Robinson, R. E. (1986). Culture, time, and the development of talent. In R. J. Sternberg & J. E. Davidson (Eds.), Conceptions of giftedness (pp. 264–284). Cambridge University Press. Cairns, R. B., Elder, G. H. & Costello, E. J. (Eds.) (1996). Developmental science. Cambridge University Press. Carolina Symposium on Human Development (1996). Developmental science: A collaborative statement. In R. B. Cairns, G. H. Elder & E. Costello, J. (Eds.), Developmental Science (pp. 1–6). Cambridge University Press. Cicchetti, D., & Toth, S. L. (2009). The past achievements and future promises of developmental psychopathology: The coming of age of a discipline. Journal of Child Psychology and Psychiatry, 50(1–2), 16–25.

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Dai, D. Y. (2016). Envisioning a new century of gifted education: The case for a paradigm shift. In D. Ambrose & R. J. Sternberg (Eds.), Giftedness and talent in the 21st century: Adapting to the turbulence of globalization (pp. 45–63). SensePublishers. Dai, D. Y. (2020). Rethinking human potential from a talent development perspective. Journal for the Education of the Gifted, 43, 19–37. Dai, D. Y., & Chen, F. (2013). Three paradigms of gifted education: In search of conceptual clarity in research and practice. Gifted Child Quarterly, 57, 151–168. Dai, D. Y., & Sternberg, R. J. (2021). Introduction: Historical and contemporary perspectives on human potential. In D. Y. Dai, & R. J. Sternberg (Eds.), Scientific inquiry into human potential: Historical and contemporary perspectives across disciplines (pp. xvi–xxviii). Routledge. Diamond, M. C., Scheibel, A. B., Murphy, G. M., & Harvey, T. (1985). On the brain of a scientist: Albert Einstein. Experimental Psychology, 88, 1998–2004. Ericsson, K. A. (2006). The Influence of Experience and Deliberate Practice on the Development of Superior Expert Performance. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp.  683–703). Cambridge University Press. Ericsson, K. A., & Williams, A. M. (2007). Capturing naturally occurring superior performance in the laboratory: Translational research on expert performance. Journal of Experimental Psychology: Applied, 13, 115–123. Farrington, D. P. (2003). Developmental and life-course criminology: Key theoretical and empirical issues-the 2002 Sutherland Award address. Criminology, 41(2), 221–225. Gladwell, M. (2011). Outliers: The story of success. Back Bay Books. Gagné, F. (1985). Gifted and talent: Reexamining a reexamination of the definitions. Gifted Child Quarterly, 29, 103–112. Gagné, F. (2020). Differentiating giftedness from talent: The DMGT perspective on talent development. Routledge. Gruber, H. E. (1986). The self-construction of the extraordinary. In R. J. Sternberg & J. E. Davidson (Eds.), Conceptions of giftedness (pp. 247–263). Cambridge University Press. Lubinski, D., & Benbow, C.  P. (2006). Study of mathematically precious youth after 35 years. Perspectives on Psychological Science, 1, 316–345. Lubinski, D. & Benbow, C. P. (2021). Intellectual precocity: What have we learned since Terman? Gifted Child Quarterly, 65, 3–28. Lerner, R.  M. (2004). Genes and the promotion of positive human development: Hereditarian versus developmental systems perspectives. In C. G. Coll, E. L. Bearer & R. M. Lerner (Eds.), Nature and nurture: The complex interplay of genetic and environmental influences on human behavior and development (pp. 1–33). Lawrence Erlbaum. Men, W., Falk, D., Sun, T. Chen, W., Li, J. Yin, D. et al. (2014). The corpus callosum of Albert Einstein’s brain: Another clue to his high intelligence. Brain, 137(4), e268. Obler, L. K., & Fein, D. (1988). The exceptional brain: Neuropsychology of talent and special abilities. The Guilford Press. Sawyer, R. K. (2012). Explaining creativity: The science of human innovation (2nd ed.). Oxford University Press. Schlaug, G. (2001). The brain of musicians: A model for functional and structural adaptation. Annals of the New York Academy of Sciences, 930(1), 281–299. Seligman, E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55, 5–14. Simonton, D.  K. (1999). Talent and its development: an emergenic and epigenetic model. Psychological Review, 106(3), 435–457. Simonton, D. K. (2018). Defining creativity: Don’t we also need to define what is not creative?. The Journal of Creative Behavior, 52(1), 80–90. Stokes, D. E. (1997). Pasteur’s quadrant: Basic science and technological innovation. Brookings Institute Press. Tannenbaum, A.  J. (1983). Gifted children: Psychological and educational perspectives. Macmillan.

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Tannenbaum, A. J. (1997). The meaning and making of giftedness. In N. Colangelo & G. A. Davis (Eds.), Handbook of gifted education (2nd ed.). Allyn & Bacon, Incorporated. Terman, L. M. (1925). Genetic studies of genius: Vol. 1, Mental and physical traits of a thousand gifted children. Stanford University Press. Weisberg, R. W. (2006). Modes of expertise in creative thinking: Evidence from case studies. In K. A. Ericsson, N. Charness, P. J. Feltovich & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 761–787). Cambridge University Press.

Chapter 2

Existing Theories and Models of Talent Development

As Uttal (2003) pointed out, “Science must be orderly and, by implication, convergent. That is, science must proceed from the aggregation of a large number of observations to a small number of general and synoptic principles summarizing the meanings of those observations (p.  33).”  This chapter provides an overall of the history of theories and research on talent development (TD). We first review and define a series of terms (e.g., giftedness, precocity, polymathy) closely associated to TD; the terminology reveals implicit belief systems regarding of the nature of talent and TD. Then, we provide a capsule review of the intellectual and research history from Galton to contemporary inquiries into the nature and nurture of talent, approached from differential, educational, cognitive, developmental, and sociocultural perspectives. We identify the component, process, and developmental systems theoretical models of TD as three, guiding frameworks for research in the past decades. Finally, this chapter summarizes the state of knowledge on TD with a set of consensuses that can guide future research on TD and human excellence.

2.1 Phenomena and Concepts: Scope of Talent Development Research Within the domain of talent development (TD) research, a rich tapestry of theoretical models has been woven across various contexts, most notably in educational, developmental, and socioeconomic spheres. These models can be categorized into three primary types: component models (e.g., Gagné 2005, 2020; Lubinski and Benbow 2006, 2021; Piirto 1994; Renzulli 1986; Tannenbaum 1983, 1997), process models (e.g., Bloom 1985; Feldman 2003; Subotnik et al. 2011), and developmental systems models (e.g., Csikszentmihalyi and Robinson 1986; Dai 2021; Gruber and Wallace 2001). Additionally, a distinction can be drawn between implicit models of

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_2

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TD and explicit theories of TD. Implicit models, characterized by their intuitive and heuristic nature, lack the specific details in their arguments and propositions necessary for empirical investigation and testing. In contrast, explicit theories comprise a set of interconnected propositions that are more analytical and detailed, suitable for empirical examination of their validity and viability (Sternberg and Davidson 1986). Implicit models primarily serve as guiding principles for educational practices, while explicit theories, with or without immediate practical implications, offer the foundation for TD research. However, prior to introducing these models and theories, it is essential to establish a clear definition of talent and talent development. Furthermore, a comprehensive examination of the multitude of concepts related to talent and TD—whether they hold semantic, empirical, or conceptual significance— is warranted.

2.1.1 Relevant Terminology of Talent Within the confines of Chap. 4, we embark on the task of defining talent through the lens of three fundamental attributes: (a) excellence, (b) authenticity, and (c) domain specificity. In practical terms, an array of associated concepts enters our discourse, encompassing facets of personal attributes, developmental processes, and enduring outcomes. Genius as a concept conveys the exceptional nature of excellence, often linked to a remarkable rarity. However, a consensus regarding the degree of rarity remains elusive—is it one in a million or perhaps one per decade? Geniuses, standing as the utmost exemplars of talent, are true “outliers.” Their cognitive processes and growth trajectories may diverge significantly from those of the broader population. Figures such as Einstein, Mozart, or Wu Qingyuan in the game of Go epitomize this phenomenon. While some researchers fixate on the enigma of genius, utilizing geniuses as the prototype for talent in talent development (TD) research can be counterproductive. They might constitute outliers even within an exceptionally talented cohort within their respective fields, rendering the findings about TD inapplicable to a broader context. Giftedness encompasses two distinct interpretations: first, it signifies an exceptional cognitive quality possessed by a select few individuals; second, it denotes high intellectual potential. Historically, the former interpretation held greater prominence. For instance, in the tradition established by Terman, it referred to a psychometrically defined high IQ group, typically the top 1% in the population, characterized not only by elevated mental capacity but also distinct, often more superior modes of thought. This quality, believed to have genetic underpinnings, was considered enduring and permanent, suggesting that a gifted child would inevitably grow into a gifted adult. Dai (2010) recognized these assumptions of homogeneity and permanence as an essentialist perspective on giftedness. However, this perspective has become increasingly controversial, as numerous American and international scholars dispute the categorical interpretation of the high IQ group

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(e.g., Borland 2003). Due to its perceived lack of scientific definition and its historical baggage, some scholars advocate for completely abandoning this concept (Borland 2005; Peters and Borland 2020). Conversely, the second interpretation of giftedness, which denotes high intellectual potential, has gained wider acceptance and is more commonly employed today. Nevertheless, certain scholars, cautious of the term’s historical and conceptual associations, prefer alternative descriptors like “high ability,” “high potential,” “advanced,” or “precocious.” Despite this, researchers continue to employ the term “giftedness” as a comprehensive concept to signify a neural, cognitive, and personal quality contributing to exceptional performance or developmental outcomes. In fact, gifted and talented studies are intimately intertwined in research practice. Intelligence encompasses a range of cognitive functions, including selective attention, working memory, reasoning, metacognition, and the ability to transfer learning to new contexts, among others, all of which play crucial roles in effective learning and problem-solving (Neisser et al. 1996). A widely accepted perspective is that intelligence, as defined, represents a diverse cognitive spectrum (Cattell 1971), rather than a singular, unitary capacity as earlier scholars, like Spearman (1904), once believed. In this context, intelligence differs from talent. Intelligence refers to the utilization of broadly applicable perceptual and cognitive faculties for effectively navigating novel, intricate, and evolving environments. In contrast, the concept of talent is confined to capacities, sensitivities, and inclinations, innate or acquired, closely linked to specific cognitive and emotional stimuli and task requirements, hence being domain-specific. Nonetheless, intelligence remains relevant to talent whenever specific cognitive functions are central and pervasive within a talent domain. For instance, concept-­ based analytic reasoning might be more critical than skill-based procedural learning in one domain, while the opposite holds in another, like analytic science versus imaginative art, or in different roles within a domain, such as the distinctions between a quarterback and a lineman in American football. It is generally presumed that domains vary in terms of the threshold requirements of intelligence for effective learning and performance, with some requirements being domain-relevant yet not necessarily domain-specific (a topic to be discussed in Chap. 5). Taking the perspective of multiple intelligences (MI) proposed by Gardner (1983), one might view Gardner’s seven or eight intelligences as relatively independent cognitive functions (e.g., musical, linguistic, logical-mathematical, interpersonal), each acting as a modular device with its neurobiological substrates. Due to this modularity, one can excel in music or logical-mathematical intelligence while not necessarily excelling in linguistic or spatial intelligence, and so forth. This perspective aligns Gardner’s MI theory more closely with a talent theory, as opposed to conventional intelligence theories that view cognitive functions as broadly applicable, rather than tied to specific tasks or information. For instance, Sternberg (1996b) proposed analytic, creative, and practical intelligences, which are adaptively employed in situations that demand their particular strengths. A further note of caution is warranted. Although the terms “intelligence” and “intellect” are sometimes used interchangeably (e.g., “intellectual talent”), they

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signify distinct functional properties. Intelligence primarily concerns the adequacy of cognitive functions in effectively executing a given task, whether it involves complex mathematical operations or devising an on-the-spot strategy for a quarterback during a football game. Intelligence is thus implicated when a cognitively demanding task is skillfully executed. In contrast, intellect leans toward the epistemic, often manifesting itself as meta-level insights into the essence of a subject or the ability to make astute observations and generalizations. According to Hofstadter (1963), “anti-intellectualism” in American life is anti-intellectual, not anti-intelligence. The presence of this distinction is why Dai (2021) discerns between the intellectual domain, focusing on meaning-making and cognitive insight, and the technical domain, which is more instrumental, such as the precision required in crafting sharp yet non-brittle flakes. Talent  In colloquial language, the term talent is often used to describe individuals who demonstrate exceptional abilities or skills in performing specific types of tasks. Differing from the concept of being gifted, talent typically implies a distinct capacity, innate or cultivated, that is closely tied to a particular category of tasks, making it domain-specific. Unlike intelligence, which encompasses a range of cognitive functions that operate across various domains, “talent” often has an intuitive, embodied foundation specific to a task domain, not easily dissected through conscious analysis. This perspective aligns with the notion of an emergenic-epigenetic basis for talent, as introduced by Simonton (1999). To provide a more formal definition of talent, we propose three criteria as follows: (A) Excellence. In order to be identified as talented, a performance needs to be outstanding as compared to its age peers, in a developmentally appropriate manner. (B) Domain specificity. Talent is not generic but confined to certain types of tasks and certain realms of meaning, indicating the circumscribed nature of performance or competence. (C) Authenticity. Determination of the presence and degree of talent is based on authentic performance in authentic settings, therefore revealing its adaptive function and contextual nature, though the ingredients of a talent can be further analyzed through relevant task analysis. Defined as such, a talent is rarely a pre-ordained structure. The phenomenon of child prodigies in fields like the arts, mathematics, and chess (Feldman 1986) is a testament to the fact that these children had early exposure to an enriched environment, especially with respect to the domain of their talent (e.g., Mozart), albeit their distinct precocity. Instead of viewing talent solely as natural endowment, it can be understood as an acquired module, representing a form of high biological readiness toward realizing one’s cultural potential (Csikszentmihalyi and Robinson 1986). This perspective highlights the deep integration of talent within the educational and cultural provision. Certainly, it is reasonable to argue that some individuals may possess a greater “predisposition” for achieving high levels of talent in a given domain compared to their peers. Precocity  Precocity denotes the early development of certain attributes, whether cognitive, emotional, or motivational, in comparison to individuals of the same age.

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A child prodigy represents an extreme case of precocity. Traditionally, precocity was viewed as a result of early biological maturation, thereby being an inherent trait. However, research on child prodigies (e.g., Feldman 1986) and recent investigations into developmental precocity (e.g., early reading skills; Dai and Li 2023) suggest that certain aspects of precocity involve early education and the swift acquisition of knowledge and abilities. Thus, various forms of precocity can be influenced by early environmental provisions and experiences (Gottlieb 1998) and are not solely the product of innate, “natural” qualities. While the aforementioned concepts lay a foundational framework for comprehending the origins of talent potential, the subsequent concepts are indispensable for understanding the TD process and the desired developmental outcomes. We have chosen specific concepts for discussion.

2.1.2 Related Terminology of Talent Development Talent Development (TD)  In the realm of TD research, the term development carries a certain ambiguity. It can be defined in two distinct ways: individual development, which pertains to how a person develops a unique set of personal attributes—cognitive, emotional, or motivational (Snow 1992) through developmental and social interaction with an impinging environment; and human development, which implies the nurturing and enculturation of human capabilities and inclinations with systematic cultural and institutional support. Talent development can be viewed as either part of individual development or human development, depending on whether a personal or sociocultural perspective is adopted. Talent development is used in the educational world but often referred to in the business domain as talent management (Meyers et al. 2013). From the perspective of individual development, researchers on TD invariably trace its origins to epigenetic factors that are inherently unique to each individual. Conversely, when viewed through the lens of human development, TD incorporates a discernible cultural dimension, influenced by a myriad of social institutions, tools, and resources. It is almost a rule that TD, as an aspect of individual development, involves intricate interactions with the cultural milieu. Similarly, in the realm of human capital development or talent management, characterized by institutionalized TD, one cannot help but observe the resilience of individual development in terms of niche selection and self-initiative. Recognizing this dual nature of TD is of paramount importance in the field of TD research. Deliberate Practice  Deliberate practice, as articulated by Ericsson (2006), constitutes a focused, intensive, and goal-oriented approach to practice. It shares similarities with related concepts such as extended problem-solving and operating at the limits of one’s competence, as proposed by Bereiter and Scardamalia (1993). Deliberate practice is widely regarded as a necessary, albeit not necessarily sufficient,

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condition for cultivating high-level expertise, particularly in terms of ­technical proficiency. However, its potential to foster creative productivity remains a subject of debate. It is reasonable to assert that deliberate practice is indispensable in domains emphasizing craftsmanship and skilled performance. In other fields, though, it may not be the most fitting explanatory concept. For instance, in entrepreneurship, innovative vision and social leadership may hold greater importance, while in scholarly inquiry, intellectual curiosity and insightful thinking may take precedence. Recent research underscores the significant role of a multitude of personal and social factors beyond deliberate practice (Hambrick et al. 2018). Progressive Deepening (PD)  Progressive deepening (PD) refers to the iterative nature of cognition, which follows its own path toward achieving a deeper understanding or higher proficiency, as articulated by Newell (1990); it may be a fundamental cognitive characteristic of TD. An illustrative instance of PD is the cognitive tendency to initially focus on surface features of a task but gradually evolve to grasp the profound logic of procedures or the underlying structure of a particular domain, as discussed by Gee (2007). The concept of PD also encompasses stages and phase transitions in the process of mastering complex ways of perceiving, representing, and interacting with the world. This phenomenon is observed across various domains, whether it is in the context of professional gambling (Ceci and Liker 1986), a game of chess against an equally skilled opponent (Saariluoma 1992), or the development of a scientific theory (Gruber 1981). Psychosocial Skills/Characteristics  The concept of psychosocial skills and characteristics encompasses a diverse range of personal attributes and cultivated characteristics that serve important adaptive functions (e.g., maintaining a focus toward goal achievement and combating frustration and setbacks). These attributes can function as a protective shield during challenging moments and adversities. Examples of these attributes include self-efficacy, optimism, mental toughness, and a sense of destiny. They play a pivotal role in regulating one’s focused pursuits of excellence and personal ambitions, as highlighted in studies by Mischel et al. (1989) and Olszewski-Kubilius et al. (2019). Grit, which denotes an individual’s ability or propensity to pursue a personal interest with unwavering determination and perseverance, as elucidated by Duckworth (2016), aligns well with this category of psychosocial skills and attributes. Psychosocial skills and characteristics are typically regarded as contextually and developmentally influenced, rather than static traits depleted of contextual influences. They can be seen as characteristic adaptations specific to particular life contexts, as discussed by McAdams and Pals (2006; for applications of the concept of characteristic adaptation to TD, refer to Dai, 2017, 2021, 2024). Expertise  Expertise represents a state of elevated proficiency within a specific domain, typically attained through extensive training and practice, often quantified

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as a significant duration of deliberate practice, such as 10 years or 10,000  hours (Ericsson and Williams 2007). It is important to differentiate between two aspects of expertise: socially recognized expertise, where individuals command authority largely based on their social acclaim and status, and expertise demonstrated through reproducible superior performance (Ericsson et al. 2007), often evaluated in controlled settings. However, it is worth noting that in certain domains, particularly those involving creative and inventive endeavors, expertise cannot be solely determined by the latter criterion. Additionally, some social accolades, like Fields or Nobel awards, hold consensual validity as they involve peer nominations and consensus. The term expertise finds its most applicable context in domains that emphasize practical or technical proficiency, such as executing an ice-skating routine, playing golf, or performing complex surgeries. All these skills belong to performance domains, not production domains. It is essential to recognize that accomplishments in many production domains may not necessarily attain high levels of proficiency implied by the notion of expertise. In fact, an argument can be made that an excess of expertise can lead to cognitive rigidity, fostering entrenched perspectives that may hinder creative insights and alternative problem-solving approaches, as pointed out by Sternberg (1996a). Creative Productivity  Creative productivity stands as a measure of an individual’s output in terms of both quantity and quality when it comes to creative endeavors, encompassing meaningful ideas and useful products that are novel or innovative. These creative outcomes can manifest in various forms, including publications, patents, and artistic works or performances. Drawing upon Tannenbaum’s (1997) distinction between performers and producers, expertise is most apt for gauging the level of excellence attained by performers, particularly in performance-oriented domains. On the other hand, creative productivity is better suited for assessing the level of excellence achieved by producers, particularly in production-centric domains, with a caveat that performance can also carry creative flavor (e.g., a creative move of a golfer or a distinct expressiveness of a pianist), and superior production can also show “reproducibility” or “replicability.” Creative productivity exhibits diverse manifestations, spanning across domains such as scientific and artistic, humanities and technology, and entrepreneurship and craftsmanship. High levels of excellence in creative productivity achieved by any individual are often confined to a single domain, likely because of prolonged TD and, indeed, many years of deliberate practice and extended problem-solving involved. Here is where high-level expertise and creative productivity share a family resemblance in human excellence. It is crucial to differentiate creative productivity from creative potential, which can be discerned through a combination of abilities and personality traits conducive to the development of creative productivity, as outlined by Runco (2021). Innovation  At times, the terms “creativity” and “innovation” are used interchangeably, yet they carry subtle distinctions. The term innovation is often reserved for a

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type of creativity that directly results in instrumental and organizational changes within society. This might involve the creation of novel concepts or products that swiftly become new norms or standards. Some scholars differentiate between “creativity” and “innovation” as two distinct forms of creative expression, with the former leaning more toward intellectual aspects and the latter emphasizing technical dimensions, as discussed by VanTassel-Baska (2016). However, a more prevalent perspective treats innovation as a specialized category within the broader realm of creativity, placing particular emphasis on diverging from conventions to generate innovations that wield “direct social impact and practical consequences,” as pointed out by Dai (2013, p. 52). More recently, Dai (2024) identifies five forms of innovation from the most concrete to the most abstract: (a) technical innovation, (b) design innovation, (c) knowledge innovation, (d) theoretical innovation, and (e) paradigmatic innovation. In this view, innovation is considered “the cornerstone of economic prosperity, scientific discovery, technological invention, and cultural vibrancy,” as articulated by Shavinina (2013, p. xxvi). This perspective has even led to the emergence of distinct educational imperatives like STEM or STEAM education. Polymathy  Polymathy refers to the unique phenomenon of individuals (polymaths) who possess extensive erudition, displaying profound knowledge and often a multitude of talents. Leonardo da Vinci stands out as a prominent example of a polymath, notably characterized by his exceptional intellectual prowess, aesthetic sensitivity, and technical acumen, as well as an unparalleled inclination for observation, exploration, and invention. Talent development (TD) in the direction of polymathy diverges from the conventional, highly specialized paths of study and training that lead individuals to become specialists, the typical TD route toward achieving high-level expertise. Polymaths are usually highly autodidactic (self-taught), leaning naturally toward a liberal education, emphasizing a broad and diverse range of knowledge and skills. This approach has been advocated as a potentially more effective way to nurture creative scientists, as suggested by Root-Bernstein (2009). However, it is worth noting that polymathy may also involve distinct personality traits. The phenomenon of polymathy poses an intriguing challenge to the conventional expert performance model of human excellence. Excellence  In everyday language, “excellence” is a term used to describe exceptional achievements in various culturally valued domains. It is a concept rooted in comparison to established norms, signifying the remarkable success achieved by a select few who undertake highly challenging tasks. Notably, excellence is always earned, in contrast to concepts like “giftedness” or “talent,” which may imply a natural, inherent endowment. Excellence is relative, contingent on the specific standards within a particular context. For instance, athletes participating in the Special Olympics earn medals and honors by triumphing over their challenging circumstances through sheer

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perseverance, optimism, and unwavering determination. Their accomplishments are a testament to their ability to defy the odds and become accomplished individuals, even though their achievements may not parallel those of conventional athletes without serious handicapping conditions. This understanding of excellence distinguishes itself from a capacity or genius-­ based interpretation of human accomplishments. It is, indeed, unfair to draw comparisons between athletes in the Special Olympics and elite individuals like Usain Bolt or even between the running capabilities of humans and deer. For this reason, talent development (TD) research should extend its scope to encompass individuals with unique talents who contend with various human disorders yet achieve excellence in their distinctive ways. Their TD journeys, including their deficiencies, also offer valuable insights into the talent potential of child prodigies and individuals considered “normal,” as to what it takes to achieve excellence at the caliber of a world-class master or creator, as discussed by Feldman (2003) and Miller (2005). Eminence  The term “eminence” signifies being widely recognized, distinguished, and celebrated. Eminence is typically achieved through the quality of one’s contributions or the social accolades and recognition received. Consequently, eminence holds cultural distinction, although it is worth noting that recognition for exceptional achievements can vary across different domains. For instance, while an anonymous, excellent boots-maker or chef may deserve some degree of social recognition, the social impact of their respective crafts is often not comparable to the significance of excellence in scientific discovery or technical invention, which often make substantial contributions to civilization and improved human conditions. Although eminence features prominently in the work of many TD researchers (Subotnik et al. 2011), there exist reservations about employing eminence as a primary goal or criterion of TD success. Similar to the term “greatness,” eminence inherently carries a degree of subjectivity and social prestige that may or may not be deserved, and consequently, it is not embraced by researchers focused on expertise as a definitive measure of excellence, as articulated by Ericsson and Williams (2007). Additionally, eminence should not be equated with high intelligence or effectiveness, as highlighted by MacKinnon (1978).

2.2 A Brief History of Research on Talent Development (TD) The roots of rigorous research into talent and TD can be traced back to early historical and biographical studies focused on individuals who have left indelible marks on civilization, as exemplified by Anne Roe’s (1953) work comparing eminent psychologists and anthropologists, and biological and physical scientists. These roots can be further linked to the pioneering efforts of Francis Galton (1869), who conducted extensive research tracing the family histories of gifted individuals. Galton was the first person in history to make an empirically based claim that genius is heritable.

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A seminal moment in the research tradition of TD came with Lewis Terman’s (1925) longitudinal study of high IQ adolescents. This groundbreaking work introduced a model of research that prospectively predicts long-term developmental outcomes, encompassing accomplishments across various talent domains. While Terman’s study primarily focused on the intellectually gifted, its longitudinal approach, examining ultimate achievements, set the standard for research capable of unveiling long-term talent trajectories. It is noteworthy that Terman also conducted follow-up studies aimed at identifying the factors that facilitate or hinder long-term talent development (Terman and Oden 1959). The earliest systematic research into TD, which began around the mid-twentieth century, aimed to uncover the defining personal characteristics and significant life events of eminent individuals, as demonstrated by Goertzel and Goertzel (1962; revised in 2004). It also sought to differentiate the most accomplished achievers from their less distinguished peers, an endeavor undertaken by MacKinnon (1978). Furthermore, this research sought to distinguish talents within specific domains from talents in others, as illustrated by Roe (1953). The foundation of this body of research was primarily biographical in nature. One notable exception to this trend was the Talented Youth Project, initiated in 1953 and spanning over a decade. It was a collaborative effort involving several professors at Columbia University, including Passow, Tannenbaum, and Goldberg. This initiative integrated empirical research components into TD and encompassed various research and development efforts aimed at nurturing talented individuals (see Borland 2014). A pivotal moment in the evolution of gifted education occurred when Paul Witty (1958) introduced a new definition of giftedness that was both pluralistic and developmental in nature. This perspective, echoed by Marland (1972), marked a significant “self-correction” in the historical discourse on giftedness and TD. Witty argued that the scholarly discussion had previously overemphasized capacity while underestimating the role of motivation (see Jolly and Robin (2014)). Reintroducing motivation into the framework placed increased emphasis on personal agency and initiative, transcending the limited capacity view of giftedness, as expounded by Gagné (1985, 2020, see also Ericsson et al. 2007). The period spanning from the 1980s to the 1990s marked a significant upsurge in both theoretical and practical interest in TD, with TD models and research gaining prominence. On the theoretical front, pioneering studies like Simonton’s histriometric investigations into prominent historical figures, including great artists, scientists, and political leaders (Simonton 1988, 1994), and Gruber’s (1986) comprehensive biographical analyses of Darwin and Piaget, catalyzed a fresh research agenda. Simonton’s work notably charted numerous vital individual and developmental milestones associated with talent accomplishments, while Gruber’s research provided in-depth insights into the evolution of epoch-making creative ideas, such as the theory of evolution, throughout extended intellectual journeys spanning decades. Feldman’s (1986) case study (with Goldsmith) examining child prodigies initiated a novel research tradition that views TD as a form of non-universal development, often outside the purview of mainstream developmental research. During this

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period, researchers delved into the neural-physiological, cognitive, and developmental foundations of giftedness and talent, as exemplified by the work of Obler and Fein (1988). Simultaneously, a cohort of cognitive and developmental researchers shifted their focus toward the phenomenon of high-level expertise. Cognitive psychologists concentrated primarily on unraveling the cognitive processes underpinning exceptional performance in diverse, demanding problem-solving tasks, as exemplified by the contributions of John Anderson (1987), Chase and Simon (1973), and Ericsson & Simon (1993). Concurrently, developmental psychologists began scrutinizing the developmental underpinnings of expertise. A highly influential study by Ceci and Liker in 1986, examining professional gambling, emerged as one of the earliest empirical challenges to the prevailing notion that general intelligence, as measured by IQ, was a requisite for attaining high-level expertise. Another pivotal study, conducted by Ericsson et  al. (1993), explored multiple talent domains and made a compelling empirical case that deliberate practice, rather than innate talent, constituted the path to achieving high-level expertise across diverse fields, including the arts, sciences, games, and various other domains. A landmark study in the 1980s was conducted by Benjamin Bloom and his colleagues, involving a large-scale retrospective, phenomenological examination of accomplished individuals in sports, arts, mathematics, and science (Bloom 1985). Notably, this study represented a distinct departure from the prevailing trait-based perspective on talent and achievement, which had persisted since Galton’s era in the twentieth century. Instead, it embraced a process-oriented model of TD, offering comprehensive insights into the role of pivotal life events and developmental transformations. These insights encompassed critical junctures that could make or break an individual’s trajectory. One of the study’s significant strengths lay in its selection of a sample comprising talented young adults who were still in the process of TD. This approach effectively bridged the research gap, transitioning from the demonstration of talent potential during early schooling years to the realization of eminent accomplishments in adulthood, as stressed by Siegler and Kotovsky (1986) and Mayer (2005). Furthermore, a noteworthy milestone emerged with the study of Csikszentmihalyi et al. (1993), which delved into the lived experiences of talented teenagers in the realms of arts and sciences. This research marked a pivotal shift from the static aspect of talent to the dynamic facet of talent development. This dynamic perspective has been a subject of discussion, as highlighted by Kanevsky (2020), within the context of the static-dynamic distinction. In the early years of the twenty-first century, there has been a notable proliferation of TD research from various disciplinary perspectives. A comprehensive exploration of the current trends and emerging issues in this field is reserved for subsequent chapters. However, it is worth mentioning that certain traditional lines of research have persisted. One noteworthy example is the extensive longitudinal follow-up studies conducted by Lubinski, Benbow, and their colleagues. These studies have tracked

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multiple cohorts of talented teenagers who were identified through “above-level testing,” typically employing the SAT. This ambitious SMPY longitudinal project was initiated in the 1970s and has spanned over five decades. The findings are outlined in Lubinski and Benbow (2006, 2021). Simultaneously, novel directions in research have injected a breath of fresh air into the domain of TD. For instance, Glaveanu et al. (2013) studied the exploration of five distinct creative domains, encompassing art, design, science, scriptwriting, and music. They adopted an action theory perspective on creativity, revealing both commonalities and differences in domain-specific patterns of action and interaction across these creative domains. Their findings underscored the notion that merely investigating cognitive processes within the mind of the agent or actor is insufficient. Instead, insights into the essence of creativity within these domains emerge from the examination of the distinct patterns of interaction between the actor and the task environment. This perspective aligns with the sentiments expressed by Csikszentmihalyi (1996) and echoed by others who consider exceptional performance as situated in context (Barab and Plucker 2002; Plucker & Barab, 2005). They emphasize that comprehending how individuals interact with the social-cultural practices of a given domain can be more crucial than solely scrutinizing the individual, recognizing that TD extends beyond the exercise of mental or physical prowess.

2.3 Theoretical Models of Talent Development (TD) Abraham Tannenbaum (1983, 1986, 1997) emerges as a visionary figure in this field, having established a set of principles within his psychosocial theory of TD. These principles have served as the bedrock for shaping the TD research agenda. Foremost among his contributions was his delineation of the inherently social and cultural character of TD, as outlined below: • Talent potential must align with the spirit of the age to gain recognition; thus, grasping the role of social and historical contexts is vital in comprehending particular TD trajectories and achievements. • Societies construct hierarchical structures of prestige that allocate differing degrees of cultural prominence to diverse domains and expressions of human accomplishment. Consequently, certain talents assume a more conspicuous and distinct role within their respective cultures. • Talents can be categorized into four distinct groups based on their social relevance and cultural significance: scarcity talent, surplus talent, quota talent, and anomalous talent (see Chap. 4 for more details). • Distinction Between Childhood and Adulthood Excellence: Fully developed talents typically emerge in adulthood, evolving through increasing differentiation and extended development. In contrast, children exhibit giftedness when they demonstrate the potential to become critically acclaimed performers or e­ xemplary

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creators in fields that enrich humanity’s moral, physical, emotional, social, intellectual, or aesthetic facets (Tannenbaum 1986, p. 33). • Components and Constituents of Talent: Talent potential is influenced by five interrelated factors: (a) general ability, (b) specialized abilities, (c) non-­ intellectual facilitators, (d) environmental factors, and (e) chance occurrences. While psychological testing, as per psychometric traditions, plays a pivotal role in assessing certain aspects like IQ, Tannenbaum’s psychosocial model highlights the significance of social conditions and environmental influences. Distinguishing between intellectual and non-intellectual factors in practice can be challenging, as they are intricately intertwined (refer to Kanevsky 2020, for dynamic aspects of these five factors). To conclude, Tannenbaum (1983) meticulously charted a comprehensive landscape for TD by introducing crucial organizational principles (see also Chap. 4 for his framework on a typology of talent, based on mode, content, and style). Tannenbaum’s work effectively bridged the gap between the conventional IQ-centric perspective and the TD-centered exploration of human potential and excellence. Moreover, it laid a sturdy foundation for subsequent research in the field of TD. Models of TD developed since Tannenbaum (1983) can broadly be categorized based on whether they (a) delineate talent as a collection of constituent elements, (b) emphasize developmental processes and transformations, or (c) view TD as a developmental system that integrates interactive components and developmental progressions.

2.3.1 Component Models of Talent Development Françoys Gagné’s Differentiated Model of Gifted and Talented (DMGT; 1985, 2005, 2020) shares a kinship with Tannenbaum’s five-component model of TD. The primary underpinnings of this model encompass the following: A. Discriminating between giftedness, categorized into four distinct types, as an innate endowment, and talent as a methodically cultivated skill or skillset. B. A quantifiable framework that classifies five levels of giftedness predicated on their estimated prevalence within a population. C. The inclusion of intrapersonal and environmental catalysts, alongside chance elements, which facilitate the progression from giftedness to talent. The DMGT model has gained global acclaim owing to its conceptual lucidity and the ease with which it encapsulates the primary facets of TD. However, its drawback lies in its establishment of rigid a priori assumptions, some of which prove challenging to empirically verify. For instance, it posits giftedness as an obligatory precursor to TD, and also suggests that giftedness and intrapersonal catalysts can be distinctly separated, a viewpoint that has drawn criticism, as highlighted in Dai (2004).

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Piirto’s (1994) Pyramid Model of Talent Development is also a component model, which maps out several components through development that facilitate talent development, culminating as a “calling.” Two features distinguish this mode from Gagné’s: an emphasis on personality, rather than IQ, and an emphasis on peaks of TD as creative expressions. While Gagné has heavily relied on testing data to support his model, Piirto relies on qualitative case studies of many eminent creators to make her case. Lubinski and Benbow, in their extensive longitudinal research spanning from 2006 to 2021, have de facto crafted a triadic model of TD. Their research is grounded in the analysis of multiple cohorts of teenagers who were identified through above-­ level testing, initially utilizing SAT-Math and SAT-Verbal scores, and later adding a spatial ability component among their predictive measures. This model aligns with the component-based frameworks discussed earlier, but distinguishes itself through a comprehensive and empirically substantiated theoretical exposition. To elaborate on the specifics: A. The composite index of verbal, mathematical, and spatial abilities measured at the age of 13 is correlated with frequencies of career paths and domain achievements in adulthood, even within a sample of a highly talented group. Domains that attracted the highest ability group include mathematics, computer science, physics, and engineering (Lubinski 2010). B. The unique combination of these three abilities within an individual, along with their respective strengths and weaknesses, can forecast vocational interests and career choices. For instance, a high verbal/low math combination tends to be linked with humanities, while a low verbal/high math combination aligns with physical sciences. Conversely, individuals with high mathematical and spatial abilities often gravitate toward engineering fields (Park et al. 2007). C. When accounting for the influence of years of education, including both college and graduate degrees, the ability indices continue to predict career achievements, such as patents and scientific publications (Park et al. 2008). Moreover, employing a stringent criterion of a 1 in 10,000 cut-off score to identify the top ability group within a talented cohort consistently reveals this group as the highest achievers, reinforcing the pivotal role of high ability in long-term talent development outcomes (Lubinski et al. 2001). D. Furthermore, diverse patterns of vocational interests and individual value orientations serve as additional factors contributing variance to long-term developmental outcomes. These include trajectories, vocational choices, and career achievements (Wai et al. 2005). E. Finally, it is essential to underscore that educational experiences both within and outside formal schooling settings exert indispensable influences on individual development and long-term accomplishments (Wai et al. 2010). The longitudinal research conducted by Lubinski, Benbow, and their collaborators offers robust empirical backing for the credibility and significance of ability factors in talent development. Moreover, this evidence highlights the existence of distinct talent trajectories and pathways influenced by both intellectual and

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“non-intellective” elements, which are probably shaped by environmental opportunities and experiences. Nonetheless, it is important to note that the model has limitations when it comes to elucidating the intricate developmental processes. This constraint arises from the design’s focus on prospective long-term prediction. To overcome this limitation, process-oriented models of talent development step in to provide a more comprehensive understanding.

2.3.2 Developmental Process Models of Talent Development Simonton’s (1999) emergenic-epigenetic model of talent represented a rare attempt to employ hypothetical-deductive reasoning in constructing a TD model that acknowledges the intricacies of development. While most traditional models depict “natural talent” (exemplified by historical figures like Mozart or Van Gogh) as genetically predetermined, the emergenic-epigenesis model posits talent as an emergent property arising from the combination of essential genetically based components (emergenic) maturing at the right time (epigenetic) in response to the proper exposure or experiences. For instance, the manifestation of musical talent might involve genetic components that support the perception of pitch, rhythmic patterns, and melody processing. However, the development of these phenotypes, which are underpinned by genes, follows its own developmental schedule. The absence of one component can nullify the effectiveness of others, emphasizing that all components need to emerge epigenetically with the right timing to enable the emergence of musical talent. Presumably, all these music-processing components take shape through gene-environment interaction. Simonton’s emergenic-epigenetic model demonstrates that even an early blossoming talent, which may seem like a pre-­ existing genetic condition or natural endowment, involves probabilistic epigenesis—a developmental process that requires critical input and stimulation from the environment (see also Simonton, 2018). Renzulli’s (1978, 2005) three-ring theory of giftedness implies developmental processes in a different manner. The emergent properties identified by the theory are not performance components of an emergent talent, as in Simonton (1999), but rather task motivation (task commitment) and the sparks of creative ideation (often referred to as little-c) observed in situ during authentic problem-solving tasks. Critics of the three-ring theory have sometimes dismissed its contextual and developmental aspects as irrelevant to giftedness, which is assumed to be an innate, inherent quality (see Renzulli 2005). However, this overlooks the fact that the three-­ ring theory is essentially a dynamic process model of TD masquerading as a trait model of giftedness (see Dai and Renzulli 2008 for a more detailed explanation). Bloom (1985) and his colleagues (Sosniak, 2006) devised a developmental process model of talent development through retrospective interviews with exceptionally talented young adults across various fields (mathematics, sports, music, neuroscience, etc.). Their model posits three significant claims regarding talent

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development. First, talent development unfolds in distinct stages, with at least three stages discernible in a progressive sequence: (1) a playful stage characterized by explorative engagement in talent domains; (2) an intermediate stage marked by the development of technical proficiency, and (3) an advanced stage in which individuals move to the frontiers of knowledge and technology, or confront the highest professional standards of performance and productivity. Second, advancing to more advanced stages entails more than just skill enhancement; it involves qualitative shifts in the significance of the relationship built between individuals and their chosen domain. Finally, these remarkably talented young adults did not display prodigious abilities in childhood, nor were they identified as gifted during their school years. This challenges the assumption that great achievers always manifest their gifts and talents during their formative years. Subotnik, Olszewski-Kubilius, and Worrell (2011) constructed a comprehensive megamodel of talent development by synthesizing insights from various preceding models, including Tannenbaum (1983) and Bloom (1985). This model is characterized by the following: (a) a heightened focus on TD in various domains, classified as sports, arts, academics, and professional domains (Subotnik et al. 2019), taking into account the distinct affordances and developmental constraints each domain imposes on individuals at different stages of TD; (b) a delineation of the timing of onset and peak achievements in diverse domains; and (c) a special emphasis on psychosocial skills and characteristics as essential elements in talent development. The mega model should be seen within the context of paradigm shifts in the field of gifted education. Ullén et al. (2016) crafted a multi-factorial gene-environment interaction model (MGIM) of expertise development that incorporates elements such as deliberate practice (Ericsson 2006), psychometric assessments of abilities, personality traits, and motivation. In this model, TD depends on complex gene-environment interactions that underlie numerous relevant psychological developments. The model suggests that while deliberate practice may act as a mediator of expertise development, many ability, personality, and motivational factors either directly influence the attainment of expertise or indirectly facilitate deliberate practice. However, the model does not provide a detailed specification of how gene-environment interaction operates to generate these processes, echoing the need for greater clarity in this area (cf. Gottlieb 1998).

2.3.3 Developmental Systems Models of Talent Development Developmental systems models transcend the mere identification of components and the explication of developmental processes from initial states to more advanced ones. They center on understanding how an individual progresses to a higher level of organized complexity every step of the way. A systems perspective means that the theoretical focus is on the operation of the entire system, comprising numerous interacting components that function and operate in real time. Importantly, each

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component can dynamically change, and their functioning is contingent on the state of other components. In this sense, a systems approach prioritizes interactions over individual components (Hilpert and Marchand 2018). Csikszentmihalyi’s (1996; Csikszentmihalyi and Robinson 1986) sociocultural model of TD can be viewed as an early attempt to grasp the systemic properties of TD. First, the model firmly defines talent as sociocultural, dispelling the notion of some mysterious innate structure or entity waiting to be discovered. It asserts that many manifestations of talent, in various forms and manners, reflect real-time adaptation to cultural challenges and are influenced by cultural provisions and developmental opportunities. Consequently, the model emphasizes the central role of developmental timing in talent development. According to this model, at least four aspects of developmental conditions must be considered simultaneously: (a) cognitive and intellectual development, akin to Piaget or Neo-Piagetian traditions (Porath 2006); (b) personal development in terms of emotional bonds, confidence, and identity; (c) the nature of the talent domain(s) involved and the experiences within those domains; and (d) the field or social organization of a domain that offers experiences and training in relevant talent domains as well as setting standards and maintaining rigor by gatekeepers. Optimal talent development occurs when these four developmental conditions harmoniously interact to promote an individual’s realization of their cultural potential in mastering a domain (i.e., talent development). This view contrasts with the emergenic-epigenetic model of talent (Simonton 1999), which regards talent manifestations as a developmental (epigenetic) unfolding of innate components with certain environmental exposures and experiences. Gruber’s (1986) Evolving Systems Approach (ESA), although a methodology rather than a theoretical model, aligns with Csikszentmihalyi’s emphasis on TD as a process characterized by increasing organized complexity. This approach explores how a creative idea evolves, interacts with other ideas, and ultimately transforms into a well-developed argument, as exemplified by Charles Darwin’s intellectual journey (Gruber and Wallace 2001). Gruber delves into the historical documents, such as Darwin’s diary, to examine how the four developmental conditions identified by Csikszentmihalyi coalesce in Darwin’s case. Ceci et al. (2016) have recently developed a bioecological model of TD, drawing on their empirical research with professional gamblers in the field of horse racing, as well as their theoretical work with Bronfenbrenner on nature-nurture interactions (Bronfenbrenner and Ceci 1994). This model hinges on three principles: (a) the principle of proximal processes, which asserts that talent development involves an enduring process of progressively more complex reciprocal interactions between a developing person (with distinct personal characteristics) and the immediate environment, which comprises persons, objects, and symbols; (b) the principle of emergent organization, which posits that the specific form, content, and direction of TD result from the interplay between the developing person and the impacting environment (whether immediate or symbolically mediated); and (c) the principle of efficacy, which holds that proximal processes activate the genetic potential for effective cognitive and social development relevant to TD. The efficacy of these processes, however, systematically varies in the emergent organization of the three factors

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outlined in the second principle. In essence, Ceci et al.’s model outlines the core tenets of developmental complexity in individual development, generally applicable but particularly pertinent to TD, and not reducible to lower-level constituent components. Dai’s (2017, 2021) Evolving Complexity Theory (ECT) of TD draws inspiration from Feldman’s (2003) proposal of non-universal development as the key to understanding developmental processes in TD, as well as from Bronfenbrenner and Ceci’s (1994) formulation of proximal processes as foundational to TD theory and research. In the main, ECT operates on three fundamental assumptions: (a) the human organism is an open, adaptive, developmental agent engaged in increasingly complex person-environment interactions, undergoing ever complex self-organization (hence, ECT); (b) TD initially exhibits spontaneity and becomes progressively more purposive over time; and (c) TD initially takes the form of characteristic adaptation (CA) and, as standards rise, transitions to the mode of maximal adaptation (MA). Nevertheless, CA continues to function in niche-picking even at advanced stages of talent development. ECT represents an endeavor to adopt a dynamic systems approach (Lewis 2000) in crafting a comprehensive account of TD. This dynamic systems perspective revolves around (a) the concept of self-organization, (b) the emergence of novel properties and organizational principles, and (c) the progression of complexity at new tiers of organization. ECT initiates with a conceptual framework that outlines three primary vectors: functional, temporal, and developmental (as depicted in Fig. 2.1). Subsequently, it delineates what emerges (such as new competence and self-direction properties), the mechanisms underlying emergence (including driving and regulatory processes, addressing how it develops), the timing of emergence (shaped by temporal structures and interaction patterns, addressing when it occurs),

Fig. 2.1  A conceptual foundation for ECT (Dai 2021)

2.4  Summary: Toward More Systematic Approach to Talent Development

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the contexts where it emerges (under specific social and contextual conditions, addressing where it takes place), and ultimately, why TD and excellence is essential for the person as well as for the culture. In the course of this journey, ECT addresses the foundations of TD, encompassing developmental diversity, specificity, and complexity. In summary, developmental systems models are not satisfied with merely identifying essential components, endogenous or exogenous, nor with depicting developmental processes locally without a broader picture of evolving developmental systems, of which they are an integral part. They expect a new level of conceptual clarity and empirical grounding in terms of developmental timing, specificity, and complexity.

2.4 Summary: Toward More Systematic Approach to Talent Development In the earlier days of talent research, there was a tendency to primarily view talent as a latent capacity waiting to be unlocked by environmental factors, or at most, as something that required certain catalysts to facilitate its emergence. This perspective often aligned with a theory known as reaction range, which leaned toward an explanation of outstanding achievements based on “natural talent.” However, contemporary researchers have adopted a more nuanced view. The contrast between these two viewpoints is not solely about nature versus nurture biases; instead, it emphasizes the significance of development as a central aspect of talent manifestation, as highlighted by developmental and systems models (Feldman 2003). In the following section, we outline some general theoretical stances and sentiments that underlie current research efforts, along with a call for a more systematic approach to talent development. There is a broad consensus on talent development as a legitimate subject of study within developmental science, which is reflected in the following arguments: • Human talent is founded on the potential for excellence in culturally constructed domains of human endeavor, whether it manifests as superior performance in various fields or as creative contributions in technical, artistic, and intellectual pursuits (Gagné 2020; Tannenbaum 1983, 1997). • While talent may have genetic roots, it is not biologically predetermined or a pre-ordained structure (i.e., innate and unchanging). Instead, it is epigenetic and emergent, evolving through interactions between individuals and their environments (Ceci et al. 2016; Csikszentmihalyi and Robinson 1986; Simonton 1999). • Developmental trajectories and pathways leading to excellence in specific domains are discernible. These trajectories involve increasing differentiation and integration of one’s potential (Dai 2010; Lubinski and Benbow 2021; Simonton 1999) and become more articulated and purposeful over time (Dai 2021). These talent trajectories and pathways have identifiable developmental roots, including

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aptitudes and dispositions (Dai 2010), as well as factors that facilitate or hinder talent development at various critical developmental junctures (Subotnik et al. 2011). TD encompasses the integration of personality (values and goals) and intelligence (cognitive and metacognitive competencies and decision-making skills) as individuals adapt to and interact with their surrounding environments over an extended period. Personality provides values and personal goals, while intelligence, broadly defined, provides the tools (Ford 1994). This integration extends beyond merely identifying traits and aims to capture intelligence and personality in action within the context of person-environment interactions (Snow 1995). The process of TD across various human endeavors often involves a complex interplay between social-cultural influences and institutional support on the one hand and individual psychosocial development on the other hand (Subotnik et al. 2011). This is exemplified in cases such as early theology scholars in seminaries or musicians in modern music conservatories. Even individuals who appear to be self-made talents, like Benjamin Franklin or Elon Musk, have benefited from education and have made their innovations through interactions with cultural opportunities and resources. Talent and talent development encompass a wide array of phenomena, each with its own origins, trajectories, and pathways. This developmental diversity is likely a joint function of developmental variations and cultural evolution (Dai 2024). Consequently, TD phenomena exhibit complexities and uncertainties, including equifinality (multiple pathways leading to the same outcome) and multifinality (outcomes subject to changes based on life events and conditions). Chance factors play a significant role in TD (Gagné 2020; Tannenbaum 1983) and contribute to the probabilistic nature of TD. This encompasses epigenetic processes and random encounters in personal experiences, highlighting the inherent developmental indeterminacy in TD. However, this does not preclude the identification of regularities in the timing of onset and peak experiences in certain domains and their associated developmental and sociocultural conditions (Csikszentmihalyi and Robinson 1986). Proximal indicators, markers, and milestone events can be identified to predict short-term shifts, phase transitions, and long-term outcomes (Feist 2006; Lubinski and Benbow 2021).

In conclusion, despite notable advancements, research efforts related to TD have been somewhat sporadic and insufficiently coordinated. The adoption of the developmental science framework (Cairns et al. 1996; Bronfenbrenner and Evan 2000) represents a crucial step in TD research. This framework legitimizes the study of exceptional human development and its contributions as essential for understanding human excellence, without which there would be no modern civilization. It also suggests ways in which talent can be nurtured for a productive, fulfilling life and for the betterment of society, which is the telos in the Aristotle’s sense (Dai and Sternberg 2021).

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Dai, D. Y. (2024). The nature and nurture of talent: A new foundation for education and optimal human development. Cambridge University Press. Dai, D. Y., & Li, X. (2023). A multi-case study of accelerated trajectories of science talent development: Matthew effects re-examined. Gifted Education International, 39(2), 148–167. Dai, D. Y., & Renzulli, J. S. (2008). Snowflakes, living systems, and the mystery of giftedness. Gifted Child Quarterly, 52, 114–130. Dai, D. Y., & Sternberg, R. J. (2021). Introduction: Historical and contemporary perspectives on human potential. In D. Y. Dai, & R. J. Sternberg (Eds.), Scientific inquiry into human potential: Historical and contemporary perspectives across disciplines (pp. xvi–xxviii). Routledge. Duckworth, A. (2016). Grit: The power of passion and perseverance. Scribner. Ericsson, K. A. (2006). The influence of experience and deliberate practice on the development of superior expert performance. In K. A. Ericsson, N. Charness, P. J. Feltovich & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp.  683–703). Cambridge University Press. Ericsson, K. A., Krampe, R. T., & Tesch-Romer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363–406. Ericsson, K.  A., Nandagopal, K., & Roring, R.  W. (2007). Giftedness and evidence for reproducibly superior performance: An account based on the expert-performance framework. High Ability Studies, 18, 3–55. Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (Revised Ed.). Bradford Books. Ericsson, K. A., & Williams, A. M. (2007). Capturing naturally occurring superior performance in the laboratory: Translational research on expert performance. Journal of Experimental Psychology: Applied, 13, 115–123. Feist, G. J. (2006). The development of scientific talent in Westinghouse Finalists and members of the National Academy of Sciences. Journal of Adult Development, 13, 23–35. Feldman, D. H. (1986). Nature’s gambit: Child prodigies and the development of human potential. Basic Books. Feldman, D. H. (2003). A developmental, evolutionary perspective on giftedness. In J. H. Borland (Ed.), Rethinking gifted education (pp. 9–33). Teachers College, Columbia University. Ford, M. E. (1994). A living systems approach to the integration of personality and intelligence. Personality and Intelligence, 188–217. Gagné, F. (1985). Gifted and talent: Reexamining a reexamination of the definitions. Gifted Child Quarterly, 29, 103–112. Gagné, F. (2005). From gifts to talents: The DMGT as a developmental model. In R. J. Sternberg & J.  E. Davidson (Eds.), Conceptions of giftedness (2 ed., pp.  98–119). Cambridge University Press. Gagné, F. (2020). Differentiating giftedness from talent: The DMGT perspective on talent development. Routledge. Galton, F. (1869). Hereditary genius: An inquiry into its laws and consequences. Macmillan. Gardner, H. (1983). Frames of mind. Basic Books. Gee, J.  P. (2007). What video games have to teach us about learning and literacy. Palgrave/ Mamillan. Glaveanu, V., Lubart, T., Bonnardel, N., Botella, M., De Biaisi, P. M., Desainte-Catherine, M., ... & Zenasni, F. (2013). Creativity as action: Findings from five creative domains. Frontiers in Psychology, 176, 1–14. Goertzel, V., & Goertzel, T. G. (2004). Cradle of eminence (2 ed.). Great Potential Press. Gottlieb, G. (1998). Normally occurring environmental and behavioral influences on gene activity: From central dogma to probabilistic epigenesis. Psychological Review, 105, 792–802. Gruber, H.  E. (1981). Darwin on man: A psychological study of scientific creativity (Rev. ed.).University of Chicago Press. Gruber, H. E. (1986). The self-construction of the extraordinary. In R. J. Sternberg & J. E. Davidson (Eds.), Conceptions of giftedness (pp. 247–263). Cambridge University Press.

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Chapter 3

Conceptual Frameworks Guiding Research on Talent Development

In Chap. 1 (Introduction), we introduce developmental science (Bronfenbrenner and Evans 2000; Cairns et al. 1996) as a guiding framework for conducting research and indeed for crafting strategies to tackle complex phenomena involved in talent development (TD). The choice of this framework stems from its capacity to align research endeavors, fostering a profound comprehension of the intertwined biological and sociocultural factors that mold individual developmental pathways toward human excellence. This is the only way that findings from various analytical levels, spanning different timescales and contextual realms, can be aggregated as a coherent whole. To facilitate further understanding of the nature and goal of TD research, we also introduce the notion of use-inspired research as well as three types of research based on main concerns over foundational understanding and practical use (Stokes 1997). Building on these dual guiding frameworks, this chapter postulates three tenets of human development in general and TD in particular: developmental diversity, specificity, and complexity. In light of these tenets, extant research can be critiqued, and future research can be fashioned. This chapter also postulates a research cycle encompassing six distinct research types and elucidates their interconnection through a three-phase logical architecture of TD research. The cyclical approach to TD research sets the stage for the structural layout of the book and the forthcoming chapters.

3.1 Why We Need a Developmental Science Framework Why do certain researchers posit that behavior stems from innate instincts, while others contend that it arises from reactions to specific environmental stimulation? What drives some researchers to seek quantitative, population-based measures of diverse human functions in straightforward mathematical terms, while others are drawn to study individuals holistically (e.g., Spearman vs. Binet on intelligence; see © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_3

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also Brody 2000)? And why do some scholars advocate for a “nature” perspective on human achievements, whereas others are convinced that “nurture” through learning and practice is paramount (e.g., see Ericsson et al. 2007; Gagné 2009)? A significant factor is that scientists, akin to other professionals, harbor distinct convictions. Science historian Lakatos (1978) termed these as ontological commitments—deeply entrenched beliefs regarding the mechanics of their particular research domains. Contrary to the stance of many positivists, who argue that scientific advancement occurs through falsification in a binary true-false manner, Lakatos proposed that these ontological beliefs are typically shielded from such falsification. This approach in scientific practice can hinder cross-tradition communication among researchers and, at its worst, foster dogmatic insulation (Ambrose 2000). Yet, there might be another angle to the dichotomous views on the nature-nurture debate or the cognitive versus affective-motivational explanations of TD. This perspective relates to the entrenched scientific tradition of employing reductive methodologies and adhering to positivist logic to discern between competing explanations. Reductive methods focus on collapsing behavioral and psychological phenomena into distinct components, addressing them sequentially (emphasizing the isolation of variables to identify linear causality). Positivist logic aids in discerning which theories can undergo falsification and which withstand its rigorous examination. While such a reductionist viewpoint often proves efficacious in understanding physical regularities and machinery, it encounters challenges in grasping organic nuances, especially in human development, which is both organismic (seeing the individual as an interconnected whole) and contextual (emphasizing interactions and exchanges with social environments). The challenges intensify when studying TD, which encompasses an individual experiencing developmental shifts in competence, while interacting with specific tasks and social environments. The foundational Cartesian divide (Overton 2014) presents in reductive efforts—fragmenting the whole into individual parts—relies on the premise that each part acts as an independent causal entity, assuming all other things being equal (ceteris paribus). As a result, the foundational metaphor for individual (and specifically, talent) development can be likened to a game of billiards, where the trajectory of a ball adheres to mechanical laws. Yet, an individual’s innate contribution to environmental encounters (i.e., nature) is invariably intertwined with the opportunities or constraints the environment presents (i.e., nurture) in human development. Hence, the nature-nurture binary is fundamentally flawed. Attempting a statistical decomposition of genetic and environmental impacts to quantify their causal roles in developmental variation (including talent) is misguided (see Horowitz 2000 for critique). The analytical, reductive method of examining singular elements sequentially can inadvertently resemble the myopic perspective of a blind man trying to discern an elephant. This poses risks: interpretations from a singular viewpoint might overshadow other crucial perspectives, or certain aspects of TD at one layer may be disproportionately emphasized (e.g., stable individual variances), causing core matters like developmental processes (e.g., intra-individual shifts on micro or macro scales; McCall 1981) to be obscured. Taken to its extreme, this fragmented

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understanding can evolve into rigid dogmas. For instance, some scholars, rooted in reductive reasoning, assert that for talent to blossom, one must inherently be “gifted” or possess innate abilities—qualities that prove elusive and challenging to quantify. Conversely, the same logic has led some to entirely dismiss “natural talent” as a viable explanation for expertise in a domain. Both standpoints hinge on the debatable assumption that it’s feasible to empirically identify a “pure” inborn ability, untouched by experiential learning (Howe et  al. 1998). As Magnusson (2001) pointed out, “developmental research is all too often shackled by peicemeal theories and/or sophisticated statistical models and methods, without the necessary reference to proper analysis of the phenomena under investigation at the appropriate level” (p.  151).  An encompassing, integrated, and holistic conceptual view helps sidestep such pitfalls. For instance, from the vantage of developmental science, nature is perpetually nurtured (i.e., shaped by environmental interactions in an epigenetic progression; Gottlieb 1998), and nurture, through learning and structured development, eventually reveals nature’s boundaries, including potential capacity limitations (Dai and Coleman 2005). At a foundational level, the essence of talent and TD spans from biological readiness and personal inclinations to cultural offerings and resources, along with social expectations and rewards. This spectrum encompasses learning and developmental processes that bridge the gap between inherent potential and cultural mediation. As alluded to previously, while some researchers emphasize stable individual variations, others prioritize cognitive-affective and developmental trajectories. Yet still others delve into pedagogies and technical support or the broader learning ecology. In the context of developmental science, a coherent TD research direction necessitates that these distinct traditions move beyond their individual patchwork contributions. Instead, they should strive for a holistic blend of ideas and implementations. Adopting a developmental science lens situates the diverse facets of TD research within the broader narrative of lifelong individual and societal evolution. This approach not only integrates the biological, cognitive, psychosocial, educational, and sociocultural dimensions of excellence but also acknowledges and synergizes the unique value each research tradition brings to the table. Such a perspective offers an integrated view of the intricate interplay between nature and nurture in TD (Dai and Sternberg 2021).

3.2 A Life-Span Developmental Systems and Talent Development As highlighted above, developmental science strives for coherence, integrating concepts and findings that might appear disparate and discrete. It endeavors to coalesce the biological, cognitive, psychosocial, educational, and sociocultural facets of pursuing excellence into a unified tapestry of individual growth. Actively, developmental science offers a comprehensive life-span framework, illuminating the path for

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research, revealing disparities among research traditions, and suggesting bridges over these chasms. While it’s feasible to separately analyze the biological or cultural foundations of talent, it’s crucial to recognize these as different analytical tiers (from genetic and neural to cognitive, sociocultural, developmental, and cross-generational aspects). When biology is conceptualized as a toolkit and culture as the innovative application or reinvention of these tools for both personal fulfillment and broader societal goals, their integral roles in TD, along with their interconnections, come into clearer focus (Dai 2024). Pepper (1942) delineated four foundational paradigms steering scientific exploration: mechanistic, organismic, contextual, and formal. Each paradigm serves as an overarching ontological compass, as proposed by Lakatos (1978). The mechanistic perspective gravitates toward atomism, dissecting phenomena until the most rudimentary functional units emerge (e.g., genes or memes; Dawkins 1989). The formalist approach perceives existence through the lens of logical-mathematical constants, akin to physics, often expressed in mathematical equations. Conversely, the organismic viewpoint perceives entities as holistically interconnected, operating within an organized framework governed by specific structural and functional norms (e.g., adaptive changes). The contextual paradigm views everything as situated in its functional contexts and thus having no inherent essence depleted of that contextual meaning and significance. In general, developmental science embraces a synthesis of the organismic and contextual paradigms (Lerner 2004; Magnusson and Cairns 1996). Individual development adheres to an organismic paradigm, emphasizing functional holism. This means that the individual is viewed as an open, adaptive, developing agent interacting with a specific environment, capable of self-engendered qualitative changes and transformations marked by new emergent properties and organization principles. This stands in contrast to a mechanistic perspective, where development is governed by formal rules or mechanical processes, like conditioning, that merely result in quantitative increases or decreases. Furthermore, individual development is intrinsically contextual. Factors—be they biological or cultural, intrinsic or extrinsic, including unpredictable life events or chance occurrences—all weave together to form an interconnected developmental system. The person-in-context emerges as the focal unit of analysis. This means that any adaptive characteristic of an individual cannot be divorced from its specific task and social environment. As Overton (2014) noted, a relational developmental system operates holistically due to the “interdependence of its parts” (p. 32).

3.2.1 Developmental Science as a Metatheoretical Guide Contrary to traditional developmental psychology, which concentrates on the normative age-related progression of individuals, along the physical, cognitive, and social dimensions, developmental science is dedicated to exploring developmental

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diversity. This can range from antisocial behaviors (as seen in developmental criminology) to mental disorders and maladaptive functions (examined in developmental psychopathology), all the way to human excellence in diverse culturally valued domains (as studied in TD). (a) The tenet of developmental diversity. It’s widely recognized that individual development tends to become more differentiated and divergent over time. McCall (1981) was perhaps among the first to propose that both genetic and environmental factors play a role in shaping these divergent paths, introducing the Scoop Model of mental development as a framework. McCall’s approach is largely nomothetic, meaning he views this divergent development as governed by universal laws, making it amenable to population-­ based estimates and inferences. On the other hand, Feldman (1994) approached the matter differently. He perceived individual development, particularly in TD, as non-­ universal, that is, elements of development that don’t conform to population-based parameters but need to be understood on their own terms through an idiographic approach. Such non-universal characteristics might have distinct biological foundations, such as autism or child prodigies, or they might arise from specific experiences and training, as seen in paths toward musical excellence or a dedication to a specific scientific subject. When we consider developmental diversity in the context of diverging trajectories, the unit of analysis shifts to the individual evolving within their sociohistorical and developmental contexts. This perspective is what Silverstein (1988) defines as a person’s developmental functional history (DFH). The concept of DFH can be viewed as a cornerstone for understanding TD, whether it’s Feldman’s detailed studies of child prodigies or Gruber’s deep dive into Charles Darwin’s intellectual evolution. This analytical unit allows scholars to align with the second core principle of developmental science: the tenet of developmental specificity. Additionally, the concept of DFH underscores the evolution of individuality, encompassing biological, social, and existential dimensions (Dai 2024). (b) The tenet of developmental specificity. The tenet of developmental diversity posits that there are not standardized, average “norms” that dictate individual growth, a departure from the conventional teachings of developmental psychology. Smith and Thelen (1993) demonstrated that even foundational aspects like motor movement are molded through dynamic actions and interactions, debunking the notion of “genetic blueprints” or pre-ordained normative models. The majority of talents we recognize are epigenetic, that is, they depend on provision of specific experience. In the realm of human growth, much of what we deem talent hinges on specialized environmental experiences that scaffold certain competencies (Vygotsky 1978). Those labeled “talented” often undergo extended phases of adaptation, sometimes overcoming many challenges before their talent is acknowledged. Bamberger’s (1986) studies reveal that musically gifted teenagers transition from an instinctive, intuitive way of processing music to a more formalized, analytical approach. Consequently, the idea that talent naturally and

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effortlessly emerges as a predestined ability is unfounded. Similarly, we can’t assume that the processes of TD are straightforward or inherent. They must be empirically determined through models of developmental processes that are derived inductively. As articulated by Bronfenbrenner and Ceci (1994): [H]uman development takes place through processes of progressively more complex reciprocal interaction between an active, evolving biopsychological human organism and the persons, objects, and symbols in its immediate environment. To be effective, the interaction must occur on a fairly regular basis over extended periods of time. Such enduring forms of interaction in the immediate environment are referred to as proximal processes. (p. 572)

While talent development doesn’t unfold as “naturally” as phenomena like language acquisition, it necessitates addressing the subsequent questions pertaining to developmental specificity: (a) Clarifying how an individual’s competence emerges and evolves—both structurally and functionally—arises from distinct modes of engagement and interaction within a particular task and social environment (the question of what). (b) Elucidating the developmental process behind a particular mode of engagement and interaction, and understanding the factors that drive and regulate this process (the question of how). (c) Determining the specific timing and duration of this action/interaction, pinpointing when these proximal processes occur and their duration; (the question of when). (d) Outlining the social conditions, including social structures, technological advancements, and cultural tools, resources, and norms, that support these processes (the question of where). Beyond the realm of developmental specificity, we must delve deeper into understanding how an individual progresses from being a novice to achieving excellence in expert performance, creative output, or leadership. This journey often spans years of exploration, practice, and relentless dedication to skill enhancement or profound domain understanding. Therefore, there arises the necessity for a developmental model with increasing organized complexity. Thus, we introduce the tenet of developmental complexity. (c) The tenet of developmental complexity. To grasp developmental complexity, it is imperative to consider human development as a multi-tiered, interactive dynamic system. Within this system, genetic, neural, behavioral-psychological, and sociocultural environments reciprocally interact across various strata, fostering epigenetic and organizational changes and transformations (Gottlieb 1998). For instance, while one researcher might explore the nuances of perfect pitch in music perception, another might delve into the evolution of tonality in musical development. There’s a pressing need for a unifying conceptual system that not only assesses the validity of these investigations but also contextualizes them within broader frameworks of musicality, spanning from neural to cognitive perspectives, eventually integrated into a developmental trajectory. When probing deeper aspects of musical talent development, like personal

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resonance with music or individual expression, it’s crucial to elevate the analysis, capturing personal development within the larger sociohistorical context that shapes and influences musical forms and their cultural significance. Several implications follow from the tenet of developmental complexity: (a) Pivoting Away from Pre-ordained Models Unlike traditional models, such as Galton’s (1869) genetic determinism later adopted by Terman (1925) and others, developmental complexity suggests multi-level constraints at critical developmental transition points. Research reveals that many talented individuals face failures and setbacks in TD (e.g., see Bamberger 1986 on musically or scientifically talented teenagers; Dai et  al. 2015 on early college entrants in STEM domains; and Feist 2006 on finalists in the Science Talent Search). These are not necessarily due to innate flaws or a nebulous lack of “talent” but might be attributed to endogenous adaptation failures or exogenous support inadequacies in the face of new levels of challenges, leading to system disruptions or even withdrawal. Such occurrences must be contextualized with developmental specificity. (b) Challenging Reductionist Views Instead of isolating genetic and environmental factors, talent and exceptional performance should be understood as processes of increasing differentiation and integration (Werner 1967). Over time, situational performance evolves into enduring competence, interest becomes identity, and initial competence-based motivation gives way to a more profound, meaning-driven pursuit (Gruber 1981; MacKinnon 1978; Dai and Li 2023). To build a model of developmental complexity, it’s essential to bridge concepts spanning from the neural to the cognitive, and from the biological to the cultural. This requires a guiding metatheory that helps organize seemingly discrete evidence at multiple levels of analysis into a coherent account, as opposed to a positivist scientific approach which, as Magnusson (2001) argued, may lead to fragmented theories that lack conceptual coherence (see the opening quote of this chapter). (c) Recognizing Diverse Developmental Pathways As we chart interactions between biological, social-cognitive, and developmental forces, bridging human potential with cultural accomplishments, we will observe a multitude of developmental trajectories, all marked by increasing organized complexity. The inherent differences within and between individuals form the bedrock of developmental diversity. It’s crucial for researchers to prioritize this developmental heterogeneity, inherent in the fabric of developmental complexity, rather than dismiss them as mere anomalies or “noises.” Developmental diversity in the form of equifinality and multifinality, likely due to developmental complexity, prevalent in developmental psychopathology (Cicchetti and Rogosch 1996), should be recognized as even more prevalent to TD. The myriad pathways leading to diverse creative contributions within a domain should be expected (Sternberg 1999). Overlooking such intricate complexities in favor of oversimplified models is counterproductive. The ­developmental science framework offers a means to navigate this complex conceptual landscape with clarity.

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3.2.2 Developmental Science as a Methodological Strategy The metatheoretical guide, as outlined and embraced by developmental science, carries significant methodological implications. It promotes several methodological strategies: (a) prioritizing interaction-dominant approaches over component-­ dominant ones; (b) favoring person-centered perspectives as opposed to variable-­ centered ones; and (c) choosing emergence logic over reductive logic. They are elaborated in the next section. Developmental Diversity: Interaction-Dominant vs. Component-Dominant Designs  Hilpert and Marchand (2018) differentiate between interaction-dominant and component-dominant research designs. The former emphasizes the significance of individual components, while the latter underscores the role of their interactions. Most psychometric approaches to developmental diversity adhere to the component-­ dominant methodology, pinpointing core components as driving developmental outcomes, using either parametric or non-parametric statistics. A limitation of component-dominant designs is that the identified components (such as genetic, cognitive, personality, and social variables) often appear timeless and independent of context. They are not depicted as bound by time, location, or their interactions with other components (e.g., the component models of talent development are discussed in Chap. 2). On the other hand, interaction-dominant designs, which treat the person-in-context as the primary unit of analysis and emphasize the interplay of components, facilitate a holistic, systems-based understanding of developmental outcomes. Developmental Specificity: Person-Centered vs. Variable-Centered Designs  Variable-centered designs focus on broader populations, aiming to identify phenomena at a macro level. They operate under the assumption that variables can pinpoint stable and structural attributes within a social system or a given population (i.e., become parametric). Conversely, person-centered designs prioritize the individual or small groups, seeking to discern phenomena at a more granular, micro level. Such designs posit that a detailed account of processes and changes at the individual level provides a more authentic and meaningful understanding (Bergman and Magnusson 1997). This perspective becomes especially pertinent when examining extreme cases of individual development, such as child prodigies, creative productivity, or instances of psychopathology. While variable-centered, quantitative designs may be somewhat effective in analyzing “natural” development (for example, in assessing mathematical or artistic precocity, as explored by Porath and her colleagues; see Porath 2006), they falter when confronted with developmental phenomena that challenge the assumptions of continuity in development and measurement. In contrast, person-centered methodologies prove adept at discerning in situ interaction patterns. They can capture nuanced developmental shifts, whether quantitatively through idiographic statistics (Molenaar 2004) or qualitatively by leveraging multi-wave interviews or intensive

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observations of real-time interactions within context (e.g., Barron 2006; Glaveanu et al. 2013). Developmental Complexity: The Reductive Logic vs. the Emergence Logic  Reductive logic seeks to explain intricate, higher-level interaction patterns by breaking them down into simpler, lower-level components. Through this lens, developmental complexity is perceived as being composed of foundational “building blocks.” In contrast, the emergence logic posits that higher-level interactions exhibit emergent properties that cannot be distilled to mere lower-level elements but arise from their dynamic self-organization in response to external challenges (Lewis 2000). For instance, from an organismic-contextual perspective, attributes such as personal strivings or grit for TD cannot be merely attributed to individual variations in traits like the need for achievement or persistence. Instead, they should be contextualized as adaptive responses to the opportunities and challenges presented by the surrounding environment. In essence, utilizing the person-in-context as the unit of analysis to identify emergent interaction patterns provides a more comprehensive explanation for developmental diversity. Rather than employing reductive methods, like variance partitioning or decomposition which seeks to determine the independent contribution of individual components, the emergence logic promotes the use of higher-order constructs. This includes concepts such as contextualized concerns (McAdams 1996), characteristic adaptation (Dai 2021; McAdams and Pals 2006), and personal projects (Cox and Klinger 2011). These constructs embrace a significant level of idiographic complexity, to borrow Alfred Binet’s terminology, enabling the creation of developmental complexity models that can account for a wide range of developmental pathways.

3.2.3 Summary The lens of developmental science offers a comprehensive framework that illuminates the merits and limitations of various TD research traditions, fostering a coherent understanding of talent’s origins and growth. This perspective paints a rich tapestry of diverse pathways to excellence, encapsulating both the variable learning and development trajectories seen across populations, as well as the emergence of novel person-environment interactions and heightened levels of organizational complexity (Dai 2021). One of the primary strengths of the developmental science viewpoint is its acknowledgment of human development as multifaceted. It appreciates the plurality of sources of developmental potential and emphasizes its dynamic formation through ongoing person-environment interplay (see Dai and Sternberg 2021). This stands in contrast to more linear perspectives that offer a singular narrative of societal structures, such as the IQ-determined social stratification proposed by Herrnstein and Murray (1994). Intriguingly, sometimes personal vulnerabilities can pave the

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way for unparalleled achievements, as observed in figures like Van Gogh or Michael Jackson (Tordjman et  al. 2020). This presents opportunities for interdisciplinary dialogues, drawing parallels between TD researchers and those delving into developmental psychopathology—or even developmental criminology (considering individuals like Adolf Hitler or Ted Kaczynski). Through the guiding principles of developmental diversity, specificity, and complexity, researchers can adeptly traverse the intricate landscape of talent trajectories and pathways, embracing the inherent diversity of the field.

3.3 A Conception of the Research Cycle of TD Research How can the developmental science framework, together with classification of use-­ inspired research, as outlined above, shape a research agenda for TD? How can we seamlessly integrate existing research traditions into a unified scheme and discern their unique contributions? A comprehensive conceptualization is essential. We have crafted a three-phase research sequence that aligns with the inherent logic of Stoke’s (1997) classification from pure basic research, to use-inspired research, to “pure” applied and application research (see Fig. 3.1).

3.3.1 Phase I Research: Phenomena to Be Defined and Understood The initial phase can be termed “foundational research.” Its primary objectives are to (a) explore the immediate phenomenology of talent manifestations in their broadest sense, (b) categorize talent domains according to their unique structural,

a) Phenomena to be Defined

b) Research for Grounded Knowledge

1. Definition,Classification and Boundaries of Talent

2. Differential Learning and Development (Interpersonal) 3. Talent Emergence and Developement (Intrapersonal) 4. Likelihood of Success of Failure at Important Developmental Junctures (Combining Knowledge of 2-3)

c) Research for Practical purposes 5. The Foundation and Technology of Talent Identification 6. Developmentally Responsive Provision and Intervention Systems

Fig. 3.1  A taxonomy of six types of research organized as a three-phase cycle of TD research

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functional, and developmental characteristics, and (c) define clear boundaries and criteria, while also highlighting potential pathways (encompassing both bio-­ ecological and sociocultural foundations) for addressing TD challenges associated with each specific talent domain. We generically label research in this phase as type 1 research. Type 1 research primarily adopts a descriptive approach. It frequently involves field research situated in authentic performance contexts, delving deep into the analysis of task structures, functional purposes of a domain, and associated social contexts (e.g., Ceci and Liker 1986) and comparative studies are also common in foundational research on TD, examining differences between talented and average individuals or comparing experts with novices, as well as highlighting distinct differences across domains. This type of research also looks into the biological, cognitive, and sociocultural foundations of a particular talent domain, clarifying the relevance of various components, including the IQ factor as well as more specific memory or visual-spatial abilities central to particulars lines of TD (Ceci and Liker 1986; Wai et al. 2009).

3.3.2 Phase II Research: Seeking Grounded Knowledge In the second phase, research systematically delves into the differential, cognitive-­ affective, and developmental foundations of TD in ways that are both developmentally and contextually suitable. A study is deemed fitting in these respects if: (a) It is deeply rooted in developmental and sociocultural contexts. (b) It considers both endogenous and exogenous factors that interact and jointly influence a talent trajectory. (c) The research questions explore the structure, processes, and timing intricacies of TD, aiming to comprehend developmental diversity, specificity, and complexity. Three research types are pinpointed in this phase. Type 2 Research  Within Phase II, type 2 research endeavors to map distinct talent trajectories and associated factors. This research typically emphasizes short-term differential learning and long-term divergent development (pathways and pathways), seeking to comprehend the varied distribution of talent in populations. Critical to type 2 research is the integration of two differential research traditions: intelligence and personality (Ford 1994; Mischel and Shoda 1995; Snow 1995). For instance, Lubinski, Benbow, and their team charted talent distributions across domains by analyzing cognitive and affective profiles (Lubinski and Benbow 2021). The foundational belief behind type 2 research is twofold: firstly, individuals are drawn to activities that align with their strengths and passions, and secondly, society tends to favor those who show promise in developing culturally significant talents. In essence, TD is a reciprocal selection process where certain individuals thrive and unlock new developmental opportunities. The primary aim of type 2 research is to

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highlight differential trajectories, placing unique, idiographic individual TD patterns into a more expansive, monothetic population context. Type 3 Research  Contrasting with type 2’s focus on individual variations, of between-person differences, type 3 research, as outlined in Phase II (see Fig. 3.1), concentrates on the intrapersonal and psychosocial processes and mechanisms behind developmental changes and shifts. These insights not only fortify the assertions of type 2 research but also bring forth new knowledge about the conditions, timings, and manners of relevant developmental shifts. These insights can be cross-­ referenced across domains to discern both commonalities and unique elements. While type 2 research takes a norm-referenced, inter-individual comparative approach, type 3 research adopts an intrapersonal dynamic process perspective, emphasizing proximal processes (Bronfenbrenner and Ceci 1994) and individual evolution over time. For instance, in the study by Dai et  al. (2015), participants furnished comprehensive retrospectives of their psychosocial college experiences. While type 2 research is variable-centered, overlooking some contextual nuances, type 3 research zeroes in on the individual, focusing on intimate proximal processes. To be sure, something revealed in these intrapersonal and psychosocial accounts can have a direct bearing on findings of type 2 research (on differential learning and development). However, the intrapersonal and psychosocial accounts (e.g. Evolving Systems Approach; see Gruber, 1980) often add to more nuanced understandings of intrapersonal changes (e.g., how they build resilience and restore confidence in the face of unfavorable social comparison; Dai et  al.). Type 3 research can capture pivotal TD facets that might escape the scope of type 2 research, presenting a more granular, process-focused accounts. Type 4 Research  Expanding upon the insights from types 2 and 3 research, type 4 research in Phase II focuses on crafting and evaluating more refined and accurate predictive models. These models shed light on pivotal phase transitions within TD. Rooted in the principles of developmental diversity and specificity, in that more “local” or middle-range predictions in situ are better calibrated than general prediction models (Lohman and Korb 2006). This perspective is anchored in the belief that as individual development veers toward specificity within particular domains, the overarching notion of universal development loses its weight. Consequently, understanding the nuances of development (the specifics of what, how, when, and where) and the multifaceted layers of analysis becomes essential. Furthermore, type 4 research leans toward proximal prediction rather than extensive, long-term forecasting due to the inherent unpredictability in developmental paths (as per the concept of developmental indeterminacy by Lewis 2000). This unpredictability encompasses factors such as the diminishing impact of certain effects over time. Models that emphasize immediate contexts, using local norms for talent identification (Lohman 2009), prove more effective in foreseeing imminent transitions and changes (Feist 2006). From a practical standpoint, these models hold significant utility, particularly for talent identification and targeted provisions and interventions.

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In essence, the central role of type 4 research is to bridge the grounded knowledge of TD laid out in types 2 and 3 research with the pragmatic approaches to nurture TD found in types 5 and 6 research. Phase III Research: Research on Relevant TD Practice Research in Phase III focuses on talent identification and the methodologies and systems crafted to enhance TD. Unlike conventional developmental psychology, which often delegates such research to educational specialists, the developmental science framework integrates all human practices and systems, inclusive of talent identification and provisions, as core to both human development in general and TD in particular. This research can oscillate between use-inspired basic research (as represented by Pasteur’s Quadrant)—like foundational studies on talent identification—and purely applied research (illustrated by Edison’s Quadrant). While some studies aim for a deep understanding of cultural contexts that make some cultural provisions and psychological interventions necessary, others assess the effectiveness of particular methodologies in fostering TD. Research stemming from Pasteur’s Quadrant serves as groundwork, whereas Edison’s Quadrant’s focus is on conceptualizing and appraising practical innovations. Furthermore, they should reciprocate by enhancing our fundamental grasp of the role of educational and societal practices in TD. Type 5 Research  We categorize research on talent identification as type 5 research. Investigations into the foundation of talent identification tackle myriad concerns, from the impetus and theoretical frameworks underpinning talent identification to its validity and applicability in specific TD contexts. Such foundational studies mirror use-inspired basic research (Pasteur’s Quadrant) and lay the groundwork for formulating talent identification technologies. In comparison, the technological side of talent identification zeroes in on the design and validation of instruments, tools, and systems tailored to specific talent identification objectives. It’s pivotal to note, especially in relation to the research cycle illustrated in Fig. 3.1, that these tools and systems are always conceptualized within the broader TD context. For instance, the appropriateness of any psychometric tool hinges upon its relevance in specific TD scenarios. Essentially, no single instrument, such as an IQ test, can be deemed universally valid outside of its application context. The objective of talent identification is inherently pragmatic—it’s a conduit to nurturing human potential. Type 6 Research  Type 6 research, which we designate for studies on TD programs of provisions and interventions (sometimes inclusive of an identification/selection component), bears similarities to type 5. It too can be situated within Pasteur’s or Edison’s Quadrants. The foundational aspect of type 6 research explores the broader sociocultural contexts and available resources for TD activities (Chowkase 2022). This foundational work also delves into the core principles of advanced human learning and development, probing into topics like learning ecology (Barron 2006) or a community of learning that pushes learners at the edge of their competence (Bereiter and Scardamalia 1993).

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In Edison’s Quadrant of type 6 research, the primary focus lies in devising new TD systems adaptable to a broad spectrum of TD settings. While type 6 research within Pasteur’s Quadrant might delve into the nuances of naturally occurring “ontological innovations” (DiSessa and Cobb 2004), research in Edison’s Quadrant ventures into pioneering approaches to TD. It investigates novel tools and resources for TD across various contexts, utilizing methodologies like design-based research (Barab and Squire 2004) or quasi-experimental designs (e.g., Reis et  al. 2011). Importantly, within many of these “ontological innovations,” the process of talent identification isn’t necessarily distinct from TD—it can be seamlessly integrated into TD itself (e.g., Passow 1981; Renzulli 1986). Moreover, these “ontological innovations” may encompass systems explicitly tailored to TD, like Talent Search models, Young Scholar programs, and Science Olympia. They can also embrace foundational TD innovations, such as cognitive apprenticeship (Rogoff 1990, legitimate peripheral participation (Lave and Wenger 1991), or the concept of “learning ecology” (Barron 2006). To summarize, within this research cycle framework (referenced as Fig. 3.1), six pivotal research categories have been delineated. These categories or types adhere to a logical progression of intensifying depth, facilitating the constructive application of knowledge in the creation of practical tools and resources aimed at nurturing TD. (a) Type 1 Research (Chap. 4): Identifying the immediate phenomenological aspects of talent, delineating the scope and boundaries of pertinent talent phenomena, and categorizing diverse talent manifestations according to distinct characteristics, domain differences and typology, and phases of development (b) Type 2 Research (Chap. 5): Charting the differential learning and divergent development patterns both quantitatively and qualitatively, tracing different talent trajectories and pathways and identifying timing of the onset and peaks of TD, and providing a nomothetic landscape of talent distributions (c) Type 3 Research (Chap. 6): Elucidating intrapersonal and psychosocial dynamics, shifts, and transitions at pivotal moments of TD to comprehensively grasp the specific conditions and interactions that either foster or impede progression to higher levels of TD and excellence (d) Type 4 Research (Chap. 7): Drawing from types 2 and 3 research, pinpointing differential and developmental benchmarks, milestone achievements, psychosocial markers, and interaction trends to craft refined and nuanced prediction models that can guide and enrich practical TD decision-making (e) Type 5 Research (Chap. 8): Drawing from types 2–4 research, establishing a robust foundation for talent identification and delving into the technology of identification, with criteria and procedures customized to specific TD trajectories and aligned with the practical objectives of providing timely provisions and services (f) Type 6 Research (Chap. 9): Establishing a robust social and pedagogical foundation for TD infrastructures and provisions, constructing a TD system that is genuinely attuned to developmental needs and forward-thinking, and pioneering novel “ontological innovations” to bolster talent support for TD

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Details for each research type, along with specific recommendations, will be explored in the subsequent six chapters (Chaps. 4, 5, 6, 7, 8, and 9). It’s crucial to note that these six research types form an iterative cycle, not a strictly linear progression, for reasons outlined in the following section.

3.4 How the Cycle of the Three-Phase Research Agenda Works Several reasons underscore the iterative, cyclical nature of TD research: (a) Evolving Nature of TD: TD phenomena are not static but continually evolve with changing human activities and endeavors, particularly as societal structures and technologies advance. For instance, the distinctions between TD in the information age’s knowledge economy and the industrial age are pronounced. This dynamic nature means that what was effective in the past may not be relevant today or in the future. Even basic talent phenomena may transform under changing socio-technological conditions, necessitating continual reassessments of established knowledge. (b) Balance Between Universal and Particular: There’s an inherent nomothetic-­ idiographic tension in TD research, a tension between universal assumptions about talent categories and TD, and immediate phenomenology of particular cases of talent and TD. While norms and statistical regularities offer a broad understanding, individual adaptations reveal in-depth, intimate insights. This dichotomy can lead to debates between top-down generalizations and bottom­up inductions, underscoring the necessity for an iterative approach to research progression. (c) Research Reciprocity: The interplay between different research types is bidirectional. For instance, practical problem-solving has often been the birthplace of theory and research in gifted studies. Julian Stanley’s Talent Search model, a solution to a practical challenge, subsequently gave birth to a landmark foundational and longitudinal research project (SMPY; Lubinski and Benbow 2006), illustrating the interconnectedness of various research types. (d) Human Practices as Catalysts: While foundational research offers new insights, applied research can simultaneously create new paradigms. Technological, pedagogical, and institutional advancements can redefine how talent is perceived, identified, and nurtured. This symbiotic relationship means that practices and theories continually inform each other. (e) Use-Inspired Research as an Ongoing Journey: Such research is perpetually “under construction.” Like Neurath’s boat metaphor, it’s being built even as it sails, with inevitable imperfections and gaps that need addressing. However, just as the boat remains seaworthy despite its ongoing construction, our models and theories, grounded in real-world TD practices, continue to evolve and improve through this iterative research process.

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In essence, the research process depicted in Fig. 3.1. illustrates an ongoing feedback and feedforward mechanism, rather than a linear progression from basic to applied research. New discoveries prompt new questions, keeping the cycle vibrant and relevant. Use-inspired research is always evolving, with the ultimate goal of establishing a robust foundation for understanding pathways to human excellence across diverse endeavors. Structure of the Rest of the Book  Chapters 4, 5, 6, 7, 8, and 9 will systematically detail the six types of research, progressing from type 1 through type 6, as previously outlined. For each of these chapters, two “demo studies” are presented as a pedagogical feature in the book to show how a study can be designed with a particular type of research, and what kinds of contributions it can make and what limitations it might have (by limitations we do not mean pitfalls, as any single study has “limitations” that warrant further research). After each type of research is discussed in depth, Chap. 10 offers a comprehensive review of the present knowledge and research landscape concerning TD in terms of strengths and weaknesses in light of the framework of three-phase cycle (Fig. 3.1), drawing comparisons and contrasts with the fields of developmental psychopathology and developmental criminology. Chapter 11 delves deeper into methodological challenges highlighted in this chapter and those that arise in the subsequent six chapters, primarily those associated with analyses across various facets and levels of TD. Furthermore, Chap. 11 will end the book by proposing an epistemology of TD and human excellence, capable of capturing five central features of TD and excellence: engagement, divergence, emergence, excellence, and coherence.

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Gruber, H. E. (1980). “And the bush was not consumed.” The evolving systems approach to creative work. In S. Modgil, & C. Modgil (Eds.), Toward a theory of psychological development (pp. 269–299). NFER Press. Gruber, H.  E. (1981). Darwin on man: A psychological study of scientific creativity (Rev. ed.). University of Chicago Press. Hilpert, J. C., & Marchand, G. C. (2018). Complex systems research in educational psychology: Aligning theory and method. Educational Psychologist, 53, 185–202. Howe, M.  J. A., Davidson, J.  W., & Sloboda, J.  A. (1998). Innate talents: Reality or myth? Behavioral and Brain Sciences, 21, 399–442. Horowitz, F. D. (2000). Child development and the PITS: Simple questions, complex answers, and developmental theory. Child Development, 71, 1–10. Herrnstein, R. J., & Murray, C. (1994). The bell curve: Intelligence and class structure in American life. New York: Free Press. Lakatos, I. (1978). The methodology of scientific research programs. Cambridge University Press. Lave, J., & Wenger, E. (1991). Situated learning: legitimate peripheral participation. Cambridge University Press. Lerner, R.  M. (2004). Genes and the promotion of positive human development: Hereditarian versus developmental systems perspectives. In C. G. Coll, E. L. Bearer & R. M. Lerner (Eds.), Nature and nurture: The complex interplay of genetic and environmental influences on human behavior and development (pp. 1–33). Lawrence Erlbaum Associates. Lewis, M. D. (2000). The promise of dynamic systems approaches for an integrated account of human development. Child Development, 71, 36–43. Lohman, D.  F. (2009). Identifying academically talented students: some general principles, two specific procedures. In L.  V. Shavinina (Ed.), International handbook on giftedness (pp. 971–997). Springer. Lohman, D. F., & Korb, K. A. (2006). Gifted today but not tomorrow? Longitudinal changes in ability and achievement during elementary school. Journal for the Education of the Gifted 29, 451–484. Lubinski, D., & Benbow, C.  P. (2006). Study of mathematically precious youth after 35 years. Perspectives on Psychological Science, 1, 316–345. Lubinski, D. & Benbow, C. P. (2021). Intellectual precocity: What have we learned since Terman? Gifted Child Quarterly, 65, 3–28. Magnusson, D. (2001). The holistic-interationistic paradigm: Some directions for empirical developmental research. European Psychologist, 6, 153–162. Magnusson, D., & Cairns, R. B. (1996). Developmental science: Toward a unified framework. In R. B. Cairns, G. H. Elder, Jr., & E. J. Costello (Eds.), Cambridge studies in social and emotional development. Developmental science (p. 7–30). Cambridge University Press Mackinnon, D. W. (1978). In search of human effectiveness. Creative Education Foundation. McAdams, D. P. (1996) Personality, Modernity, and the Storied Self: A Contemporary Framework for Studying Persons, Psychological Inquiry, 7, 295–321. McAdams, D. P., & Pals, J. L. (2006). A new big five: Fundamental principles for an integrative science of personality. American Psychologist, 61, 204–217. McCall, R. B. (1981). Nature-nurture and the two realms of development: A proposed integration with respect to mental development. Child Development, 52, 1–12. Mischel, W., & Shoda, Y. (1995). A cognitive-affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102, 246–268. Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2, 201–218. Overton, W. F. (2014). Relational developmental systems and developmental science: A focus on methodology. In P. C. M. Molenaar, R. M. Lerner & K. M. Newell (Eds.), Handbook of developmental systems theory and methodology (pp. 19–65). The Guilford Press. Passow, A. H. (1981). The nature of giftedness and talent. Gifted Child Quarterly, 25, 5–10.

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Chapter 4

Type 1 Research: Phenomenology, Definition, Classification, and Foundation

In everyday language, talent is a label attributed to behavior or performance that appears exceptional, yet its underlying meaning and foundations often remain implicit. While one of the definitions presented by the Merriam-Webster Dictionary describes it as a unique aptitude, particularly in athletics, arts, or creativity, the term lacks a singular, scientifically investigable definition (Merriam-Webster n.d.). Historically, the concept of talent has been examined through three distinct perspectives within relevant research: the giftedness and talent perspective (represented by Gagné 2005; Renzulli 2011, 2021; Stanley 1997), the expertise perspective (represented by Ericsson 2001; Ericsson and Ward 2007; Weisberg 2006), and the creative productivity perspective (represented by Glaveanu et al. 2020; Sawyer 1999, 2003; Simonton 1988, 1997). Significantly, the various perspectives embraced by researchers can give rise to different assumptions about the nature of talent, as well as diverse understandings of how talent originates and develops over time. As a result, these differing perspectives can lead to the utilization of notably distinct empirical research methodologies. What we identify as foundational research (type 1 research) delves into three main questions: (a) how we define talents and the boundaries of talent domains, (b) what makes the foundation of talents, and (c) how we characterize and delineate talent manifestations across main life stages. Notably, instead of providing definitive answers to these questions, the intention of this chapter is to provide an overview of existing views on these issues and reflect on the topic for effective approaches to addressing and investigating those questions.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_4

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4.1 Definition and Classification Recognizing the limitations inherent in relying solely on “general intelligence” as the principal criterion for identifying individuals displaying exceptional potential across a multitude of domains, Witty (1958) introduced a more comprehensive definition of giftedness or high potential: There exist children whose remarkable potential in areas such as art, writing, or social leadership becomes manifest primarily through their performance. Consequently, we advocate for an expanded definition of giftedness that encompasses any child whose consistently exceptional performance in a potentially valuable realm of human activity garners attention. (p. 62)

Witty’s proposition stresses the necessity of investigating talents spanning various domains. A fundamental aim of foundational research, what we label as type 1 research, is to demarcate talent domains to elucidate the boundaries that delineate different spheres of human excellence.

4.1.1 Starting with the Immediate Phenomenology of Talents The preliminary exploration of a given domain should ideally commence by scrutinizing immediate phenomena, thereby involving direct observation of activities within that specific realm. During the early stages of academic inquiry, pioneering scholars such as Witty (1958) and Feldman (1986) exemplified this bottom-up approach through their direct observations of apparently talented behavior and outstanding performance. Their work significantly contributed to the foundational understanding of talent within their respective studies. Feldman’s (1986) seminal work encompassed comprehensive case studies focusing on six child prodigies, who exhibited potential for excellence in music, painting, chess, and mathematics, that can only be reached by adults, presumably because of a prolonged period of formal learning and training (e.g., the ten-year rule) needed for such achievements. Importantly, prodigies are not merely characterized by high intelligence; rather, they manifest a variety of domain-specific talents. The assumption can be made that these child prodigies possess an exceptional ability to rapidly master domain-specific knowledge and skills (e.g., distinct evidence for the ease of mastering a particular type of task (e.g., writing a story) or processing a particular type of information (e.g., playing chess). Although the primary aim isn’t centered on scrutinizing specific domains, Feldman (1986, p.  77) astutely recognized the pivotal correlation between domain characteristics and the emergence of prodigies, noting that prodigies arise from “the alignment of a child’s particular capabilities with a domain’s specific complementary demands.” Consequently, gaining a comprehensive understanding of prodigies necessitates an insight into the inherent nature of the domains themselves (e.g., affordances and constraints of tasks featured by a particular domain; Dai and Renzulli 2008). For instance, grounded in his

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meticulous observation of a male prodigy in writing and the distinct narrative features his work displays, Feldman concluded that “writing is a domain that seemingly requires advanced age and presumably greater maturity for the creation of enduring works” (p.  59). He distinguished writing from other domains, such as musical performance and chess, wherein even very young children can produce a performance that rivals that of well-trained adults. Moreover, an intriguing and commonly observed phenomenon in the study of child prodigies is that prodigious individuals do not exist across all domains. Remarkably, domains such as music performance and chess appear to be more fertile grounds for prodigies, whereas fields like visual arts and natural sciences exhibit a dearth of such cases (Feldman 1986; Obler and Fein 1988). This discrepancy of talent occurrences in children across different domains offers a unique opportunity to scrutinize the differentiating features between domains that enable prodigious talent in childhood and those that do not. Importantly, Feldman (1986) proposed a series of domain qualities conducive to the emergence of prodigies. These include developmental factors, where a domain undergoes significant changes or restructuring over time; attractiveness and accessibility, whereby even children can find the domain intriguing and comprehend its essence, knowledge, and skills; and the presence of well-defined performance standards and mastery levels, entailing clear criteria for discerning varying degrees of performance or outputs. However, not all domains encompass these attributes. For instance, the emergence of basketball prodigies is unlikely due to inadequate physical power at early ages. Similarly, the emergence of prodigies in playwriting is implausible without the requisite life experience and social understandings, often prerequisites for accomplished playwrights. It should be noted that the above investigation of talent phenomena is not limited to prodigies or gifted children, and can be conducted on talented individuals from different developmental stages, such as adolescence or adults (e.g., Cotterill 2015; Haraldsen et al. 2020; Glaveanu et al. 2013). From the mentioned examples, we can see how investigations of immediate phenomenon of talent unfold intertwined relationships between domain characteristics and developmental constraints on talent within that domain. Researchers exploring specific talent domains should begin with focused observations, whether on children’s talents (e.g., Feldman 1986) or adults’ abilities (e.g., Ceci and Liker 1986), revealing shared patterns and unique features across domains (e.g., Glaveanu et al. 2013).

4.1.2 Creating a Framework for Developing Taxonomies of Talent Domains After accumulating ample observations of specific talent instances, a top-down approach becomes valuable for structuring a framework that systematically defines and categorizes talent domains. This framework holds particular significance when

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addressing domain specificity. When investigating a domain, a shared understanding of its scope is crucial. For instance, in exploring musical talent, it’s essential to determine if the “domain of music” possesses a coherent understanding among researchers. For example, musical talent can encompass instrumental performance, composition, and even music teaching, each with distinct skill requirements and standards of excellence (McPherson and Williamon 2016). Thus, the term “domain” can vary in a large breadth. Talent domains can be categorized into two primary groups: performance-based and production-based domains, as discussed by Subotnik et  al. (2011) and Tannenbaum (1997, 2009). The distinction between these two categories arises from their differing objectives and skill sets. Performance entails the real-time execution of skills to achieve a specific goal, such as executing a triple axel or singing a song. In contrast, production involves the meticulous crafting and arrangement of elements to create a valuable product, such as developing a theory or inventing a gadget. Expanding upon these categories, Tannenbaum (1986, 1997, 2009; see Kanevsky 2020) further classifies talent domains into 12 patterns, taking into account three key parameters resulting from a 2 × 3 × 2 matrix: the mode of functioning (performance or production), the content (intellectual/technical products, artistic performances/creations, or human services), and the style (creativity or proficiency). Tannenbaum’s framework is clear and useful, but certain talent domains, like law and chess, can exhibit hybrid features of both performance and production. For instance, chess players encompass dual roles. During competitions, they perform as they execute strategic moves (performance). Yet, they can transition into a producer role and craft innovative strategies (production). Similarly, think of Frédéric Chopin, who was both a pianist and a composer. Moreover, creativity and proficiency can manifest across different degrees in both performance and production. This duality underscores the multi-faceted nature of these domains and the interconnectedness between performance and production. As mentioned earlier, talent has developmental underpinnings. Feldman (2003) distinguished between domains that rely more on innate, domain-specific intuitions and aptitudes, and those that lack innate foundations and instead depend on fluid intelligence to navigate their intricacies. By the same token, Dai (2017) proposed three kinds of domains based on developmental levels: domains of bio-ecological effectivity, domains of cultural creation, and domains carved out by individuals. This novel classification implies a developmental cascade, from universal foundations via specific sociocultural mediation to unique personal endeavors (Dai 2024; Dai and Chen 2013). In addition, five bio-ecological domains of effectiveness are adaptable to various cultural contexts: psychomotor (athletic), expressive (artistic), social (leadership), technical (innovative), and intellectual (creative). Talent development (TD) in domains shaped by culture often hinges on distinctive blends of these foundational bio-ecological strengths, forming a person’s unique profile (Rose 2016). For instance, an individual might excel in track and field sports but not in mathematics. The developmental cascade is responsible for the emergence of a unique Personal Action Space (PAS), with all its biopsychosocial evolving complexity: Leonardo da Vinci or Elon Musk was born.

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4.1.3 How Taxonomy and Framework Help to Chart Research on Specific Phenomena Witty (1958) challenges the idea of giftedness as a universal trait transferable across domains. Each domain requires specific skills and expertise, emphasizing the importance of “domain specificity.” This approach highlights that excellence in one area doesn’t guarantee proficiency in another. Domain specificity underscores task-­ specific and context-dependent attributes, emphasizing the need for a talent taxonomy to define and compare different talents effectively. Such a framework clarifies what a talent refers to and its structure, aiding research comparability and understanding. A range of qualitative methods (e.g., historical-archival methods, phenomenological studies, ethnographic fieldwork, and case studies) can be employed to probe the immediate manifestations of talents and their developmental trajectories (or cascades). These direct observations contribute to the refinement of the taxonomy of talent domains, enabling a comprehensive synthesis of our understanding. Conversely, the taxonomy of talents or domains itself serves as a guiding compass, steering our analyses of performances across diverse domains. For instance, in exploring a specific talent, researchers can ask about how it operates (mode), the exact domains where talents become evident (content), and the ways in which exceptional abilities are demonstrated (style, using Tannenbaum’s [1997] taxonomy). Glaveanu et al. (2013) investigated how creative work is generated across six domains, offering valuable insights into patterns and differences among these domains. It’s important to note, however, that based on Tannenbaum’s taxonomy, these six domains predominantly fall under production domains rather than performance domains. Additionally, the study primarily focused on participants who produce creative ideas (e.g., scriptwriters, painters) or tangible products (e.g., sculptors, designers), with only a few exceptions of scientists who could be seen as proficient idea producers. In this case, utilizing domain taxonomies as guidance enables us to assess the potential applicability of our findings to domains beyond those already explored in the study.

4.2 Three Foundations Beyond establishing a robust classification system encompassing various talent domains, type 1 research plays a crucial role in uncovering the causal determinants that underlie TD. The formation of talent finds its roots in three foundations: the biological foundation (particularly neurophysiological), the sociocultural foundation, and the psychological foundation (cognitive-affective-conative). We will explore how the three perspectives—the giftedness and talent perspective, the expertise perspective, and the creative productivity perspective—differ in their interpretations of the aforementioned three foundations and how they steer empirical research to investigate the influence of these foundational aspects of TD.

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4.2.1 Neurophysiological Foundation The nature-nurture debate has, throughout history, constituted a fundamental and pivotal inquiry across all research fields of human behavior, including the phenomena of talent and TD (Dai 2013; Gagné 1999). Central to this discourse is the inquiry as to whether talent represents an innate trait that gradually unfolds over time (a Galtonian view) or if it is epigenetically shaped through extended engagement with sociocultural elements (Bronfenbrenner and Ceci 1994; see Gottlieb 1998, 2007 for a distinction between a reaction range view of human potential and a norm of interaction view). In seeking resolutions to these inquiries, it is prudent to embark on a preliminary exploration of whether talent has its underpinnings in neurophysiological substrates. The existence of sensitive periods in the formative years of human development offers valuable insights into the potential biological underpinnings of talent (Shavinina 2010). Sensitive periods delineate specific temporal windows during which developmental processes are markedly receptive to external stimuli (Bornstein 1989). Of particular significance is their prominence during early childhood, bearing considerable implications for the acquisition of certain skills, such as language proficiency, and other perceptual sensitivities such as pitch perception in music. This receptivity to environmental stimulation or intervention is notably heightened within well-defined age ranges (Obler and Fein 1988; Robinson and Jolly 2014). An exploration of the cognitive domains susceptible to sensitive periods stands to illuminate the inherent biological foundations that contribute to the emergence of talent (Simonton 1999). Furthermore, insights can be drawn from the examination of child prodigies and savants (Feldman 1986; Miller 2005; Obler and Fein 1988). For instance, when a child demonstrates an unusually strong musical precocity not commonly observed in peers (e.g., Wolfgang Amadeus Mozart), it suggests the presence of innate advantages that training alone cannot account for. By the same token, Savants exhibit exceptional capabilities despite low general intelligence (Charness et  al. 1988; Miller 2005), suggesting dedicated mechanisms, often attributed to biologically built-in (hard-wired) modular devices. Examining the biological origins of the extraordinary abilities of savants (such as drawing and calendar forecasting), alongside the cognitive limitations that prevent them from becoming true masters of their craft, offers a unique window for comprehending talent across various domains. This approach sheds light on how the brain handles and synthesizes information to accomplish intricate tasks (Miller 2005; Obler and Fein 1988, p. 281). It also helps us understand situations where embodied cognition excels and when symbolic representation and analytical thinking take precedence (Dai 2021). In addition to investigating and contrasting performance among prodigies, savants, and their peers, studies involving individuals with brain damages offer further insights into the interplay between brain activity and skill development. Those with brain damage may display diminished or enhanced abilities compared to their unaffected counterparts. Utilizing advanced neuroimaging techniques, research has

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yielded robust evidence regarding the relationship between brain structure, damage, and cognitive skills. For example, mathematical talent has been linked to bilateral cognitive function representation (Obler and Fein 1988). Certain mild developmental abnormalities can also lead to what Geschwind and Galaburda (1987) viewed as a pathology of superiority. For instance, some individuals with conditions like Asperger’s syndrome or dyslexia can exhibit remarkable talent, suggesting that nature sometimes compensates. Studies by Haier and colleagues indicated faster glucose consumption declines in those with higher IQ scores (Haier 2001; Haier et al. 2003), underscoring a vital biological foundation of talent: learning. Beyond the above, various physiological indications also underscore the biological foundation of talent. Certain phenomena are challenging to explain solely through environmental factors, such as sex distributions across different talent domains and the prevalence of left-handedness among mathematically talented groups (Benbow 1988; Obler and Fein 1988). Once again, the consideration of talent domains plays a pivotal role in comprehending the biological roots of talents. Domains such as sports, chess, and mathematics appear to heavily rely on physical or cognitive strengths and early development. This could explain why significant talent manifestations in these domains can be identified at relatively young ages (Feldman 1986). In conclusion, a range of methods can be employed to investigate the neurophysiological basis of talents. Traditionally, the giftedness and talent perspective posits that giftedness or talent potential is innate and pre-established; it becomes evident and is uncovered within the appropriate environmental context. This age-­ old capacity view (Galton 1869) has prompted the early identification of individuals with inherent talents in education. Similarly, it has driven the use of standardized tests like IQ tests for identifying gifted individuals. Nevertheless, while contemporary researchers acknowledge the neurophysiological foundation of talent, they contend that “nature” and “nurture” are closely interwoven (Dai and Coleman 2005). In essence, genetic expressions (nature) depend on the type of environmental stimuli one encounters. Concurrently, experiences and deliberate practice can enhance presumably “innate” capacities (Ericsson et al. 2005, 2007). A notable illustration is Schlaug and colleagues’ work on professional musicians (Gaser and Schlaug 2003; Schlaug 2001). They observed that individuals undergoing extensive and prolonged musical training displayed observable alterations in brain structure and function. These changes seemed adaptive, aligning brain functions with the specific demands of musical performance. This highlights how “nurture” can reshape “nature” and cultivate acquired talent into a “second nature.”

4.2.2 Sociocultural Foundation When Feldman (1974) introduced the term “domain,” he aimed to underscore that talents are intricately linked to sociocultural contexts. Recognizing talent within a specific domain is shaped by prevailing cultural values, beliefs, and practices.

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Tannenbaum (1983, 2000) categorized talents into scarcity, surplus, quota, and anomalous based on societal significance. (a) Scarcity talents: Individuals who reshape the world at its core through profound contributions, such as great thinkers and transformative social leaders. (b) Surplus talents: those who enhance the aesthetics and intellectual landscape of society, including artists, creative writers, and philosophers. (c)  Quota talents: individuals who fulfill the ever-shifting supply-and-­ demand dynamics of an economy through their specialized skills and expertise, such as physicians, educators, lawyers, engineers, and business executives. (d) Anomalous talents: those who may occupy the periphery in terms of cultural prestige and instrumental significance, yet exhibit exceptional mental or physical abilities, such as lightning calculators, those with photographic memory, or skilled computer hackers. Anomalous talents also include those conflicting with norms or exhibiting antisocial tendencies (e.g., burglars, hackers) which are marginalized, highlighting unequal recognition within a social-historical context (Csikszentmihalyi 1988; Tannenbaum 1986). To comprehend TD, it is imperative to investigate how diverse talents are recognized and valued within society, as cultural selection plays a pivotal role in directing resource allocation (Dai, in press). For the same reason, Csikszentmihalyi (1996) explicitly distinguished a field from a domain. A domain consists of particular goals to be achieved, tools and methods employed, and symbol systems and a body of concepts used for that purpose. In contrast, a field is the social organization of a domain, consisting of social institutions, norms and standards, and leaders and gatekeepers who decide on the rules of the game, so to speak. Thus, a field has its own culture and power structure in terms of what is valued, what is the modus operandi, and what is the code of conduct for its members. If domain implies internal rules governing a particularly human endeavor, field implies a set of culturally imposed “external” rules (Gee 2007), which are in some way arbitrary (i.e., involving preferences), held by its leaders and gatekeepers as “normal,” until a new generation of leaders decides to revamp what is considered paradigmatic (Kuhn 1962). Incorporating the concept of “field” into the discussion holds significant implications for TD. Previously perceived as inherent to the operation within a domain, the conventions governing TD within that domain are now recognized as institutionally imposed rules that can be modified. While the recognition of exceptional creative productivity or the identification of the “gifted and talented” was thought to be achievable through standardized procedures and criteria, disparities in opinions can emerge within a field. For instance, Vincent van Gogh’s paintings were not initially well received, illustrating how perspectives can vary. These disparities often stem from resistance to cutting-edge work by artistic or scientific establishments, as seen with the impressionist movement in art and quantum mechanics in science. In essence, the interplay between a domain’s internal structure, intrinsic to human endeavors, and the external structure of a field with its culture and power dynamics is complex. Hence, the notion of TD, particularly creative productivity, as a social process of transitioning from the periphery to the center (Kagan 2002) takes on heightened significance.

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The sociocultural foundation of talent also becomes more evident when we recognize that talent itself has a distinct social dimension. For instance, Sawyer’s investigations have explored the collaborative nature of various creative endeavors, such as Jazz and theater performances (Sawyer and DeZutter 2009; Sawyer 2014). Dunbar (1997) examined the collaborative aspect of scientific inquiry, which possesses its own cognitive structure involving countering confirmation biases and playing devil’s advocate during idea discussions. Since social interactions aren’t easily observed within laboratory settings, researchers must immerse themselves in the actual environments where creative productivity unfolds. Similar to Dunbar and Sawyer, through ethnography and discourse analysis, researchers meticulously observe, document, and analyze how social interactions contribute to creative ideation and productivity.

4.2.3 Cognitive-Affective-Conative Foundation The process of TD is intricately influenced by three key psychological components: cognition, affection, and conation. While the nature-nurture debate traditionally emphasizes genetic predispositions versus environmental influences, the biological foundation versus sociocultural foundation, it often overlooks the micro-level transactional nature of TD, embodied as cognitive mastery, enduring interest, and sometimes relentless persistence (refer to Chap. 6). In essence, when proximal processes (Bronfenbrenner and Ceci 1994) take a central role in human development, the cognitive-affective-conative psychological process, or more simply, a focus on human action itself, becomes foundational. Cognitive Dimension  An impactful contribution of expertise research (Ericsson 2006) is its reinstatement of the cognitive foundation of human excellence. Cognition goes beyond innate abilities to encompass domain-specific knowledge acquisition, skill enhancement, and utilization of learning strategies. It emphasizes active cultivation and expertise refinement through the accumulation of domain-­ relevant experience, extending beyond mere genetic inheritance. Cognitive processes have traditionally been investigated within cognitive psychology through experimental methods (Ericsson and Williams 2007). The core logic in this research involves controlled environments, enabling detailed observations of participants’ engagement in target activities and the cognitive processes driving behavioral or performance changes. Unlike creative productivity, which lacks clear consensus as an achievement, “reproducible superior performance” serves as a hallmark of excellence from which mediating cognitive mechanisms can be traced. While replicating a highly creative and impactful painting might be challenging in a controlled setting, replicating an excellent chess move based on a specific board situation is highly feasible (de Groot 1978). Empirical investigations have employed various methods. Post-performance retrospective interviews shed light on cognitive processes underlying performance, as seen in uncovering the cognitive mechanisms

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behind a move in a chess play (e.g., Sheridan and Reingold 2013). Additionally, the concurrent think-aloud verbal protocol offers real-time insights into ongoing thoughts as the performance unfolds (e.g., Ericsson and Simon 1984). A distinct cognitive phenomenon of talent is that much expertise and creative ideation have an intuitive or implicit basis. For example, professional gamblers in Ceci and Liker’s (1986) study and many other practical experts in natural settings (Salas and Klein 2001) were capable of manipulating many parameters in real time in a dynamic working setting. They likely have a mental model of how the game works. How do they develop such a mental model? Is the model implicit and intuitive (Dreyfus & Dreyfus, 1986), or explicit and analytically controllable or accessible to metacognitive awareness? It is likely that in practical domains involving situated actions (firefighters, computer troubleshooters), a significant portion of understanding and insight is embodied. This conjecture might lead to the conclusion that expert intuitions are acquired through direct experiences. However, many creative ideations in science and humanities involve intuitions (e.g., intuitions in physics or history) that are intellectual and abstract in nature and apparently do not involve embodied actions. Where do these intuitions come from? The German philosopher Immanuel Kant conjectured, in his Critique of Judgment (1790), that creative intuitions in arts and sciences must have an innate basis, thus implicating natural talent. However, one can also argue that creative intuitions or “sudden insights” are actually the result of many months and years of total immersion in a topic, with much conscious and unconscious prodding and searching (Miller 1996). This is key to unraveling the secret of TD. Regardless, this part of expertise and creativity may not be amenable to the think-aloud technique for soliciting critical information about the workings of the expert or creative mind. Conative Dimension  Conation, denoting a drive or inclination for purposeful action, goes beyond reacting to the environment by highlighting purposeful decisions and sustained goal-driven efforts. Research within the expertise perspective asserts that reproducible superior performance relies on deliberate practice (Ericsson 2001), underscoring conation through well-defined goals and persistent improvement efforts (Ericsson 2006). Deliberate practice is predominantly discussed in skill acquisition and professional performance domains, yet its role in fostering TD within production domains is equally notable: domains such as writing also demand substantial time for honing expertise (e.g., Kellogg 2006; Kellogg and Whiteford 2009). It’s worth noting that expert performance and contemporary creative productivity accounts (e.g., Glaveanu et al. 2013; Sawyer 2012) stand apart from the giftedness and talent perspective by accentuating the conative aspect of human excellence. They acknowledge that TD involves purposeful actions, perseverance in the face of challenges, and the conscious choice to engage in deliberate practice and skill refinement. However, accounts of creative productivity extend from deliberate practice in emphasizing the relentless pursuit of ideas and actions that can impact an audience and make a difference.

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Affective Dimension  In comparison to cognition and conation, affect taps into a more elusive realm of psychological processes: emotions and feelings. Galton’s (1869) definition of genius incorporates an affective element—zeal (or passion, in contemporary terms)—alongside capacity and hard work. Broadly speaking, the affective aspect of TD encompasses the entire spectrum of positive and negative emotional experiences. On the positive side, successful experiences or growing interests enhance the rewarding nature of TD. Conversely, effectively navigating challenges, setbacks, and stressful situations inherent in the pursuit of excellence determines the sustainability of TD over time (Dai and Speerschneider 2012). Considering affective factors in TD research is not uncommon (e.g., Gaudreau et al. 2009). Csikszentmihalyi et al. (1993) discovered that scientifically and artistically talented adolescents display distinct patterns of affect and motivation. Artistically talented individuals are drawn by the intrinsic appeal of art, while scientifically talented ones are more enticed by science’s instrumental value in unraveling the mysteries of the universe. These findings were partly enabled by an experience sampling approach they adopted to track the longitudinal dynamics of emotional changes (Csikszentmihalyi and Hunter 2003; Csikszentmihalyi and Rathunde 1993). This approach offers valuable insights into emotional fluctuations experienced during TD and their influence on personal pursuits. In performance domains like sports or the arts, similar emotional and affective aspects are present, albeit less explored by expertise research due to its technical focus on mastery. Even in controlled laboratory settings, real-time or pre-/post-test collection of emotional data during performance remains a viable option (e.g., Eaton et al. 2015; Wagstaff 2014).

4.3 Developmental Manifestations of Talent The manifestations of talent are diverse and multifaceted, encompassing both current individual performance and the potential for future eminent achievement. Researchers have introduced an age-based framework to comprehend the various dimensions of talent manifestations (Mayer 2005). Within this framework, talent potential is regarded as a crucial manifestation during the foundational years or formative phase of childhood. Furthermore, the transitional years of adolescence witness talent potential materialized through high achievements in chosen domains by age-appropriate standards. As individuals progress into advanced years of adulthood, evidence of high-level expertise and eminent achievements become proper standards of excellence. This age-based framework offers a nuanced and more precise understanding of how talent is displayed, highlighting the changing dynamics of its expression and growth. As a result, this framework allows us to consider the distinct circumstances and qualities of individuals’ talents throughout their lifelong journey.

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4.3.1 The Foundational or Formative Phase of Talent Development (in Childhood) The immediate phenomenology of talent during the early years mainly concerns the emergence of a range of talent potentials, a phase characterized by the delicate interplay between natural endowment and environmental affordances. From the seminal work of Witty (1958), we glean insights into the foundational aspects of talent that constitute evidence of talent potential during childhood. This developmental period often showcases talent through exceptional performance that stands out when compared to peers. It’s worth noting that a spectrum of talent expressions comes to the fore, with some children displaying remarkable academic abilities while others shine due to their unique artistic inclinations. Two notable research exemplars reveal what talent potential looks like during childhood. Winner (1996) conducted case studies of children with artistic inclinations. She characterized their most distinct characteristic as “the rage to master.” By keenly observing the experiences of these budding artists, Winner unveils the inherent yearning for mastery that serves as a precursor to their artistic brilliance. Kanevsky (1990) explored early indicators of aptitude through the investigation of problem-solving abilities in children with high IQs (140 or above). Through controlled experiments involving a task called Tower of Hanoi, Kanevsky demonstrated that exceptional problem-solving abilities can surface in children with high IQ, an early marker of talent in cognitive domains. Likewise, Russ (2014), through a protocol of “pretend play,” was able to capture a nascent form of imaginative talent in action. Conceptualizing talent assessment childhood in a controlled setting, akin to the “strange situation” for developing attachment theory (Ainsworth and Wittig 1969), provides a good way of thinking about alternative ways to the traditional paper-and-pencil test in which creative potential can be assessed.

4.3.2 The Transitional Phase of Talent Development (Typically During Adolescence) Navigating the transitional years from childhood to adulthood marks a critical juncture in the nature and evolution of talent. During this period, talent achievements can be viewed as milestone events as they mark a more distinct domain-specific trajectory. As individuals progress through adolescence, the transitional phase of TD typically witnesses an increasingly differentiated profile of strengths and interests, coupled with a clearer self-understanding regarding values and directions, as underscored by Gagne (2005) and Csikszentmihalyi et  al. (1993). The journey through adolescence entails a multifaceted interplay between formal education, exemplified by traditional schooling, and informal learning, which can play a pivotal role across home, school, and community (Barron 2006). This transformational phase truly marks the beginning of a long personal journey in life. A more distinct

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sense of self, more active personal initiatives, and a drive to improve and surpass oneself, all can be seen as milestone psychosocial events that can sustain TD (Subotnik and Jarvin 2005). Within this context, several notable research examples underscore the multifaceted dimensions of talent evolution during these transitional years. Lubinski and Benbow’s (2006, 2021) seminal studies mapped out milestone adolescent achievements in mathematical and verbal areas. Their investigations illuminate the patterns of strengths that serve as precursors to distinctive talent trajectories, unraveling the dynamic integration of aptitudes in charting particular developmental pathways. Barron (2006) extends our comprehension of what makes a good learning ecology for adolescents by looking into early adolescents’ interest-driven and self-sustained learning. Her work probes the intricate landscape where youthful passions converge with persistent efforts, culminating in an enriched understanding of how enduring interests fuel TD. Similar to Barron’s work, Duckworth’s (2006) pioneering exploration of “grit” introduces a facet crucial for transitional years: sustained interest. She accentuates the role of developing strong interests as a cornerstone of TD. Coupled with traits like passion, persistence, ego strength, and self-control, these factors empower individuals to overcome setbacks and challenges, propelling them toward long-term development leading to new levels of excellence. This interplay of enduring interest and resilience unveils the dynamic process by which talent not only flourishes but also triumphs over adversities.

4.3.3 The Advanced Phase of Talent Development (Typically Starting in Young Adulthood) This is a phase characterized by the dedicated effort to develop high-level expertise in performance domains and striving for creative productivity in production domains. Central to the concept of expertise is the recognition that it is a dynamic, ever-evolving construct, as opposed to a static endpoint. This realization prompts a departure from conventional notions of expertise and invites an exploration of the fluidity of adaptive expertise over time (Hatano 1988). It should be noted that creative productivity within production domains is always a collaborative endeavor of a community of kindred spirits, situated at the forefront of knowledge and technology. Here, practitioners engage in a collective pursuit that pursues cutting-edge innovation through collaborative discourse. Maximal adaptation to task constraints becomes pivotal, as does the quest to carve out a distinct personal niche for contributions within this vibrant landscape (Dai 2021, in press). Research exemplars within this realm further enrich our understanding. Gray and Lindstedt (2017) elucidated the plateau-dip-leap model of expertise development, reaffirming that expertise is a constant journey characterized by stagnation, deliberate trials and errors, and ultimate leaps to a new level of performance. Sawyer’s (2003) investigations into jazz musicians’ collaborative creativity, Shavinina’s (2004) exploration of

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Nobel Laureates working at the edge of chaos, and Kozbelt and Meredith’s (2010) inquiries into the realm of art all reveal the intricacies of talent in this phase.

Demo Study 1: Glaveanu et al. (2013) on Creative Action in Five Domains Source: Glaveanu, V., Lubart, T., Bonnardel, N., Botella, M., De Biaisi, P. M., Desainte-Catherine, M., … & Zenasni, F. (2013). Creativity as action: Findings from five creative domains. Frontiers in Psychology, 4, 40417. Description. Glaveanu et al. (2013) put forth an action theory of creativity that regards “creativity as action” and “creative work as activity.” They further employed this theory as a roadmap to probe and elucidate the mechanisms through which creativity manifests across diverse domains. This was achieved through interviews with acclaimed creators spanning five distinct fields: art, design, science, scriptwriting, and music. Methods. Glaveanu et al. (2013) carried out semi-structured interviews with 60 French creators from five distinct creative domains. These interviews encompassed insights from three primary dimensions: the creator, the creative work, and the creative process. Thematic analysis was employed to elucidate the characteristics of creative actions. Contributions. A noteworthy contribution of this study, serving as a type 1 research demo, lies in its consideration of domain-specificity. By delineating and scrutinizing creative actions across five distinct domains under a unified theoretical framework, the study effectively identifies both similarities and differences in the patterns of creative actions among these domains. This effort addresses a pivotal query within type 1 research: the inherent nature of talent domains. Furthermore, the study encompasses a comprehensive exploration of the sociocultural foundation. This is achieved by attentively examining the interplay between the creative experience, the physical environment, and the social milieu. Additionally, the cognitive-affective-conative foundation is also thoroughly addressed by investigating factors such as pre-action motivational aspects and the emotional experience throughout the entirety of the creative endeavor. These inquiries collectively contribute to an enhanced understanding of the foundational sources of talent. In addition, the study concentrates on immediate phenomena, portraying talent manifestation within creative productivity domains as a multifaceted and dynamic process—capturing the essence of “the continuous cycle between doing and undergoing.” This stands in stark contrast to oversimplifying the phenomenon into discrete components. Such an approach holds immense value for type 1 research, aligning with its objective of comprehensively grasping the essence of talent and the intricate process through which it unfolds.

4.3  Developmental Manifestations of Talent

Limitations. A primary limitation of this study stems from its reliance on participants’ memory and recollection, resulting in self-report data. While such data is acknowledged for its potential bias, a central limitation of this study as a demo of type 1 research, particularly, is the absence of direct observation of ongoing phenomena (e.g., micro-genetic analysis as per Siegler 1996). What exactly transpires during creative production was not documented (see Li et al. [2022] for an example of showing how data can be collected during the creative process). Additionally, while such a study can surely examine domain specificity and generality by comparing five distinct domains (art, design, science, scriptwriting, and music) and derive patterns from an inductive manner, the selection of these domains and the corresponding analyses can also introduce top-down guidance from a taxonomy framework. Reciprocal bottom-up and top-down iteration can eventually provide a viable characterization in the larger scheme of things.

Demo Study 2: Amsel et al. (2022) on Differences Between Lawyers and Psychologists Source: Amsel, E., Langer, R., & Loutzenhiser, L. (2014). Do lawyers reason differently from psychologists? A comparative design for studying expertise. Complex problem solving (pp. 223–250). Psychology Press. Description. Amsel et al. (2014) conducted comparative studies by comparing the organization of causal inference rules among four groups of people: experts in law (i.e., practicing lawyers and third-year law students), experts in psychology (i.e., psychology professors and Ph.D. students), undergraduate “novices” (i.e., students enrolled in first- and second-year psychology courses), and police officers (the control group). The goal is to investigate whether there exists a uniquely legal style of causal reasoning (i.e., deciding whether an event is causally related to an effect) among expert lawyers. Methods. A crucial methodological advantage of the study lies in its approach to comparing reasoning behaviors. Instead of solely contrasting the reasoning styles of expert lawyers with those of novice lawyers, the researchers conducted comparisons among experts from various domains, such as social psychology. This approach allowed them to discern whether a distinctive style of reasoning exists among lawyers, as opposed to simply determining whether expert lawyers employ superior reasoning strategies in comparison to novices. Experts in law and psychology are similar in that both groups received training in reasoning but they differ in the context of reasoning (legal reasoning or social scientific reasoning). On the other hand, police officers possess experience in evidence evaluation, akin to

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psychologists, and possess knowledge of the law, akin to lawyers, but they have not undergone training in either legal or social scientific reasoning. Contributions. By employing a comparative approach that considered both experts versus novices and experts from distinct domains, the researchers successfully discerned distinctive reasoning styles utilized by experts in various fields. Their study provides strong support for the notion that professional learning experiences can lead to the development of unique reasoning styles. This underscores the idea that experts from different domains not only possess varying content knowledge but also cultivate different modes of thinking, highlighting the domain specificity of their cognitive abilities. Limitations. The study is in line with the expertise perspective, utilizing a back-tracking approach to explore the mechanisms behind expert performances. However, it’s essential to acknowledge a limitation inherent in this design, as it cannot offer insights into the specific processes through which various facets of expertise are cultivated. Furthermore, although the study suggests the influence of domain-specific experience on the formation of distinct reasoning styles among expert lawyers, the causal inference needs further verification.

4.4 Recommendations This chapter has explored various facets of talent manifestations and their evolution across different stages of development. To further advance our understanding in this area, future studies can be directed toward three distinct dimensions:

4.4.1 Foundational Issues Worth Exploring This chapter has underscored several crucial foundational matters that warrant exploration. Broadly speaking, the chapter has discussed three fundamental issues: (1) what the essence of talents is, (2) what the possible sources of talent development are (i.e., the three foundations of talents), and (3) how we know if an individual has particular talents (i.e., talent manifestations across different life stages). Due to talent’s developmental and interdisciplinary characteristics, addressing these foundational questions is essential for propelling the field of talent development as a unified research domain.

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4.4.2 Methodological Approaches and Options Various methodological approaches can be selected based on the specific foundational research questions being explored. Approaches like field studies (e.g., Ceci and Liker 1986) allow for in-depth examination of immediate talent phenomena, while case studies (e.g., Knowles and Lorimer 2014) offer nuanced insights into individual talent development paths. Additionally, mixed-method approaches can provide a comprehensive understanding by combining quantitative talent potential data with qualitative personal experience narratives. Comparative designs, exemplified by Glaveanu et al. (2013), are significant as they enable a comprehensive perspective on talent development, analyzing differences across domains, age groups, talented individuals, and cultural contexts.

4.4.3 Addressing Research Questions Adequately As Ceci and Liker (1986) and Glaveanu et al. (2013) indicated, the efficacy of a foundational study lies not in providing definitive answers to the foundational question, but in generating novel insights and raising new questions. Future studies should aim to employ methods that enable the generation of new perspectives (e.g., a talent developmental perspective) on talent development. By collecting diverse and rich data (beyond intelligence), researchers can explore the complexities of talent manifestations across different contexts and age groups.

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Siegler, R. S. (1996). Emerging minds: The process of change in children’s thinking. New York: Oxford University Press. Simonton, D. K. (1988). Age and outstanding achievement: What do we know after a century of research?. Psychological Bulletin, 104(2), 251–267. Simonton, D. K. (1997). Creative productivity: A predictive and explanatory model of career trajectories and landmarks. Psychological Review, 104(1), 66–89. Simonton, D.  K. (1999). Talent and its development: An emergenic and epigenetic model. Psychological Review, 106(3), 435–457. Stanley, J.  C. (1997). Varieties of Intellectual talent 1. The Journal of Creative Behavior, 31(2), 93–119. Subotnik, R. F., & Jarvin, L. (2005). Beyond expertise. Conceptions of giftedness, 343–357. Subotnik, R. F., Olszewski-Kubilius, P., & Worrell, F. C. (2011). Rethinking giftedness and gifted education: A proposed direction forward based on psychological science. Psychological Science in the Public Interest, 12(1), 3–54. Tannenbaum, A.  J. (1983). Gifted children: Psychological and educational perspectives. Macmillan Tannenbaum, A. J. (1986). Giftedness: A psychosocial approach. In R.J. Sternberg & J. E. Davidson (Eds.), Conceptions of giftedness. Cambridge University Press. Tannenbaum, A. J. (1997). The meaning and making of giftedness. In N. Colangelo & G. A. Davis (Eds.), Handbook of gifted education (2nd ed.). Allyn & Bacon, Incorporated. Tannenbaum, A.  J. (2000). A history of giftedness in school and society. In K.  A. Heller, F. J. Mönks, R. J. Sternberg, & R. F. Subotnik (Eds.), International handbook of giftedness and talent (2nd ed.). Elsevier. Tannenbaum, A.  J. (2009). Defining, determining, discovering, and developing excellence. In J. S. Renzulli, E. J. Gubbins, K. S. McMillen, R. D. Eckert, & C. A. Little (Eds.), Systems & models for developing programs for the gifted & talented. Creative Learning Press. Wagstaff, C. R. (2014). Emotion regulation and sport performance. Journal of Sport and Exercise Psychology, 36(4), 401–412. Weisberg, R. W. (2006). Modes of expertise in creative thinking: Evidence from case studies. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance. Cambridge University Press Winner, E. (1996). Gifted children: Myths and realities. Basic Books. Witty P. A. (1958). Who are the gifted? In Henry N. B. (Ed.), Education of the gifted. 57th Yearbook of the National Society for the Study of Education: Part 2. Crossref.

Chapter 5

Type 2 Research: Differential Learning and Divergent Development

In talent development (TD) research, the debate surrounding nature versus nurture remains persistent (Howe et al. 1998). Extreme reductionist stances result in either an elitist nature viewpoint, suggesting that only a select few possess innate talent, or a populist nurture perspective, which posits that anyone has the potential to excel in any field. This chapter reviews various research methodologies that aim to elucidate the origins and progression of talent. It emphasizes the significance of Type 2 Research, which focuses on deciphering short-term differential learning and long-­ term developmental trajectories and pathways. Such research is pivotal for understanding the evolution of a distinct, personalized skill set (i.e., talent) within a societal framework. This approach diverges from the Cartesian dichotomous perspective that treats nature and nurture as separate entities contributing independently to TD (see Overtone 2014). Factors pivotal to TD encompass (a) initial variations in cognitive, affective, and conative aptitudes and dispositions, (b) comparative advantages (and disadvantages) relative to age peers, (c) social resources and support (including social privileges), and (d) social comparison and self-concept, both in terms of actuality versus ideality, serving as a motivational component. The task lies in delineating their impact on differential rates of learning (i.e., learning curves), asymptotic performance (i.e., when performance plateaus), and divergent developmental trajectories and pathways (Ceci and Papierno 2005; Dai and Li 2023; Simonton 1999). Such analyses shed light on why certain individuals achieve remarkable talent milestones while others do not. This chapter will discuss and evaluate a variety of research endeavors emphasizing interindividual differences in talent and TD. These encompass (a) behavioral genetics research, (b) gifted and talented placement-prediction research, (c) research on expertise development, and (d) research on modeling long-term differential patterns of stability and changes in talent domains. Insights developed through these lines of inquiry into important “components” of TD can inform more refined prediction models of TD and provide general explanations as to what are essential “traits” © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_5

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or factors (nature or nurture) responsible for high-level performance/productivity. Nonetheless, this chapter also underscores the constraints of such research as it typically falls short of explicating the developmental processes involved and underlying psychosocial processes and intrapersonal changes that ultimately specify how talent evolves and what drives the developmental process.

5.1 Differential Learning and Divergent Development: A Population-Based Perspective on Talent Development In the 1990s, expertise research flourished, notably emphasizing the pivotal role of “deliberate practice (DP)” in developing high-level expertise (Ericsson and Lehmann 1996; Ericsson et  al. 1993). This ushered in a wave of nature-nurture debates throughout the ensuing debates (e.g., Howe et  al. 1998; Ericsson et  al. 2007a) over the next decade on whether the prevalent talent account of human accomplishments since Galton’s (1869) notion of heritable genius is outdated and invalid (see Gagné 2009 for a rebuttal; see Papierno et al. 2005 for an interactionist response). The central issue of the debate was not the importance of DP but rather if DP alone was sufficient for achieving expertise. While proving the existence of pure “natural talent” devoid of experience and learning influences remains challenging, assuming its presence as a given is problematic (see Howe et al. 1998). Such a priori assumption of innate talent commits an error of reification, treating innate talent as a structural property of the person like a Japanese origami, only to be undusted and uncovered intact, free from the contamination of experiences. While Ericsson acknowledged the existence of individual variations in “natural talent,” the debate persisted on whether such innate capacities impose a definitive ceiling on talent achievement, as Galton proposed, or if they are merely signposted by measures like IQ (Gagné 2005; Ericsson et al. 2007b). To address this controversy, Shiffrin (1996) proposed a more testable hypothesis on the existence of talent by giving subjects a novel, difficult task in a controlled setting for a prolonged period of learning. Suppose the experiment identifies distinct variations in the rate of learning and asymptotic performance (i.e., the performance reaches a point of plateau or diminishing returns, despite the continued effort for improvement). In that case, the validity of the talent account is established. Although it is difficult to eliminate the confounding effect of motivation with the cognitive advantage, the rate of learning is relatively easy to ascertain in controlled or natural settings (Gagné 2005). However, pinpointing an individual’s asymptotic performance is often more elusive, except in specific domains like track and field or chess rankings (Elo ratings). Consider, for instance, a cohort of college-bound students taking the Scholastic Assessment Test (SAT), a consequential examination, multiple times. If their scores plateau or even decline slightly (potentially due to regression to the mean), can we infer that they have reached their peak abilities? Complicating matters, if certain students enlist the help of skilled tutors or invest more effort than their peers, can their plateaus attained truly be considered comparable?

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While technical challenges persist, the notion of differential asymptotes, as proposed by Shiffrin (1996), and the idea of long-term divergent developmental outcomes as indicators of asymptotic achievements (Lubinski and Benbow 2021) are more conducive to empirical scrutiny. It is also consistent with the developmental science focus, starting with McCall (1981), on developmental diversity (McCall’s Scoop Model of divergent mental development; also see Horowitz 2000 for a more balanced, sophisticated treatment of the topic). By looking at differential learning and divergent development, we are closer to the empirical basis of talent and TD and, therefore, more likely to understand talent in the social context in which it is observed. From a population point of view, it is important not to treat talent as an all-or-none phenomenon. Moreover, it is more productive to go beyond the nature-­ nurture dichotomy and focus on all factors, endogenous or exogenous, that facilitate or hinder TD. Also, we should understand talent as a fundamentally developmental phenomenon, epigenetically and contextually shaped (Gottlieb 1998), rather than presuming a priori its existence in a static, structural, and permanent form. This is how we can (combat) a natural tendency in folk psychology: reification, that is, taking something abstract as if it has material and structural existence, hence some explanatory power (Dai 2010).

5.2 Research Traditions in This Line of Inquiry Historically, a dialectic cycle took place in understanding differential learning and divergent development. Gifted and talented studies typically used the gifted-­ nongifted comparison paradigm to establish distinct characteristics of differential learning and problem-solving in favor of the gifted (Borkowski and Peck 1986; Jackson and Butterfield 1986; Kanevsky 1990). Subsequently, an alternative perspective on high-level performance emerged from the cognitive tradition, emphasizing deliberate practice and a decade of intensive study or training as explanatory factors (Ericsson et al. 1993). This was countered in more recent years by a plethora of studies that questioned the sole reliance on the deliberate practice account, introducing other factors such as personality traits and genetic predispositions (e.g., Hambrick et al. 2018; Gobet 2021; Ullén et al. 2016). Adding another dimension, longitudinal research that modeled enduring patterns of stability and fluctuations in talent domains among committed practitioners or professionals (like chess tournament players) offered fresh insights into how differential learning informs longterm developmental trajectories. This approach resonated with Shiffrin’s (1996) criteria concerning rates of learning and performance plateaus (Howard 2009). In the succeeding section, we will review various research traditions to show how each addresses the issue of what accounts for long-term success and accomplishments. The purpose is not to summarize findings and conclusions but to reveal the logic of their approach and, for that matter, whether they make a strong case from a developmental science point of view and how much they can inform and inspire practice.

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5.2.1 Behavioral and Molecular Genetics Research Behavioral genetics research frequently employs various twin designs to estimate the proportion of genetic influence on a developmental outcome relative to environmental influences. In areas like chess, music, and academics, substantial genetic contributions have consistently been observed (see Hambrick et  al. 2014 for a review). For instance, the heritability of rhythm, melody, and pitch perception stands at 50%, 59%, and 12–30%, respectively. Moreover, cumulative hours of music training explain up to 50% of the variance in these attributes (Hambrick et al. 2018). More recent genetics studies directly associated the genetic makeup (e.g., using genome-wide association studies or GWAS) with various phenotypes or developmental outcomes (reading fluency, intelligence, years of education, career success, and even good marriage; see Belsky et al. 2016). Based on the results of GWAS, genes that are associated with a particular phenotype are combined to form a “polygenic index.” How much this index can account for the variance in a developmental outcome is calculated as an index of heritability (i.e., the proportion of variance in an outcome attributable to genetic influences). On average, this heritability estimate is often much smaller than what is found by behavioral genetics using twin designs; the discrepancy raises the issue of “missing heritability” (Turkheimer 2011), which so far remains unresolved. Many conceptual and methodological challenges arise when attempting to directly connect genetics to specific developmental outcomes. A primary concern is the replicability of results, especially when these are based on “blind” searches for empirical relationships. If earlier efforts to identify candidate genes have largely been unsuccessful (as noted by Harden 2021), the reliability of such “blind” methods as GWAS is brought into question. From the vantage point of developmental science, the behavioral genetics approach—focusing on the statistical division of variance in developmental outcomes into genetic and environmental components— often results in statistical artifacts rather than generating substantive insights. By bypassing developmental processes, such results fail to provide an understanding of developmental shifts, often perpetuating highly reductionist perspectives on human development. Many scholars in developmental science have argued that the notion of heritability derived from these methods can be misleading (see Horowitz 2000; Lerner 2004, for a critique). In light of the developmental tenets discussed in the previous chapter, we argue that the dichotomizing genetic and environmental contributions are not tenable because both are intertwined and inseparable in a functional and developmental system. Although the findings implicate genetic sources of differential learning and divergent development in music, chess, academics, among other domains, the methods are not capable of yielding any insight as to how and when the genetic makeup influences relevant developmental processes and outcomes. In short, genetics research like twin studies or GWAS studies are not capable of tapping into developmental specificity, let alone developmental complexity. Genetic research can be more productive if studies of gene-environment or gene-intervention interactions

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(Dick et al. 2015) can show how certain genes can be “developmentally instigative” when activated in response to certain stimulation or intervention. For example, a heightened perceptual sensitivity to pitch or rhythm might be traced to specific genes.

5.2.2 Placement-Prediction Studies Placement-prediction studies serve as the foundational underpinning for gifted and talented education. This line of inquiry encompasses two distinct types of studies. The first emphasizes short-term differential learning processes and outcomes, generally conducted in a controlled environment with a designated control or comparison group. The underlying hypothesis posits that gifted and talented children exhibit a discernible advantage in learning and problem-solving (Borkowski and Peck 1986), an advantage that remains even when age-related maturity is accounted for statistically (Kanevsky 1990). Such comparative studies between gifted and non-­ gifted individuals have become a dominant research paradigm in the field of gifted and talented studies (see Dai et al. 1998; Jackson and Butterfield 1986 for reviews). The second type of studies focuses on long-term divergent developmental outcomes. This research trajectory began with Terman’s longitudinal examination of over 1500 high-IQ adolescents conducted during the first half of the twentieth century (Terman 1925; Terman and Oden 1947). This pioneering effort was succeeded by numerous longitudinal studies aiming to predict the long-term development and achievements of those demonstrating high potential, as indicated by IQ or cognitive abilities. Notably, the Study of Mathematically Precocious Youth (SMPY) stands out in this regard (Lubinski and Benbow 2006, 2021). Additionally, there are descriptive studies employing cross-sectional data to chart differential academic performance trajectories. For instance, Gagné (2005) utilized the developmental standard score norms of the Iowa Tests of Basic Skills (ITBS) and, with a cross-­ sectional dataset, discerned a fan-spread effect. This effect demonstrates that academic achievement from grades 1 to 9 reveals an expanding achievement gap within each age cohort. Such an enlarging disparity suggests the presence of a Matthew effect, which refers to a cumulative advantage resulting in an ever-increasing achievement gap over time. While the Matthew effect is commonly interpreted as a cognitive advantage (Ceci and Papierno 2005), there is an ongoing debate about whether such an advantage exhibits a threshold effect regarding long-term achievement. Specifically, the question arises as to whether there is a point of diminishing returns for this cognitive advantage. Subotnik et al.’s (1993) longitudinal study of high-IQ children suggests that the long-term prospects for high achievement among these children might not be as robust as one might expect. However, Lubinski and Benbow (2021) counter this by arguing, based on their own findings, that the proposed threshold effect (as conceptualized by models such as Renzulli’s “above-average abilities” notion, 1986) lacks empirical support. Controversies aside, the results from the SMPY study reveal that long-term divergent development and achievement are

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characterized not only by differing trajectories (e.g., career choices) between the exceptionally promising and the average groups but also within the high-achieving group, with some leaning toward technical domains and others toward the humanities. It is important to emphasize the significance of assessing intervening factors when using a longitudinal design to bridge the gap between potential and eventual talent achievement. For instance, in a follow-up to his original study, Terman (1925) retrospectively compared the top 100 most successful with the 100 least successful individuals from his high-IQ cohort (Terman and Oden 1947). This comparison highlighted key traits, termed “intrapersonal catalysts” by Gagné (2020)—traits such as unwavering goal pursuit and robust self-confidence—that set apart the highly successful from their less successful counterparts. Conceptually, this step taken by Terman and colleagues represents a significant improvement in research design, as it provides insight as to what mediates developmental changes and ultimate success. Incidentally, such qualitative comparison can be easily converted to a quantitative one today by creating metrics of these variables and subjecting them to a discriminant analysis. As mentioned earlier, the within-group comparison strategy was also frequently used by Lubinski and colleagues with the SMPY longitudinal data (see Demo Study 1 for details). With respect to mapping more detailed developmental trajectories, the Fullerton Longitudinal Study shows distinct strengths in using a multi-wave design, with characteristics annually assessed from infancy to high school (Gottfried et al. 2006). For example, they found children with high IQ to be consistently high in academic intrinsic motivation and high performing in school.

5.2.3 Expertise Development Research: Examining Proximal and Distal Determinants Ericsson and his team approached talent development from a distinct angle, emphasizing proximal determinants and mediating mechanisms of differential learning and development specific to elite-level expert performance. Utilizing a backward tracing method, they started by establishing “reproducible superior performance” typically monitored in controlled environments. They then connected this competence to specific performance components or “cognitive structures,” such as strategies experts use to navigate working memory constraints. Finally, they retraced the acquisition of these cognitive structures to deliberate practice (Ericsson and Williams 2007). Their argument against certain “talent accounts” posits that if proximal determinants sufficiently elucidate performance and the differential learning and development of expertise, then there is no need to attribute these achievements to distal determinants like differences in genetic makeup. However, a notable developmental gap emerges here. As Ericsson and Ericsson et al. (2006), being specialized in expertise research, are almost exclusively focusing on adult populations. The people they studied (e.g., Ericsson et  al. 1993) are already deeply involved in a domain, thus highly subject to self-selection biases (Sternberg 1996), which

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weakened their critique of talent accounts of high-level performance (Ericsson et al. 2005, 2007a). For example, evidence of between-group differences, such as differential learning rates and divergent developmental trajectories and pathways in formative and adolescent years, is beyond the expertise perspective’s purview. The key concept in Ericsson’s model is deliberate practice, which is, in contrast to ordinary practice, defined as “a ‘typically planned’ training activity aimed at reaching a level just beyond the currently attainable level of performance by engaging in full concentration, analysis after feedback, and repetitions with refinement” (Ericsson and Ward 2007, p. 349). It is a form of maximal adaptation to task constraints, especially those involving real-time execution of routines, by playing a technically difficult piece of music or performing a triple axel in figure skating (Dai 2021; Ericsson and Lehman 1996). It is worth noting that Ericsson and his colleagues’ research predominantly centered on the physical and cognitive facets of skill development. Nonetheless, control and achievement motivation play pivotal roles in maximal adaptation. Consequently, research programs led by Hambrick and Gobet, among others, sought a more comprehensive understanding of expertise development by looking into a wider range of factors, from genetic, and neuropsychological to personality. They integrated the cognitive and psychometric traditions in investigating the deliberate practice perspective and talent perspectives over the past decade or so and concluded that a host of individual differences (both cognitive and affective-conative variables) matter; they account for a significant portion of variances of expert achievement beyond what deliberate practice can explain (see Gobet 2021; Gullich et al. 2020; Hambrick et al. 2018; Ullén et al. 2016 for reviews; also see Kozbelt 2008, for a unique Darwinian vs. expertise perspective). While such integration of cognitive and psychometric traditions is promising in producing a better explanation of short-term differential learning and long-term divergent development, it runs the risk of reverting back to a trait account of talent development, as it still resorts to the analytic strategy of partitioning variance to separate components, rather generating process accounts by which how these “traits” operate is specified in situ. In other words, it does not address as well the issue of developmental specificity. Moreover, an exclusive focus on a single domain of expertise is purchased at the cost of ignoring the various pathways to excellence within and across domains. Besides, the notion of deliberate practice itself might apply to domains of performance better than domains of creative productivity (Dai 2021, 2024; see also Gullich et al. 2020 for a comparison between deliberate practice vs. deliberate play in sports).

5.2.4 Modeling Long-Term Talent Development Diverging from traditional methods that examine the distal and proximal determinants of expertise development, statistical modeling of talent trajectories (or curves) offers a formalized approach to estimating differential learning rates and peak performance or creative productivity. For instance, Simonton (1988) pioneered a model

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that charted the typical age trajectory of talent emergence and peak creative output and further delineated variations across domains. This model was enhanced by linking precocity (a marker of talent) with longevity (the lifespan devoted to a specific domain) and production rate (or productivity). This integration enabled Simonton to craft a cohesive theoretical framework of TD and creative accomplishment, which was further refined in a subsequent formal presentation (Simonton 1997). Details of this theoretical model can always be subjected to scrutiny, but what is worth noting is that such a model represents an ambitious attempt to build a nomothetic model with main parameters identified and estimated, granted that the realities are always messier than such a theoretical simplification. Compared to Simonton’s (1988, 1997) deductive approach, Howard (2009) employed a more descriptive and inductive methodology, focusing on talent development within the specific domain of chess. This choice is ambitious, given that chess is frequently regarded as the “drosophila” of challenging cognitive tasks akin to IQ tests. Moreover, the progression of chess talent benefits from objectively documented performance data, represented by the cumulative Elo ratings based on tournament outcomes at any given time. This design effectively addresses Shiffrin’s dual criteria within an ecologically valid context. The study was cast in the backdrop of the debate mentioned earlier between the natural talent and deliberate practice account of high-level expertise. The most relevant finding of the study is that precocity (age of entering the International Chess Federation), rate of expert acquisition (years and numbers of tournament games before gaining the title of chess master), and peak performance levels (the highest Elo ratings) are significantly correlated, suggesting the presence of a talent factor. Further analyses show clear evidence of asymptotic performance beyond which more practice and more games played simply do not produce gains in Elo ratings, another piece of evidence in support of the capacity argument (i.e., performance hitting the ceiling). In a subsequent study on practice effects, Howard (2012) identified the number of serious games played and study duration as significant predictors of skill improvement, even among elite players. This finding corroborates the deliberate practice theory of high achievement (see also Roring and Charness 2007 for a longitudinal analysis of chess expertise, though with a focus on aging effects). Comparatively, while Howard (2009) offers depth, Simonton (1988) provides breadth. Such developmental modeling, including dynamic modeling of simulated data (den Hartigh et al. 2016), presents a more holistic view of the interplay between short-term differential learning and long-term developmental pathways (see Demo Study 2 for a critique of Howard 2009, 2012).

5.2 Research Traditions in This Line of Inquiry

Demo Study 1: Lubinski and Benbow’s (2006) on the SMPY Longitudinal Study Source: Lubinski, D., & Benbow, C. P. (2006). Study of mathematically precocious youth after 35 years: Uncovering antecedents for the development of math-science expertise. Perspectives on Psychological Science, 1(4), 316–345. Description. The Study of Mathematically Precocious Youth (SMPY) was initiated by Julian Stanley at Johns Hopkins University in the 1970s and was subsequently led by Lubinski and Benbow (2006, 2021). This longitudinal study has spanned over five decades, with more than two dozen research publications emerging from it. The SMPY encompasses multiple cohorts of adolescents who underwent above-level testing with the SAT-Math and SAT-Verbal at the agof 13. To be included in the study, participants needed to score 700 or higher on either test, which is two standard deviations above the mean. Vocational interests and value orientations were also assessed at the outset. In subsequent cohorts, spatial ability was integrated into the evaluation process. The study employs a diverse range of measures to assess developmental outcomes, encompassing advanced degrees attained, scholarly publications, patents obtained, professional accolades, and psychological well-being, among others. Additionally, retrospective evaluations of educational experiences, such as STEM doses (Wai et al. 2010), were incorporated as indicators of pivotal intermediate experiences. Methods. The foundational design of the SMPY study revolves around the long-term prediction of elevated educational and career achievements, along with the divergent trajectories and decisions undertaken. Lubinski, Benbow, with colleagues and students, successfully showcased the predictive validity of placement based on three psychometric measures of abilities concerning an extensive array of domain achievements. Furthermore, group profiles constructed on these three ability measures, when paired with interests and values, were indicative of divergent educational and career paths. Contributions: The SMPY study’s breadth, encompassing multiple cohorts of academically gifted adolescents, a diverse set of predictor and outcome variables, and a follow-up spanning 50  years, is virtually unparalleled. Regarding differential learning and divergent development, the SMPY study substantiates several contentions: (a) Verbal, mathematical, and spatial abilities robustly underpin differential learning and divergent development. (b) The population-based distribution of talent across various domains can be ascertained. For instance, in domains such as theoretical mathematics and theoretical physics, “natural talent” appears to hold more significance compared to other areas.

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(c) Certain psychometric measures of cognitive and affective characteristics at the population level point toward high potential. Their unique combinations can be instrumental in predicting talent and career paths. (d) Employing above-level testing at the age of 13 for Talent Search seems to suggest an optimal period for the identification of intellectual talent, fostering timely educational experiences (See more detailed description in Chap. 2). In sum, the SMPY stands as a monumental study within the TD research domain, offering a compelling narrative of the structured patterns of talent development rooted in longitudinal data.

5.1  Limitations: Consistent with its initial conception and design, the SMPY offers a component model of TD. While it identifies core components, it does not delve deeply into developmental specificity and complexity. Inherent to its design, there are substantial gaps between the predictors measured at age 13 and the developmental outcomes recorded decades later. While some studies have probed intermediate developmental processes and changes (e.g., STEM doses), detailed process accounts remain largely outside the scope of such a macro-level study. Given that the psychometric predictors suggest distal influences, the resulting component model is primarily descriptive. Constructed post hoc, it does not provide causal evidence or possess robust explanatory power. This is where Type 3 research would pick up the slack.

Demo Study 2: Howard (2009) on Developmental Patterns of Talented Chess Players Source: Howard, R. W. (2009). Individual differences in expertise development over decades in a complex intellectual domain, Memory and Cognition, 37, 194–209. Description. Howard conducted an extensive study on chess players who were listed in the International Chess Federation (FEDE) starting in 1985. These players provided approximately a 20-year record of tournament games played, the timing of achieving grandmaster status (if attained), and constantly updated Elo ratings with each new game played. The research was framed within the ongoing debate between natural talent and deliberate practice as the key to high-level expertise. Chess offers a unique platform to explore this nature-nurture debate. As a board game, chess has its own rules and strategies and is less influenced by general world knowledge. Universally recognized as a cognitively demanding game, chess requires thoughtful move selection and the evaluation of alternatives. From a measurement perspective,

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chess boasts a unique rating system (Elo ratings) that provides finely calibrated performance levels not found in most other domains. These ratings enable the modeling of expertise acquisition rates and peak performance in a manner comparable perhaps only to certain solo sports. Methods. The primary objective of Howard’s study was to examine the game performance trajectories and levels of engagement in game playing for a select group of dedicated chess players with chess ratings of 2200 or above. While the research did not take place in a controlled environment, the structured nature of the tournaments served as an effective framework to test the contrasting hypotheses. In many ways, the study aligns with Shiffrin’s (1996) conceptual experiment, which aimed to trace learning rates and asymptotic performance as indicators of “natural talent.” To begin, Howard evaluated the attrition rate for all participants since 1985. He then sought to establish various indices that could potentially serve as indicators of “natural talent.” These indices included the age at which participants joined the Federation (indicating precocity), the duration and number of games played before achieving “grandmaster” status, and the highest ratings ever reached (denoting asymptotic performance). Notably, the study revealed that three elements—precocity, the speed of expertise acquisition (i.e., time taken to achieve the status of grandmaster), and peak ratings—were correlated and collectively loaded on a singular general factor. Subsequently, Howard plotted the performance of several player groups over time to outline the differential growth curves in chess ratings. Initial observations showed a modest rating difference between the top candidate group and their counterparts. However, this gap expanded over time and plateaued in about 2 years. This pattern suggests variations in the pace of expertise development and the eventual performance ceilings or maximum capacities attained by different players. In the final phase of his research, Howard analyzed the frequency of games played by individual players. This served as a validity check to determine if the frequency of game playing influenced either the rate of expertise development or peak performance. The results indicated that game frequency did not alter the previously identified patterns of expertise development (the reader is referred to Howard 2009, for more details of the study design and statistical analyses). Contributions. Chess serves as an exemplary platform for examining the rate of expertise acquisition and asymptotic performance due to its inherent ecological validity and the consistent, objective measurement of performance over time. The study design is optimally structured to evaluate the differences in growth curves, particularly in determining the extent of an individual’s potential in chess. Although many studies have employed IQ as an approximate measure of individual variations in intellectual potential or capacity, it often falls short as a robust predictor (Ackerman 1988; Ceci and Liker 1986). This limitation might be attributed to the generic nature of IQ tests. Early domain-specific achievements consistently prove to be superior predictors of

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subsequent accomplishments (Lohman 2005, 2009), and this study robustly reinforces this claim. In summary, the author adeptly navigated an intricate dataset derived from real-world scenarios, providing compelling evidence for the existence of “talent,” as manifested in learning rates and asymptotic performance (Shiffrin 1996). Limitations. When conceptualizing “talent” as distinct from deliberate practice, the author appears to advocate for segregating the variance in growth curves between the influences of talent and deliberate practice. This approach mirrors the objectives of cognitive-psychometric research (refer to the critique of component models in this chapter). However, what if talent is intrinsically intertwined with deliberate practice, manifesting as attributes like inquisitiveness, profound game insight, or analytical depth? The journey toward expertise inherently demands reasoning and problem-solving, with continuous problem restructuring (Saariluoma 1992). Under this lens, the rate of acquisition and peak performance might be interpreted as existing on the “edge of chaos” (Dai and Renzulli 2008) rather than being driven by an enigmatic force. A potential shortcoming of the study is the author’s choice to present raw, descriptive data. A more refined approach might involve the formulation of a theoretical model rooted in these observations or the modeling of developmental trajectories for specific subgroups, akin to what has been done by Simonton (1997) or den Hartig et al. (2016). Regardless, the talent factor does not have to be treated as if it is hiding in a black box.

5.3 Contributions and Issues Regarding Type 2 Research Researchers studying TD should recognize the unique strengths and limitations of Type 2 research, as defined in this chapter.

5.3.1 Contributions of Type 2 Research This line of research is significant because it sketches a nomothetic overview of talent distribution and potential personal and societal factors that contribute to varying talent trajectories. This is most evident in Simonton’s (1988, 1997, 1999) developmental modeling of talent achievement and creative productivity and in Lubinski and Benbow’s (2021) delineation of a blend of psychometrically identified cognitive and affective-conative factors responsible for numerous talent trajectories and pathways, utilizing the SMPY datasets. Both approaches operate under the presumption of differential learning rates and asymptotic performance or productivity. Such assumptions are bolstered by short-term, micro-level studies in controlled

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environments (e.g., Borkowski and Peck 1986; Steiner 2006; Haier 2001) and by a plethora of long-term developmental data (Gagné 2005; Lehman 2017; Murray 2003). It is essential to recognize that these findings offer a macro-level landscape of talent development and societal talent distribution rooted in population thinking (Lohman 2001). Their estimation of the parameters should be seen as a first approximation rather than a conclusion, subject to further empirical investigation. Many details have to be worked out, such as whether talent distribution in sheer numbers follows the Pareto Principle of 20/80 ratio or the more stringent Lotka curve (Murray 2003), whether talent distributions across domains will skew as a function of social-­ historical zeitgeist (e.g., science becoming more of a collaborative endeavor, or popular arts becoming more prominent than highbrow ones), or whether there are indeed threshold requirements for particular domains (e.g., chess, dance, or clinical psychology vs. experimental psychology; Simonton 2019). Regardless, population thinking in talent development is based on the economic assumption of scarce resources, in terms of not only “natural talent” but also social and cultural capital. Developmental science is fundamentally constrained by the sociology and psychology of human conditions. A major contribution of this line of research is that it turns an intractable, ideological debate between the “nature” camp and “nurture” camp into a set of tractable research questions in terms of short-term learning and long-term divergent development. By incorporating psychometric and cognitive traditions of research (e.g., Hambrick et al. 2014, 2018; Ullén et al. 2016), this line of research has broadened the scope of research on cognitive excellence of various sorts (e.g., expertise and creativity), with the potential for integrating intelligence and personality, an issue that has plagued differential psychologists and psychometricians for decades and has never been addressed to satisfaction in psychological research (Cattell 1971; Snow 1995; Sternberg and Ruzgis 1996; see also Collis and Messick 2001). By forging the integration of natural talent and deliberate practice accounts, they also help settle the nature-nurture debate in a way that makes any further dichotomous arguments (pro-nature vs. pro-nurture) moot and fruitless (Hambrick et al. 2018; Howard 2009, 2012; Lubinski and Benbow 2021). By integrating genetic, neural, cognitive, personality, and social factors, a more satisfactory data-model fit can be achieved (Ullén et al. 2016). In a broader context, this body of research is consistent with a developmental science view of talent, not as a static, unitary quality, but as manifested phenomenologically through differential learning and divergent development, which could be as dramatic as child prodigies (Feldman 1986), but may just be indicated by accelerated and divergent trajectories and pathways. Note that differential learning and divergent development can be demonstrated as quantitative variations in the rate of learning and accelerated development, as a sort of Matthew effect (Dai and Li 2023). But it can also be observed as qualitative differences in the directions these individuals are heading, for instance, indicated by their emergent interests and ultimate accomplishments (Lubinski and Benbow 2021), as well as cultural selection (Dai 2024).

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5.3.2 Issues on Type 2 Research To further the progression of this line of research, it is essential to address the following considerations and potential limitations. A. Well-structured versus ill-structured domains. Much of the research in this line of inquiry, particularly those tracing the long-term trajectories of TD in expertise, often exhibits a single-domain focus. This is typically centered on well-­ defined, skill-based domains, with notable exceptions like the works of Simonton and Lubinski. This trend might stem from the expertise research’s emphasis on a singular definition of excellence: “reproducible superior performance.” However, it is debatable whether creative productivity across various domains can consistently be “reproduced” in controlled settings. There are also domains, such as entrepreneurship or creative writing (Spiro and DeSchryver 2009), which are ill-structured and might not lend themselves easily to empirical scrutiny. Even within the realm of science, a career can have numerous unexpected turns. While scientific skills remain paramount, excellence in many scientific areas might not be rooted solely in skill but hinges on conceptual breakthroughs and the inception of novel ideas and systems. Consequently, a skill-centric model, like the one Howard (2009) constructed for chess, might fall short in elucidating scientific progress, making models like Simonton’s (1997, 2008) approach to scientific TD more fitting. Adding layers to this complexity, how does one juxtapose Chopin’s role as a performer against his stature as a composer? The former, seemingly, is more structured than the latter. Is there a unique developmental path for polymathy (Root-Bernstein 2009)? Additionally, even within a singular domain, such as basketball, there exist distinct roles (e.g., center vs. guard) with their unique threshold requirements. In literature, poets and novelists might exhibit differing creative life cycles (Simonton 2007). Addressing these queries will undoubtedly illuminate the vast diversity inherent in myriad talent phenomena. B. Reciprocation of cognitive and social advantages. While cognitive advantage is a well-established factor (Ceci and Papierno 2005), it also likely paves the way for accruing social advantages and privileges over time. This interplay between cognitive and social advantages can be envisioned as a bidirectional dynamic. Individuals with cognitive advantages are inherently predisposed to a more expansive worldview, which in turn might drive them to pursue a broader array of learning and developmental opportunities. Concurrently, these individuals are more apt to be socially recognized through a process of cultural selection, with their strengths and inclinations further bolstered by the availability of social opportunities and resources, such as access to specialized schools, participation in programs like the Science Talent Search, admissions to prestigious colleges, and opportunities in state-of-the-art laboratories. This phenomenon exemplifies the active or evocative correlation between personal attributes and environmental opportunities (Wachs 1992). In real-world settings, where many descriptive studies find their footing, it becomes challenging to assert that a long-term

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accelerated developmental trajectory is solely indicative of innate abilities. The individual differences discerned in a between-subjects design could potentially be intertwined with the advantages of unique opportunities, experiences, and possibly even social support. The Matthew effect, as highlighted by Merton (1969) in the context of scientific careers, underscores this reciprocity between cognitive and social advantages. In essence, the between-person research design inherently skews toward an individualistic perspective. C. Component-dominant rather than interaction-dominant accounts. Ullén et  al. (2016) pinpointed various personality and intelligence factors that play a role in high-level expertise, extending beyond the realm of deliberate practice. Nevertheless, the specific ways in which these factors bolster expertise, aside from deliberate practice, remain insufficiently detailed. This shortcoming prevents a genuine integration of intelligence and personality—where intelligent behaviors are adaptive, thereby reflecting personality in action—and the subsequent diverse developmental pathways. Such limitations can be attributed to the inherent constraints of correlational research that employs a between-person statistical design to pinpoint statistically significant effects associated with specific traits (Cronbach 1957). Echoing Snow’s (1995) quote introduced earlier in this chapter, the paramount objective should be crafting a developmental narrative that views the individual and the situation as a cohesive unit rather than separate entities. While some of the aforementioned studies (e.g., Howard 2009; Kanevsky 1990) adeptly situate their research in this context, numerous correlational studies propose individual difference variables without providing sufficient context, making it challenging to discern their function in distinct situational dynamics. D. Nonuniversal development. Differential learning and divergent development extend beyond mere quantitative considerations and should not be solely addressed through nomothetic modeling or experimental frameworks (e.g., pronounced learning curves, accelerated developmental rates, the Matthew effects, or asymptotic performance indicative of capacity constraints). While tracing talent trajectories through growth curves (e.g., den Hatig et al. 2016; Howard 2009) offers insights into varied rates of learning and the plateau of performance/productivity, it often overlooks the qualitative facets of changes and developmental discontinuities. Such approaches also display a bias toward quantitative justifications for excellence, like the hours dedicated to deliberate practice. In many domains, however, talent trajectories are inherently qualitative, with determining factors also possessing qualitative characteristics. For instance, pivotal decisions, like a transformative shift in life focus or an intensified understanding of a specific pursuit (e.g., Piaget’s career; see Gruber 1986), can significantly influence trajectories. At some developmental junctures, differential learning and divergent development take a qualitative turn, such as the emergence of novel interest patterns, strengths, and personal visions—referred to as characteristic adaptations—or an enlarged personal action space (Dai 2017). Feldman (2003, 2020) described these as nonuniversal patterns of development, often overlooked by the predominantly nomothetic, population-­centered methodologies in this area of research.

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Given these nuances, person-centric idiographic methods or inductive developmental systems (IDS; Wood 2014) may be more fitting than variable-focused nomothetic strategies, especially when exploring the idiographic complexity of differential learning or divergent developmental patterns, as emphasized by Binet. When the top-down, rule-governed, nomothetic perspective seems inadequate or lacks generalizability, a bottom-up, case-specific, idiographic approach can be employed to discern unique properties, such as the differing developmental trajectories observed in entrepreneurship as opposed to scholarship or artistic endeavors. E. Reductionism versus emergence. The nomothetic, population-centric quantitative methodology tends to gravitate toward reductionism, distilling intricate phenomena into more straightforward components for the sake of deductive reasoning and probabilistic modeling. When effective, such an analytic approach can be exceptionally potent, reminiscent of Newtonian physics. Yet, when the realm of developmental science embraces a more inductive and synthetic epistemological standpoint (Wood 2014), integrates a relational developmental system worldview (Overton 2014), or adopts a dynamic systems perspective (Lewis 2000), the notion of emergence (or emergentism, rooted in complex systems science) gains philosophical significance (Dai 2005; Sawyer 2002). From a methodological viewpoint, this approach remains skeptical of purely reductive analytic techniques, which often segment or generalize the variance of noteworthy outcomes such as learning rates or peak performance into various components, be they endogenous or exogenous (e.g., Hambrick et  al. 2018), as the resultant component models of TD would miss important developmental properties that are the result of the additive effects of all components but emerging from the interaction of these components. In essence, the developmental science’s emergence logic leans more toward an interaction-dominant than a component-­ dominant analytic framework (Hilpert and Marchand 2018). Conceptually, this reductive approach yields a central narrative of TD, suggesting that individuals exhibiting talent or precocity, mediated by education and motivation, stand a greater chance than their counterparts to attain advanced expertise and creative output (Dai et al. 1998; Gagné 2005, 2020; Lubinski and Benbow 2006, 2021). Conversely, emergence logic inherently emphasizes interactivity and epigenesis (e.g., Simonton 1999). It prioritizes the emergent properties of a developmental system at every stage. From this vantage point, the TD narrative emphasizes the significance of action or interaction (Glaveanu et al. 2020), highlighting the symbiotic relationship between human agency and the opportunities and limitations presented by a specific environment. This relationship continuously grows in complexity and interactivity (Bronfenbrenner and Ceci 1994; Dai 2017, 2021, in press). Dai (2010, 2018) categorizes the former perspective as “essentialism” and the latter as “developmentalism.” From the vantage point of these divergent epistemologies, we can more clearly discern the ontological underpinnings and the epistemological foundation of placement-prediction research, statistical modeling of TD, and the emergent psychometric-cognitive tradition. A hallmark of nomothetic models is their

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capacity for predictive modeling. However, they often fall short of delivering genuine developmental narratives that capture developmental nuance and intricacy. Such models predominantly focus on individual differences in the psychometric sense but not individuality. Without studying the developmental emergence of interest and identity and their stability and change, the attempt to integrate components of human intelligence and personality into a coherent account of human high performance will not succeed (see Ford 1994). In this sense, techniques of variance partitioning or building probabilistic models of prediction of learning rates and asymptotic performance are not sufficient. Taking a person-centered, idiographic approach becomes necessary, which is addressed in the next chapter (Chap. 6 on Type 3 research).

5.4 Recommendations The following steps can be taken to conduct Type 2 research more productively.

5.4.1 Step 1. Mapping Out Major Parameters Identifying domain × person × developmental stage/phase matrix and related differential learning/divergent developmental patterns. Developmental science, adopting a systems perspective on human functioning and development (refer to Chap. 3), underscores the need to concurrently evaluate multiple interacting factors within an ecologically defined system. Such interaction shapes specific phenomena associated with differential learning or divergent development. It is a misstep to assume that variations in learning and development stem solely from individual characteristics without accounting for the interplay with task variations, domain distinctions, and social contexts. Likewise, it is overly simplistic to believe these processes are purely quantitative, such as merely progressing at a faster rate. Feist (1998) distinguished between developmental patterns in artistic versus scientific creativity, noting unique expressions in each domain (also seen in Chen et al. 2021). There is room to postulate that differential learning or divergent development is contingent upon specific developmental stages or phases (Subotnik et al. 2011). This suggests that these trajectories need not solely manifest as learning ease or peak performance but might also reveal themselves through qualitative shifts, such as profound interest in a subject’s inherent logic or the innovative application of knowledge and skills. In general, researchers should pay attention to (a) domain differences, which can vary along many dimensions (see Dai and Chen 2014; Chap. 7); (b) person differences: strengths in math versus verbal (Lubinski and Benbow 2006), technical versus cultural (Ackerman 2003), instrumental versus intrinsic (Csikszentmihalyi et  al. 1993), working with things versus with people (Dai 2021); and (c) different developmental stages/phases: from receptive to

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productive, from acclimation to critical participation, from technical proficiency to finding one’s own voice (Dai 2021; Dai and Li 2023). At what junctures some survive and thrive while some break down or opt-out is also of theoretical interest (Dai et al. 2015).

5.4.2 Step 2. Negotiating a Good Nomothetic-Idiographic Balance by Which the Problem Chosen Can Be Tackled Properly A developmental science perspective means the researcher should be mindful of the methodology used, depending on the nature and scope of their inquiry. The researcher can move from parametric to nonparametric analytic strategies when homogeneity assumption cannot hold, and when multiple patterns are identified within the group. Developmental researchers often compare variable-centered versus person-centered approaches (Bergman and Magnusson 1997) and determine where a developmental phenomenon falls on the universal-unique continuum (Feldman 2003, 2020). Mid-range theory approaches are also an option (i.e., a focus on localized phenomena with a limited scope of generality or conceptual reach; Merton 1996; see Chap. 7). Some researchers might want to use Inductive Developmental Systems (IDS; Wood 2014) to frame their research on differential learning and divergent development. When developmental variance can be quantitatively scrutinized, capturing multiple data points or waves is always preferable. This enables the application of sophisticated analytical tools like Structural Equation Modeling to delineate growth curves (Willert and Sayer 1994). When dealing with qualitative and binary predictor and outcome measures (e.g., all or none, 1,0), survival analysis becomes an apt choice (Singer and Willert 2003). Yet, when the study of differential learning and problem-solving demands observation of both qualitative and quantitative distinctions, a mixed-method approach is recommended (e.g., Kanevsky 1990). In a nonparametric context, Lubinski and Benbow (2006) utilized verbal-math tilt to chart distinct talent trajectories and paths (e.g., humanities vs. math/science). For identifying qualitatively divergent paths, Ackerman and Heggestad (1997) employed cluster analysis, uncovering four distinct patterns of knowledge/skillsets, interests, and self-concept among college students. Such distinctively individual developmental trajectories, gravitating toward the unique end of the universal-unique continuum, may be overlooked when focusing exclusively on a singular domain. An overlooked aspect of research on differential learning and divergent development is the overarching influence of educational experiences. For instance, Goldstein et  al. (2017) examined arts education, emphasizing its wider developmental role beyond merely fostering artistic talent. Such a non-talent-centric perspective can be profoundly relevant for TD, as the intersections of one’s aesthetic, technical, and intellectual journeys can lead to unique combinatory advantages, often termed “polymathy” (Root-Bernstein 2009). Consequently, research design should be tailored to accommodate the phenomenon under investigation rather than the reverse.

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Consider Jean Piaget, who transitioned from biology to philosophy, eventually crafting his distinctive framework of developmental psychology. Similarly, Elon Musk’s ability to pioneer multiple innovative ventures highlights these “nonconventional” pathways to excellence. Such divergent developmental trajectories merit scholarly attention and require appropriate research methodologies.

5.4.3 Step 3. Determining Implications of Having Interactive Systems of Specific Endogenous and Exogenous Components: Beyond Dichotomous Thinking While the primary focus of Type 2 research aims to elucidate individual variations in developmental trajectories, particularly their potential for excellence in discernible human domains, it is imperative to also delve into the intricacies of developmental specificity. This includes considering the dynamic interplay between individuals and their environments, which in turn gives rise to emergent attributes and guiding principles in TD. Unless one posits that individual growth adheres strictly to universal laws, the very essence of developmental diversity suggests intricate patterns stemming from the interplay of internal and external forces. Such patterns cannot be purely attributed to these forces operating independently (Snow 1995). As a result, a dichotomous thinking that views personal and environmental influences as mere additive components should be challenged (see Papierno et al. 2005). Noteworthy is the fact that developmental heterogeneity is present both across individuals and within a single individual, manifesting as intrapersonal variability (Siegler 1998) and a spectrum of developmental possibilities. Therefore, researchers ought to approach generalizations with discretion, treating evidence-­ based assertions as context-dependent knowledge bound by specific social and developmental contexts and always open to further examination.

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Chapter 6

Type 3 Research: Intrapersonal and Psychosocial Processes and Changes

Aristotle once said: “Excellence is never an accident. It is always the result of high intention, sincere effort, and intelligent execution.” For any long-term talent development or TD, whether self-initiated or influenced by significant others, an individual must deeply engage with specific facets of the world. This engagement nurtures personal interests, which, in turn, cultivates a stable commitment. Such commitment is essential for honing expertise and fostering creativity within one or more domains. Throughout this developmental journey, various psychosocial factors—including courage, perseverance, personal identity, optimism, and risk-taking (Renzulli 2005; Subotnik et  al. 2011)—or, more succinctly put, grit (Duckworth 2016), become instrumental. These factors are particularly vital when confronting challenges or adversities, as they propel developmental changes. Such a transformative process merits in-depth exploration since it delves into the core issue of talent evolution (Dai 2021, 2024). Developmental processes often remain unaddressed in population-based nomothetic research, which operates on the assumption that individual experiences adhere to universal rules and regularities. Any deviations from these norms, if acknowledged, are typically considered non-essential. Notably, existing nomothetic models and theories do hint at developmental processes. For example, Gagné (2020) pinpoints “intrapersonal and environmental catalysts” as triggers or drivers of talent development (TD). Similarly, Ericsson (2006) recognizes the roles of motivation and temperamental factors in guiding deliberate practice (Ericsson et  al. 1993). However, they are just components to be reckoned with, not something growing out of one’s evolving individuality through social and developmental interaction. If many aspects of developmental diversity are truly “non-universal” (Feldman 1981, 2003) rather than following population norms and regularities, then they have to be uncovered by investigating particular cases of talent and TD as following its own logic. Indeed, Feldman’s (1986) case studies of child prodigies were an early attempt in this idiographic tradition.

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Type 3 research introduced in this chapter continues the line of inquiry of differential learning and divergent development (explored in Chap. 5), but with a methodological twist, in the sense of explicating proximal processes, not based on population-based estimates of parameters but inductively derived from the immediate phenomenology of TD. Because of the methodological differences, type 3 research is particularly focused on intrapersonal processes, changes, and transitions (e.g., make-or-break moments) at different critical junctures of individual development, in contrast to the strengths of type 2 research in identifying population-­ based parameters for divergent talent developmental patterns (McCall 1981). This chapter discusses and critiques a range of research programs on intrapersonal and psychosocial changes relevant to TD, from inclinations to deep engagement, from interest development to identity formation, and from competence to expertise to creative productivity (Subotnik et al. 2019). Moreover, this line of research will inevitably tap into one’s evolving individuality and selfhood in terms of developing personal strivings and finding one’s niche environment that fits one’s niche potential and valence (Werner 1967; see also Edelman 1995). It is this proximal aspect of TD, not some universal regularities, that can offer truly developmental explanations of characteristic and maximal adaptation to the highest level of challenges that stretches the human limits or generate creative solutions to important problems (Dai 2021). The term development in the developmental literature implies the evolving and gradual process of changes at a micro or macro level (Granott and Parziale 2002). These alterations encompass both incremental shifts and transformative ones that propel an individual to heightened levels of organizational intricacy. Intrapersonal and psychosocial processes and changes can be operationally defined as contextually observed real-time developmental transactions and temporal changes in some characteristics of the person, be it competence or inclinations. By “intrapersonal changes” we mean internal changes and transformations important for adapting to new levels of standards and excellence. By “psychosocial” we mean characteristics (e.g., grit) that are better understood as developed in particular social contexts (e.g., a new level of challenges in TD; MacNamara et al. 2008), rather than purely “endogenous.” This is a commitment to the principle of situating any changes in the relational developmental system (Overton 2014). As to why we insist on type 3 research as necessary, the answer is that only this type of research gives us clues as to what drives, regulates, and maintains TD in the first place (i.e., addressing the question of how, when, and where).

6.1 Rationale for Type 3 Research: Delineating Micro-Level Intrapersonal and Psychosocial Processes, Changes, and Transitions The continuities and discontinuities between type 3 and type 2 research can be summarized as follows: • Component models of TD derived from type 2 research only map out macro-­ structural properties (from different rates of learning to divergent trajectories and

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asymptotic performance) with little explicated about micro-level “proximal processes” (Bronfenbrenner and Ceci 1994) and developmental changes (Ericsson may be an exception in his elaboration on the effects of deliberate practice). At a minimum, a process model delineating intrapersonal changes would substantiate what the component models found in more detail and depth with additional insight. Nomothetic, population-based models of talent development frequently operate under the assumption of universal regularities, lacking the intricate and intimate insights that idiographic research can offer about the specific processes at play. In this regard, idiographic investigations have the potential to unveil facets of talent development that may elude the purview of nomothetic approaches. Essentially, individual-level narratives hold the capacity to yield more nuanced and possibly alternative models of talent development (Molenaar 2004). Nomothetic, variable-centered models of TD typically focused on competence development, such as rate of learning and asymptotic performance; the question of how talent develops and what drives the process is often underspecified beyond the effects of motivation, cognitive effort, and amounts of learning experiences. Idiographic, person-centered approaches are more apt to capture psychosocial processes such as evolving interest, identity, and commitment. This is why Renzulli (1986) focuses on dynamic information about task commitment and creative ideation while working on authentic tasks. As previously discussed, type 2 research frequently exhibits a preference for investigating clearly delineated domains, particularly those performance-based domains where stages of expertise cultivation and levels of proficiency can be readily identified and quantified. In comparison, idiographic research is well equipped to tackle TD “in the wild” (Hutchins 1995; Klein 1998), particularly with those “ill-structured” domains or tasks in which learning and training are not easily routinized and cognitive flexibility is important in creative problem-­ solving (Spiro and DeSchryver 2009). At a more theoretical level, in comparison with static component models yielded by type 2 research, type 3 research takes a more intimate, dynamic up-and-close look at real-time transactions or person-task, person-context interactions (see Kanevsky 2020 for the static-dynamic distinction). Using dynamic systems language, type 3 research is interaction-dominant (Hilpert and Marchand 2018), as compared to type 2 research, which is component-dominant, thus more capable of dealing with issues that emerge from real-time interaction between the person and the task and social environment involved.

For instance, the majority of longitudinal investigations into TD primarily address macro-level development, lacking the provision for capturing dynamic processes at every juncture. While their results are persuasive in terms of the long-term predictive capacity of individual components, they fall short in elucidating the real-­ time niche-picking processes. Although they recognize the significance of interest and intrinsic motivation (Gottfried et al. 2006), these studies lack a comprehensive model of how these interests actually evolve. Type 3 research seeks to rectify this

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shortfall by striving for a more profound comprehension of the phenomenon (e.g., ethnographic exploration of violin virtuosos; Wagner 2015). By introducing an interactionist perspective, type 3 research offers novel insights into the very nature of TD (e.g., see Glaveanu et al. 2013 for an action theory of talent and creativity). Taken together, we can argue that type 2 research never truly satisfies the criteria of developmental specificity, especially regarding (a) how talent develops and (b) when it develops; it also falls short of satisfying the criteria of developmental complexity regarding (a) emergent properties and new organization principles and (b) developmental transitions to more complex levels of interaction. Thus, type 3 research is a necessary next step.

6.2 The Structure of Type 3 Research and Three Principles The type 3 research is structured in the following manner: (a) situates TD in a personal and social context, (b) explicates the connections the person makes to the world through TD, and (c) specifies the developmental process of such engagement, involving emergent properties such as interest and self-identity as the person adapts to a TD situation (e.g., making a transition from interested exploration to committed TD). In other words, TD is not treated as merely a technical issue of how superior performance is attained (Ericsson 2006); rather, it involves choices and adaptations that carry personal meaning and cultural significance (Bloom 1985). Type 3 research follows the following three principles:

6.2.1 Principle 1: Situating TD in a Personal and Social Context to Reveal the Developmental Importance of TD to the Person as Well as the Social Institution Involved TD conceptualized within the realm of individual growth diverges from formulations within educational and training contexts. For example, a common stereotypical portrayal of TD is: Identified Talent +10 Years of Dedicated Work = High-Level Excellence in a Domain However, such a formulation predominantly represents an institutional standpoint. When applied to individual development, it presents a limited perspective of TD, focusing solely on its technical aspect, while disregarding the underlying motivations and intentions that prompt an individual to invest a decade in dedicated efforts and pursuits. Furthermore, the formula works better in domains wherein procedural and technical competence (e.g., mastery of fingering in piano playing or making triple axles in diving) figure prominently in performance domains, that is, involving real-time execution of routines and skills under time pressure. This formulation of

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expertise does not work as well for production domains wherein quests for meaning (e.g., scientific inquiry), explorations of new forms of expression (e.g., music composition), or solving technical and practical problems (e.g., exploring innovative approaches). For instance, the artistic techniques of Vincent van Gogh might be deemed insufficient according to institutional criteria, yet it is precisely due to his technical “awkwardness” that a distinctive style of artistic expression, characterized by his signature brushstrokes, emerged as a result. TD research should not be limited to domains characterized by established and clearly delineated developmental trajectories, often institutionalized (e.g., structures facilitated by music conservatories or structured sports training programs). Instead, it should encompass a broader spectrum of relatively less explored developmental paths and trajectories that have also culminated in exceptional outcomes (e.g., entrepreneurship and creative writing; Piirto 2004). In the real world, individuals may traverse developmental pathways that align harmoniously with institutionalized routes, while others forge their distinctive and unique trajectories. Regardless of the scenario, even performance-oriented domains such as sports, dance, and chess hold both personal and social significance. Failing to scrutinize this facet leaves explanations for attained excellence, be it attributed to inherent talent or deliberate practice, insufficient and lacking in comprehensiveness.

6.2.2 Principle 2: Identifying the Connections the Person Makes to the World Through TD Every Step of the Way, and What Drives and Regulates TD in a Social and Personal Context Type 3 research focuses on what Bronfenbrenner and Ceci (1994) called “proximal processes,” referring to real-time, enduring transactional experiences with objects, people, and symbol systems in one’s immediate environments that have developmental significance and consequences. Such a process-focused approach often focuses on microdevelopment, documenting developmental events and changes in weeks or months (Granott and Parziale 2002). The unit of analysis is not the person per se, as is the case with the between-person design in most type 2 research, but person-in-action-in-context in the spirit of relational developmental systems (Overton 2014). The scope of investigation should be commensurate with the targeted phenomenon in question (Cairns et al. 1996). In type 2 research, the driving forces can be statically represented as “catalysts,” relative to the population norm. Type 3 research, in contrast, has to contextualize these driving and regulatory forces as operating in real-time in situ to foster and strengthen engagement and participation; these driving and regulatory forces themselves (e.g., interest and identity) are seen as evolving and changing (i.e., dynamic rather than static) vis-à-vis new affordances and challenges. What is important is documenting not just between-person differences but intrapersonal (within-person)

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changes over time, so that explanations are truly interaction-dominant, rather than component-dominant (Hilpert and Marchand 2018). Talent development involves personal development or evolving individuality (e.g., a unique skill set, a value orientation, and various psychological buffers in the face of setbacks and hurdles). This personal side of TD is closely associated with the social contexts in which talent development is embedded. For example, what Duckworth (2016) identified through type 2 research as “grit,” under the microscope of type 3 research, is psychosocial in nature; that is, it is not a static personal trait that works across situations, but a state-space of an enduring process of interest development and motivated self-regulatory control in context (Subotnik et al. 2011). Understanding social contexts in terms of the perceived importance of tasks and domains in question gives us a better sense of the personal side of the TD story, such as the story of grit.

6.2.3 Principle 3: Specifies Developmental Changes (e.g., an Emergent Interest) and Transitions (e.g., From Interested Exploration to Committed TD Effort) over an Extended Period of Engagement or Proximal Processes In type 2 research, developmental outcomes are often characterized as reaching a definitive end state (e.g., advanced degrees earned, patents attained, critically acclaimed works, social accolades). For type 3 research, a developmental change or outcome can be new structural and functional properties (e.g., new personal visions and strivings), and new organizational principles governing one’s further interaction with the environment (e.g., success defined by intrinsic value rather than by others). In this sense, developmental outcomes in TD refer to any consequential and enduring changes, not only in one’s structure of competence and performance but also in one’s personal visions and goals, perseverance in the face of adversity, and action plans for future development and sustainability of a particular line of work in the long run (Dai et al. 2015). A particularly important developmental change or outcome is major developmental transitions, an issue rarely considered relevant by population-based research, but nonetheless quintessential for TD research. For example, Dai and Li (2023) postulate three transitions as essential in TD: transitions from receptive knowledge to productive use of knowledge in problem-solving, from spontaneous characteristic adaptation to more purposive maximal adaptation, from competence-­ driven motivation (e.g., getting better at something) to meaning-driven motivation (e.g., getting to the bottom of an issue). Developmental transitions indicate developmental discontinuities and qualitative changes that alter the nature of TD, for better or for worse. However, it has received relatively little attention in TD research.

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Sum-Up  In summary, the aim of type 3 research is to elucidate the social-cognitive and psychosocial factors that propel and govern TD, elucidating the shifts and ­transitions within the individual’s journey, as they surmount diverse challenges and progress to heightened levels of organizational intricacy at distinct developmental crossroads.

6.3 A Capsule Review of the Past Research To illustrate, we organize a review of type 3 research into three main categories: (a) competence development, (b) interest development, and (c) identity-related development. We can view these three lines of development as parallel and reciprocal.

6.3.1 Competence Development Three genres of inquiry concerning competence development can be identified, largely differentiated by whether a study adopts a differential, cognitive, or sociocultural model of competence development. The first genre of research on competence development follows the nomothetic assumption of precocious domain-specific cognitive development. For example, based on the neo-Piagetian notion of “central conceptual structures,” (Porath 2006, p. 146), Porath and colleagues traced several of lines of cognitive development in foundational domains (Dai and Chen 2013): language use and story writing (McKeough et  al. 2006), numerical conceptual structure (Okamoto et  al. 2006), spatial-artistic representation and narrative structure (Porath 2006). The emergence of exceptional domain-specific competence in formative years seems to suggest an innate basis of domain competence in conjunction with more general intellectual precocity (Loewen 2006), consistent with neo-nativist conception of child development (see Hirschfeld and Gelman 1994). In a similar vein, Bamberger’s (1986) research also attempted to integrate the Hirschfeld Piagetian notion of concrete vs. formal operation into an understanding musical development of talented teenagers in terms of the transition from an intuitive mode to an analytic mode of functioning with music. The second genre of research concerning competence development delves into cognitive and motivational processes for explanatory purposes. In essence, this line of research is not that different from the expertise perspective introduced in Chap. 5. Domains under investigation also go beyond foundational domains to encompass a wide range of real-life tasks such as race track gambling (Ceci and Liker 1986), chess (Saariluoma 1992), and academics (Simonton 2019). The main concern of this line of research is not to trace superior performance to some innate cognitive advantages, but to show the essence of TD as the development of a particular way of engaging, representing, and modeling the workings of a domain (Dai 2024), or,

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simply, modus operandi. Ceci and Liker (1986) showed how professional gamblers were cognitively fine-tuned to specific task constraints in gambling tasks; the process is both cognitive and motivational in achieving a dynamic person-task fit (i.e., adaptation). Vicente and Wang (1998) and Klein (1998) advocated an ecological view of expertise development as a process of perceptually tuning into affordances and goal-related constraints in a domain, which can be subject to micro-genetic studies (Siegler and Ellis 1996), dynamic systems analysis (Fischer and van Geert 2014), or field research (Salas and Klein 2001). Using the grounded theory approach, Dai et al. (2015) found different Cope-and-Grow patterns among a cohort of early college entrants in a highly demanding STEM program. This more nuanced approach helps the researchers formulate the concept of characteristic adaptation and maximal adaptation as central to TD (see parallel accounts in Subotnik and Jarvin 2005 for the musically talented at Juilliard). A third genre of research on competence development frames the issue of competence development as a form of legitimate peripheral participation (LPP; Lave and Wenger 1991; see also Barab and Plucker 2002) and cognitive apprenticeship (Rogoff 1990, 2003), which can be further traced to Vygotsky’s notion of zone of proximal development (ZPD; see Dai 2020) and the scaffolding, and the action theory of TD and creativity (Glaveanu et  al. 2013; Ziegler 2005). Naturally, the topic involves design-based research that attempts to structure teaching-learning along a particular pathway or sequencing (e.g., increasing complexity), ushering the learner into increasingly more advanced topics, skills, concepts, and theories (Collins 2006). Hay and Barab (2001), for example, compared and contrasted two summer camps for advanced learning in science, one organized around legitimate peripheral participation and the other around constructivism. The authors identified similarities and distinctions between the two camps with respect to ownership, authenticity, power, and task structure. More pertinent to the learner side of the process, using a cognitive apprenticeship framework, DeVos et al. (2017) studied participatory experiences of doctoral students in STEM areas that led to either successful completion of doctoral programs or attrition. By merging the framework of advanced learning structures with the lived experiences of learners, the significance of specific participatory structures becomes evident.

6.3.2 Interest Development This aspect of TD research focuses on how interest develops and persists. Interest development is fundamentally a niche-picking adaptive behavior aiming to make personal connections with the world that would benefit one’s long-term development. Barron (2006) studied a group of adolescents whose emergent individual interest in technology led to a developmental pattern of self-sustained learning over the years (see Demo 1 for details). Barron’s learning ecology explanation of the interest-driven, sustained learning is in contrast to a more psychological explanation offered by Duckworth (2016) in terms of grit, the single-minded perseverance of

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interest as the key to long-term success and excellence. Duckworth broke down grit into several components, such as passion, persistence in the face of adversity, and self-control in the midst of competing attractions and temptations. Rooted in ECT (Dai 2021, 2024), interest development is a process of finding a task and social environment that fits one’s niche potential and niche valence in the midst of competing attractions and priorities in one’s life. Interest development also indicates a narrowing down of possibilities: certain developmental paths open up and others gradually shut down. Research on interest development typically focuses on how a situational interest becomes a more stable personal interest over time (Hidi and Renninger 2006). Interest itself can go through qualitative changes from perceptual to intellectual. For example, one’s interest in biology can be initially thematic, based on more superficial features (e.g., the drama of life forms), and eventually becomes discipline-­ based, with more intellectual depth (Alexander 2004). Interest development can be divided into exploratory and dedicated phases in TD. To illustrate, using a person-­ objects-­contexts framework, Akkerman and Bakker (2018) studied intrapersonal patterns of interest development and found more intrapersonal heterogeneity than commonly believed; that is, there are always simultaneously pursued interests, and interest development is more dynamic and less linear and orderly as we typically assumed (Hidi and Renninger 2006). We can consider this pattern as prevalent in the exploratory phase of TD, followed by a more single-minded pursuit (Dai 2021). Alternatively, multiple interests can also indicate a creative potential (Runco 2010) leading to polymathy (Root-Bernstein 2009). The distinct person-centered intrapersonal approach used by Akkerman and Bakker (2019) is highly instructive in demonstrating the advantages of a person-centered approach as compared to variable-centered ones. The former excels in capturing the contextual and dynamic facets of person-environment interaction and interest development. Instead of portraying interest as an immobile construct, this approach illuminates interest development as the establishment of personal connections with the world’s distinctive opportunities and prospects. The patterns of engagement that authors identify as “interest” are likely influenced by real-time personal assessments, whether related to self-efficacy or the intrinsic significance of the task at hand (Dai and Li 2023).

6.3.3 Identity Development and Commitment A focus on identity development and commitment naturally follows interest development. If an interest is object-focused, a personal identity is a reflective stance as to what the object or activity means to the person. In other words, identity creates a purpose and commitment from a functional point of view. Identity development, including crystalizing experiences (Freeman 1999), and changes in added importance of a line of work (Bloom 1985), have a social-contextual component that is not well integrated in type 2 research with a focus on predicting differential learning and divergent development. Person-centered research better captures the social and

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developmental underpinnings of identity formation. With a sample of world-class musicians, MacNamara et al. (2008) tapped into developmental transitions a group of elite musicians made to a full-time music education and music career). The researchers identified a set of what they called psycho-behavioral characteristics of developing excellence (PCDE) that helped them weather through the challenges of making such transitions. Such emergent psychosocial qualities (Subotnik et  al. 2011), easily neglected by type 2 research, were redeemed in such a phenomenological study (see Demo 2 for details). Although type 3 research is by and large person-centered, rather than variable-­ centered (quote), there are exceptions. Cribbs et  al. (2015), for example, investigated a set of variables, math self-concept, social recognition, and math identity, with a large sample of mathematically talented college students. They found significant correlations between these variables, suggesting that a math identity is perhaps contingent on a strong math self-concept and social recognition for math achievement. Thus, a person-centered study can be compared with a variable-centered study to corroborate their findings and reveal their respective advantages and weaknesses from a methodological point of view. Instead of making such causal ordering as Cribbs et al. did, a person-centered approach would highlight the reciprocation of competence development, interest, and identity development in the context of social support and recognition. The same can be said of interest/self-efficacy development (Armstrong and Vogel 2009; Lubinski 2010). The state vs. trait interpretation of “intrapersonal and environmental catalysts” (Gagné 2020) depends on the methodology that either taps into macro-level stable traits or into the micro-level emergence (see Demo 2). However, only person-centered or idiographic approaches are capable of investigating “proximal processes” (Bronfenbrenner and Ceci 1994) head-on. Demo Study 1: Barron (2006) on Interest-Driven, Self-Sustained Learning Source: Barron, B. (2006). Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human Development, 49(4), 193–224. Description: With a case study of three teenagers, Barron (2006) looked into their interest development and self-sustained learning in technology that crossed the  boundaries of home, school, and community. The study is guided by three conjectures: (1) there are many ideational resources in one’s surroundings that can spark and sustain interest in learning; (2) an interest-based learning activity can sustain itself albeit constrained by time, freedom, and available resources; (3) interest-driven learning activities are boundary-crossing and self-sustaining. These conjectures were then subjected to substantiation with the case studies. Along the way, the author also identified five self-initiated learning processes. The relevance of the study to TD lies in the fact that all three teenagers attempted to har-

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ness technology to build a long-term, career-oriented personal ambition (the first one learned web design for enhancing personal creativity, the second one learned programming to promote her artwork, and the third one learned computer technology more extensively, with a likely trajectory of becoming an expert). Methods: Given the broad argument for interest-driven, self-sustained learning across settings, the methods involved can be described as grounding the well-formulated arguments and conjectures in the case materials. The design of multiple interviews with three teenagers to generate case portraits was based on the understanding of human individual development as a result of micro-interactional processes across short-time frames and across multiple settings (what Bronfenbrenner and Ceci 1994 would call proximal processes). Thus, the three teenagers were interviewed twice in middle school, with a one-year interval. In one case, parents were also interviewed. The author was apparently able to collect highly detailed information about what transpired in their lives between the first and second interviews. The author further elaborated the design as capturing micro-genetic processes as well as activities with meso-genetic (extended activity) and ontogenetic (biographic) timescales. Contributions: A general impetus of the study is to make a case for an ecology of learning beyond the school wall or formal education. Interest-based self-sustained learning highlights the role of personal agency as well as supporting others and resources, including but not restricted to school teachers. Such self-initiated and self-sustained learning across (what can be called experience-producing and experience-organizing; Dai and Renzulli 2008) is an ignored but very essential part of TD, even within a learning institution (an academy or music conservatory). In terms of the nature of interest the three teenagers displayed, which sustains their learning, it has to do with the empowerment of technological tools to effect changes and enhance personal agency. One way or another, they found a way to incorporate technology into their niche potential and personal strivings. As for the role of significant others, the three teenagers did not work solo but their interest is initiated or sustained by interaction with parents, peers, and teachers, among others. Hilpert and Marchand (2018) advocated a kind of interaction-dominant theoretical explanation based on time-intensive and relation-intensive data. This study points in this direction. Limitations: From the perspective of TD, there are many questions unanswered by the study (rightly so because TD was not the focus of the study). The first question is prevalence: How many middle school students develop such an interest or passion for technology mastery? Because there is a wide range of interests that teenagers differentially pursued, demonstrated by the three teenagers in the study can be seen as characteristic adaptation of

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some but not others, given the encouragement, support, and resources they were exposed to. The second question follows the first one: Compared to some who are drifting along without developing a purpose, what characteristics and social conditions make self-sustained learning viable beyond interest? The preponderance of the TD literature suggests affective processes such as interest are important but conative and volitional processes also matter in sustaining a personal striving, especially when things get more challenging and one is in the coping mode (see Demo 2).

Demo Study 2: MacNamara et al. (2008) on the Psychology of Developing Excellence Source: MacNamara, Á., Holmes, P., & Collins, D. (2008). Negotiating transitions in musical development: The role of psychological characteristics of developing excellence. Psychology of Music, 36(3), 335–352. Description. This is an interview study of eight world-class musicians on their lived experiences of negotiating two transitions in TD: transition to full-­ time music education and then entry into the music profession. The main goal of the study was to a) identify the main challenges of making a transition as perceived by the musicians and b) identify self-reported behaviors and skills associated with the challenges. Methods: The study is distinctly person-centered, with a focus on the musicians’ two important career transitions as occasions to study the process of TD, akin to using life tasks (Cantor) and current concerns (Little) as person-­in-context approaches in personality research. The phenomenological nature of data as lived experiences from the subjective viewpoint of the person involved makes the study interpretive in nature; the analysis of the challenges as perceived and psycho-behavioral skills and characteristics as reported by the interviewees is based on this “first person” perspective. Content analysis is inductive in the sense that codes and major themes were allowed to flow bottom-up from the data. However, part of the data analysis is deductive in the sense of using an existing conceptual framework to impose upon the data to see the extent to which the coded data match a set of a priori constructed concepts. Together with previous research, this study should be seen as one step of a bottom-up and top-­ down iterative process of building a conceptual model of how the transitions are negotiated in musical TD. Contributions: Compared to component models, developmental specificity (where, what, how, when) is better articulated and documented, situated in

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specific TD junctures, dealing with specific challenges, related them to specific behaviors of coping and adaptation, and internal psychological perturbation and struggles, similar to what Subotnik et  al. (2011) called psychosocial skills in dealing with challenging circumstances in TD. On the one hand, many psycho-behavioral characteristics (e.g., levels of dedication, perturbed self-beliefs) the authors found parallel those in type 2 research. On the other hand, what they conducted is situated analyses, what they found is more than corroboration in that these constructs reflect the adaptive process of changing oneself or changing the environment to achieve a dynamic person-environment fit, rather than static traits. For example, the commitment and determination are made in the midst of financial constraints and juggling several things, and self-beliefs (and self-­ doubts) became “current concerns” under the condition of constant feedback and evaluation on their musical performance and improvement. To enhance the rigor of such a qualitative, interpretive research, the authors placed their study in the larger context of previous research to show how this study adds to the understanding of transitions in TD. Limitations: As a phenomenological study, the focus is to understand the phenomenon of transitions in TD thoroughly, while leaving other issues to the background. For example, from this study, we don’t witness many documented intrapersonal changes, though it was alluded by those responses to the delineated challenging circumstances (e.g., perturbed self-beliefs and how confidence is restored in terms of realistic self-evaluation). Because there is no frame of reference for comparing these musicians and their counterparts, we don’t know how critical these adaptive responses (the author called them psycho-­behavioral characteristics of developing excellence or PCDE) are in carrying on their aspiring career successfully (such comparison makes these characteristics essential in Terman’s study). Of course, we can assume that being elite musicians, they all cope well, but we should also diverge, with some keeping up successfully and others wearing down (Dai et al. 2015).

6.4 Recommendations Section 6.2 lays out a general structure and three principles used to guide type 3 research. Section 6.3 delineates three lines of research—tackling competence, interest, and identity, respectively—though the three dimensions should be seen as intertwined rather than separate. The following is a more detailed structure and steps of type 3 research.

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6.4.1 Step 1: Framing and Structuring a Type 3 Research Study Situating the three developmental dimensions delineated earlier in the person-­ domain-­context matrix, we can locate important factors to consider when inquiring into intrapersonal changes. Illustrated in Fig. 2.1, type 3 research primarily engages with the interaction of the person, domain, and context horizontally (contextually), while concurrently exploring the sequential progression of three developmental processes vertically (intrapersonal changes over time), which may initiate with either competence or interest contingent upon the dynamics of individual, domain, and social variables. In the work of Ullén et al. (2016), numerous individual difference variables were examined. However, within the context of type 3 research, a comprehensive understanding of how individual function in real-time interaction with tasks can only be achieved by a micro-level investigation in situ. A person-centered approach means that the person is not treated as a list of variables but holistically, as an open and adaptive agent when interacting with task affordances and demands with feedback and feedforward processes that inform the person how things are going. A domain in that sense is a set of affordances and demands; affordances evoke the person’s desires and feelings and allow them to express themselves and exercise their agency. Demands impose new challenges on the person, sometimes putting the person in a coping mode and other times energizing the person to overcome the hurdles. As demonstrated in Table 6.1, when an individual engages with a domain, they navigate not only task affordances and demands but also a social context that partly defines the structure and purpose of the tasks involved. In this sense, a child is not just playing Pokemon for fun but also gaining acceptance and respect from peers. By the same token, Elon Musk is not just building cars and rockets or the neural-­ computer interface (neural link) for fun but to make a difference in the world (i.e., his many acts are both intrinsically meaningful and instrumentally significant). This part of TD is often neglected by a skill-based approach to TD. Competence-Interest-Identity Development in the Context of Person-Task-­ Social Context Interaction  It is likely that competence, interest, and identity Table 6.1  A matrix of developmental processes and personal, domain, and social contexts for type 3 research Process/context Competence development Interest development Identity development

Person Domain Aptitudes and dispositions Task affordances and demands Depth or breadth of The personal understanding’ levels of significance of the engagement work involved Levels of commitment and Efficacy and agency dedication for making a difference

Social context Dynamic fit with the environment Intrinsic and instrumental value in the larger social context Carving out a unique personal niche

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development are reciprocal rather than hierarchical; that is competence can lead to interest and identity, and a strong sense of identity (e.g., a strong sense of destiny) will enhance interest and further facilitate competence development through committed and intensified efforts. This developmental process is likely further enhanced by landmark events (getting recognition in a science search or publishing an article as a high school student; see Feist 2006). However, we know very little about how these processes work with specific domains and how they vary with different domains. We also don’t know much about how they work in different social contexts (e.g., in different cultures).

6.4.2 Step 2. Paying Attention to Three Facets of TD When studying developmental processes and changes beyond mapping out developmental diversity in terms of social distributions of talent, there are at least three aspects to be addressed: (a) developmental timing, (b) developmental specificity, and (c) developmental complexity. Developmental timing means influences of certain factors, conditions, and processes are time-contingent. For example, Cho and Campbell (2011) found that family involvement in Korean adolescents engaging in Science Olympia was sustained throughout high school, whereas the same involvement for non-Olympians decreased. The new insight achieved by this study is that sustained parental involvement, especially in high school years, may be critical for high-level excellence (Science Olympia). From a relational developmental system perspective, sustained parental involvement can be attributed to the appraisal of their children’s potential for further development in science talent. For later development beyond high school, college may play a more important role. Developmental timing may also be related to important milestone changes in talent manifestation occurring in the early years (Bruce et al. 2013) or the emergence of a particular domain interest (Akkerman and Bakker 2018). For this matter, study design has to tune into the time-sensitive nature of the developmental processes and transitions. Developmental specificity refers to detailed, grounded accounts of specific developmental processes and changes at a micro-level of TD. For example, Glaveanu et al. (2013) looked into five domains of creative domains with extensive interviews with talented individuals in these domains. They identified different patterns of person-task interactions, which justifies a refined explanation of how scientists work as compared to talented designers or artists. Larson (2012) compared the professional development of entrepreneurship and that of artistry, which reveals distinct transitional issues, with the former redefining the social role when promoted to a top executive position, whereas the latter searching for a creative voice (see also Zimbleman 2011). More generally, developmental specificity speaks to the importance of using an idiographic approach to map out what, when, how, and where of intrapersonal and psychosocial changes.

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Developmental complexity concerns characteristics of talent development that involve dynamic interactive conditions and properties, not easily succumbing to a simple logic of causation. For example, Cech (2013) studied how engineering students adopt four professional identity traits: problem-solving prowess, technological leadership, managerial/communication skills, and social consciousness. These four indicators of professional identity predicted students’ intentions to remain in engineering. This study shows that talent development in engineering is more like the development of a complex set of skills, dispositions, and values. Evolving complexity also speaks to the fluid nature of individual development. As discussed earlier, Akkerman and Bakker (2018) studied the interesting development of school-age adolescents from a person-object-context perspective and found it to be nonlinear and more fluid and dynamic than often expected, just like some individuals could hover around several domains (e.g., Da Vinci or Musk) rather than sticking to a single domain of human endeavor. Compared to a nomothetic, trait approach to interest (e.g., Armstrong and Vogel 2009), the “up-close” idiographic investigation reveals the complexity of a more genuine dynamic process of TD. It is worth noting that the predictive studies in type 2 research have suggestive value when predictive factors are sequenced temporally to reflect the developmental unfolding of certain mediational processes, such as intelligence (abilities), education (Belsky et al. 2016), and interest (Lubinski and Benbow 2021), which often are used jointly to predict differential talent trajectories (humanities vs. sciences). As discussed earlier, however, micro-level scrutiny can produce more refined predictions of developmental changes and transitions. This step is essential to move forward to the next level of TD research, which is type 4 research, discussed in the next section.

6.4.3 Step 3. Developing Methods and Designs with Attention to the Timescale and Social Scope of Interaction A diverse array of methods can be employed, contingent upon the study’s nature and the research questions at hand. These methods span qualitative, mixed methods, and quantitative approaches. Qualitative methods: In general, well-contextualized research can also contribute to insights into how intrapersonal or environmental forces or “catalysts” (to use Gagné’s term) work every step of the way to propel TD. Various qualitative methods have been used for developing detailed accounts of micro-level intrapersonal changes, such as biographic studies (Gardner 1993; Gruber 1981), case studies (Feldman 1986; Dai and Li 2023), interviews (Bloom 1985; Dai et al. 2015; MacNamara et  al. 2008), and ethnographic studies (Coleman 2009; Wagner 2015). Typically, models of developmental patterns are generated inductively or abductively (e.g., Bloom 1985). Type 3 research is by nature personcentered rather than variable-centered; furthermore, an intimate up-and-close

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look at social and developmental interactions with a task environment has to serve as the empirical basis for generating such patterns. In this regard, a pure nomothetic, hypothetical-deductive method based on abstract, logical reasoning, which may work in physics, is simply not up to the job of understanding the evolving complexity of human development. Mixed methods: A person-centered design can always involve quantitative indexes, which can be used to build a behavioral and psychological profiles of the person or group in question, a practice quite common in developmental science approaches (Bergman and Magnusson 1997). Whenever quantitative data are used to make a case, some sort of local norms (high, medium, low in the group) can help determine the importance of specific characteristics. Another way of using quantitative measures in type 3 research is to mix quantitative measures of progress or trajectories and qualitative data (such as interviews and case methods) to facilitate interpretation of the progress and trajectories. Quantitative methods: Up-close look at the micro-level processes can also use quantitative longitudinal design. For example, competence, interest, and identity can be operationalized, quantified, and measured at multiple developmental points, and understanding of the person-domain-social context interaction serves as a background, and how competence, interest, and identity reciprocate can be mapped out using cross-panel analysis, time-series analysis, or single-subject analysis (Manolov and Moeyaert 2017). Note that such a design should always integrate between-subject and within-subject statistical designs to facilitate a dynamic, temporal, and developmental interpretation of the data.

6.4.4 Step 4. Interpretation and Articulation of the Significance Type 3 research has a strong qualitative component and thus heavily relies on proper interpretation. Biased attention in data coding and interpretation is always a pitfall to be avoided, and disconfirming evidence should not be ignored. Given its idiographic nature (studying particulars), a distinct danger in type 3 research is making unwarranted generalizations. For example, findings in music research should be considered as bound by the musical and at most artistic contexts. Conversely, idiographic research is in a better position to identify different patterns of development across domains or individuals. For example, Glaveanu et al. (2013) found that interaction patterns in five creative domains have their distinct domain-specific characteristics, thus refuting the argument that there is a single mechanism of creative cognition leading to creative productivity (Finke et al. 1996). A caveat is that even with a generated account that is compelling and well grounded, due to the idiographic nature of type 3 research, generalizations, if any, should be tentative and inclusive until sufficient evidence points to the same direction with convergent patterns. One way to test generalizability is to use the findings from type 3 research to

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shed light on type 2 research. The logic is that type 2 research is often population-­ based, and when type 3 research corroborates what is found in type 2, some degree of generalizability is validated.

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Chapter 7

Type 4 Research: Developing Proximal Prediction Models

Drawing on the research discussed in Chaps. 5 and 6, which provides a comprehensive understanding of interindividual differences in learning and divergent talent development (TD) trajectories (Type 2 research) and intraindividual developmental changes responsible for different pathways and developmental transitions (Type 3 research), this chapter (Chap. 7) focuses on Type 4 research. Type 4 research aims to construct prediction models that utilize time-sensitive, developmentally calibrated constraints crucial for successful transitions to a new level of excellence and accomplishment. To achieve this goal, methods such as survival analysis (e.g., Habicht 2022; Moulds et al. 2020; Pion et al. 2015; Smith and Weir 2022), decision tree analysis (e.g., Formenti et  al. 2022; Güllich et  al. 2019), and mathematical modeling (e.g., Simonton 1997, 1999, 2005) can be employed to make more accurate predictions for specific TD outcomes. Type 4 research serves as a crucial link between Types 2 and 3 research, which delve into developmental research (such as use-inspired basic research in the Pasteur Quadrant), and Types 5 and 6 research (centered on applied research in the Edison Quadrant), where the emphasis is on practical real-world applications integral to the process of human development. Both Type 2 and 3 research shed light on the significance of specific components and life experiences, either as pivotal achievements or as psychosocial milestones, as well as developmental constraints such as the onset of biological readiness and domain-specific experiences. These factors greatly influence the likelihood of successful transitions to higher levels of excellence. Both Type 2 and 3 research shed light on the significance of certain components and life experiences as critical milestones (either achievement or psychosocial) or developmental constraints (e.g., the onset of biological readiness and domain experiences) that influence the probability of successful transitions to a new level of excellence. Building upon the insights from Type 2 and 3 research, Type 4 research aims at developing more precise and refined prediction models. These models are more “circumscribed” and “refined” compared to distal prediction models, because they focus more on short-term predictions in a contextualized manner, allowing for the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_7

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monitoring of developmental progressions and the implementation of appropriate timely provisions and interventions. Unlike Type 2 and Type 3 research, which primarily seek to understand nomothetic or idiographic patterns and mechanisms responsible for divergent talent trajectories and pathways, an essential objective of Type 4 research is to validate the predictive efficacy of more circumscribed models. In other words, given a set of psychosocial and achievement milestone events, along with possible distal developmental constraints, Type 4 research assesses the likelihood of a successful transition to higher levels of excellence and TD. By effectively validating these predictive models, Type 4 research directly contributes to talent identification and the implementation of interventions in Type 5 and 6 research. Professionals and educators can leverage the insights into an individual’s current developmental stage, as well as the support from their environment and sociocultural resources, to forecast their likelihood of achieving success at the next level of excellence. Consequently, they can provide timely support and interventions to facilitate successful transitions.

7.1 An Introduction to the Basic Principles of Type 4 Research Feldman (2003) proposes the universal-unique continuum of human development as a fundamental guide for research in the field of TD research. In Type 2 research, the focus is on differential learning and divergent development, operating under the assumption of measurement continuity. This approach relies on standardized measurements for consistently predicting short-term or long-range talent outcomes for individuals (e.g., Lubinski and Benbow 2006). However, when TD is seen as a manifestation of developmental discontinuity that deviates from the norm (i.e., leaning toward the end of uniqueness), as Feldman (2003) described, the measurement continuity assumption no longer holds. For instance, in domains such as chess, two individuals with equal initial talent potential (say, having identical IQ scores) might undergo entirely different courses of skill development due to varying life experiences, such as the availability of mentorship and family support, as well as their levels of interest and persistence. In such cases, it becomes necessary to identify qualitatively different, domain-specific, and context-dependent predictors capable of foreseeing success and failure along particular talent trajectories and pathways. This recognition of developmental diversity and multifinality calls for more ideographically developed predictors (e.g., PBDE; MacNamara et al. 2008; see Chap. 6). These predictors should be designed to align with distinct trajectories of talent development, taking into account the specific domain, individuals with unique developmental backgrounds, and the timing of their development. The preceding chapters have demonstrated that Type 2 research (Chap. 5) attempts to map out population-based parameters systematically and often universally valid basic components that aid in predicting and explaining macro-level,

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long-term, population-wide differential development of excellence. Furthermore, Type 3 research (Chap. 6) aims to track more micro-level social and developmental interactions, offering insights into the step-by-step progression toward specific talent achievements. Types 2 and 3 research sometimes create a nomothetic-­idiographic tension. For example, Type 3 research tends to use individual-level constructs such as personal strivings (Emmons 1999) and crystalizing experiences (Walters and Gardner 1986), as well as individual-level developmental patterns that are beyond the purview of nomothetic (universal) assumptions and conceptualizations in Type 2 research. Expanding upon the two research traditions mentioned earlier, this chapter delineates a type of research that seeks to strike a balance between the nomothetic and idiographic, macro and micro approaches. Balance can be achieved by constraining its conceptual scope and focusing on a more circumscribed class of phenomena within a specific line of TD (domain and sociocultural contexts; e.g., predicting the probability of success in becoming a professional musician or a biologist in the United States as compared to China). By restricting the generalizability of its predictions, this type of research would carve out a niche for itself by improving model specificity in TD. Although a well-grounded nomothetic model of TD can  afford predictions in terms of general population-based distribution of talent trajectories as Lubinski and Benbow’s (2006) SMPY longitudinal research demonstrates, the long-range predictions from such Type 2 research can fall short of developmental diversity, specificity, and complexity of TD phenomena involved (Chap. 3), such as the influence of life circumstances, which may alter life trajectories and render theoretical predictions less effective. Conversely, Type 3 research, with its focus on micro-level processes, can become too nuanced to offer a more broad-based understanding of main developmental markers, milestone events (e.g., PCDE; MacNamara et al. 2008), or the most important conditions for successful transitions or achieving new levels of excellence. It is also worth noting that Type 2 research is more descriptive, and Type 3 research is more explanatory in nature, revealing underlying processes. In comparison, Type 4 research is more functional and modest in terms of offering explanations in a more circumscribed manner. However, it is more informative to practitioners with respect to practical implications and applications by integrating insights from both macro- and micro-level research while recognizing the complexities and nuances of individual development underlying TD. As previously introduced, micro-level developmental diversity can reveal distinct personal goals, strivings, jagged profiles (Rose 2016), and even idiosyncratic affective-conative inclinations within a homogeneous group of talented youths (Lubinski and Benbow 2021). As a result, a theoretical prediction model that fits well within a specific talent domain or niche may not apply effectively to another. Merton (1996) coined this kind of research as a middle-range theory approach, tackling circumscribed TD situations or phenomena with a constrained conceptual reach. While investigations into interindividual differences in talent and talent development (Type 2 research) can provide insights into significant macro-level determinants (e.g., high IQ) or distant developmental milestones (e.g., early engagement in serious chess or piano performance) crucial for a specific talent

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development trajectory (as discussed in Chap. 5), the study of micro-level psychosocial processes and intrapersonal changes (as outlined in Chap. 6 and Type 3 research) can furnish more immediate explanations for why some individuals successfully navigate their transitions or pathways to excellence, while others do not, or opt out (see references to Howard 2009 and MacNamara et al. 2008). Consequently, both Type 2 and Type 3 research can contribute to the middle-range theory approach embodied in Type 4 research. In summary, the goal of Type 4 research is to construct effective domain-specific prediction models guided by Types 2 and 3 research, attuning to specific domain, social, and developmental contexts, thus capable of providing more refined predictions for important developmental changes and transitions deemed crucial for long-­ term TD success. Technically, this type of research would identify a set of time-sensitive, developmentally well-calibrated milestone events and constraints responsible for success or failure in making transitions to higher levels of excellence. When constructing a model, it is crucial to consider both the statistical characteristics of the model (such as variable features and statistical analysis methods) and the specific problem to which the model will be applied (involving our conceptual understanding of the talent being studied). The former relates to whether the model can produce a good data-model fit. The latter influences whether we can interpret the results with high confidence (i.e., validity). For Type 4 research, we need to consider what to use for prediction and determine appropriate indicators for TD outcomes. Furthermore, when conceptualizing talent from a developmental perspective, the models we build should also reflect developmental characteristics, considering factors such as the timing of a particular event.

7.2 Successful Transitions at Critical Junctures of Talent Development Life-course processes involve two key themes: trajectory and transition, as highlighted by Elder (1994) and further expounded upon by Elder and Shanahan (2007). Talent trajectories refer to patterns and directions of developmental changes that delineate the extended competence development within and across domains, along with interest and identity development (see Chap. 6). The notion of developmental trajectories has played a pivotal role in the realm of developmental science, offering valuable insights into the evolution of individual abilities and competencies, dispositions, and values over time (Ackerman and Heggestad 1997; Wai et  al. 2009). These trajectories are frequently identified and clarified using statistical methodologies like latent class analysis, which efficiently encapsulate the intricate developmental pathways individuals follow (e.g., Castejón et al. 2016). It should be noted that developmental trajectories based on statistical analyses are descriptive in nature because they only represent developmental patterns that have happened instead of

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what is going to happen (Nagin and Tremblay 2005). In that sense, Type 4 research cares more about the prognostic function of a prediction model, with more proximal concerns than long-term prospects; for example, it is more interested in whether a child who demonstrates high potential in music would likely succeed with the prospects of moving on to formal training, rather than whether the child will eventually become a successful musician 20 years later. In contrast to trajectories, transitions are crucial moments embedded within these developmental trajectories and pathways. These pivotal transitions, analogous to entering adolescence or leaving high school, occur within the broader context of one’s life path, with some doors gradually opening and others gradually closing (Silverstein 1988). Typically, with transitions to higher levels of talent pursuits, one’s life options become narrower and fewer. Such transitions in TD represent non-­universal developmental progressions, signifying intrapersonal shifts that indicate qualitative changes. These transitions can encompass significantly huge improvements in skill levels (e.g., a novice painter who progresses from struggling with basic sketching techniques to confidently capturing intricate details and textures in their drawings) or shifts in a person’s relationship within a particular talent domain, such as significant interest development and identity-related growth (Sosniak 2006). When delineating the critical transitions in talent development, the notion of a “domain” frequently plays a pivotal role. Within a clearly defined cultural domain, every developmental transition represents a crucial juncture that can shape one’s course of action, leading individuals to either choose to stay or leave, whether voluntarily or involuntarily. While several essential inquiries can be addressed by Type 4 research, such as how many individuals will likely stay or leave during the next critical developmental transition, the key question is: Who exhibits a greater propensity to stay, and conversely, who is more likely to abort further development? However, it is also worth noting that profound transitions can take place, not only when an individual shows a much deeper engagement within a specific domain but also when a developmental shift signals a departure or sometimes a boundary-­ crossing move to embrace a cross-disciplinary, cross-domain endeavor. An example is Jean Piaget’s intellectual journey, from an early passion for animal studies to an increasing interest in adaptive changes of organisms, all the way to a focus on human developmental epistemology. Another example is Elon Musk’s recent move from SpaceX and Starlinks to Neuralink projects, which combine neuroscience and computer technology to rehabilitate patients with vision loss or paralysis. The decision of whether an individual remains within the domain of a specific talent exhibits varying manifestations across different stages of their life course. Generally speaking, these transitions correspond to the evolving phases of life-span TD: identifying talent potential during childhood, actualizing talent achievements during adolescence and early adulthood, and culminating in professional excellence in mid-adulthood. In the subsequent section, we will elucidate how theories

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concerning TD transitions aid researchers in developing prediction models of successful transitions in Type 4 research, using Dai’s (2017, 2021) Evolving Complexity Theory (ECT) for illustration purposes.

7.2.1 The Emergence of Personal Action Space (PAS) and Characteristic Adaptation (CA) When One Person Transitions from the Foundational Phase to the Transitional Phase Both the concepts of personal action space (PAS) and characteristic adaptation (CA) highlight the growing autonomy of individuals as they evolve into responsible agents in their own developmental journey (Dai 2017, 2021). For example, let us consider a child who demonstrates a talent for mathematics, earning recognition and praise from both adults and peers. As a result, the child develops an intrinsic motivation to invest more effort in refining their mathematical abilities. Starting at a young age, the child takes proactive steps to immerse themselves in activities like participating in math competitions and joining math clubs. In this proactive pursuit, the child assumes the responsibility of seeking out additional opportunities and acquiring extra resources to continue enhancing their proficiency and knowledge in mathematics. In this illustrative example, the child’s voluntary and sustained engagement in activities associated with mathematical learning serves as a pivotal developmental transition. It is imperative to reiterate the spontaneity of personal agency as a critical component in this process, in contrast to a scenario where a child is compelled to partake in mathematical learning activities by external forces (for instance, due to parental pressure to attend math classes). In the latter case, it becomes arduous to predict whether the child will genuinely excel in the realm of mathematics, as no intrinsic interest is demonstrated. The process of interest development (e.g., in math) might prompt the emergence of PAS that features prominently self-initiated math learning and activities. During this period, a strength in math can propagate to other domains such as physics, computer science, artificial intelligence, or engineering. When cast in the nomothetical landscape of divergent development and a variety of TD trajectories among age peers, this ideographically delineated intrapersonal process reveals CA. Thus, ECT predicts a successful transition to CA through evidence for the emergence of a distinct PAS, which shows a talent trajectory or gravitation toward a particular line of individual development. The psychosocial markers can be an enduring interest indicated by extended and self-sustained engagement across settings (Barron 2006), a self-concept of personal strengths and propensities as a prelude to one’s self-identity (Marsh and Hau 2003), and the evidence of an achievement pattern (e.g., developing competence and milestone fulfillment) indicative of a distinct comparative advantage (Lohman 2005; Lubinski and Benbow 2006).

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7.2.2 The Transition from Characteristic Adaptation (CA) to Maximal Adaptation (MA) during the Crystallizing Phase The transition from characteristic adaptation (CA) to maximal adaptation (MA) typically demands a substantial time investment within specific domains, exemplified by the Ten-Year or Ten-Thousand-Hour Rule (Chase and Simon 1973; Ericsson 2006). In the context of the aforementioned illustrative example, the initial focus on mathematical study to enhance competence might eventually evolve into an intrinsic delight in mathematics, driven by newfound insights into the significance of mathematics and the child’s self-identity related to it. In this progression, one’s engagement with mathematics (e.g., advanced courses and independent research projects) becomes more meaning-driven. Notably, an essential aspect emerges as one’s mathematical pursuits are intertwined with other facets of self-concept, forging a clear life purpose and direction: a distinct personal identity. Through this process, the recognition from cultural institutions and admiration from peers within the domain stand as a pivotal social marker that facilitates a transition to MA. Thus, ECT predicts a successful transition to MA by substantiating a solid commitment to intensive and dedicated work within specific domains, as demonstrated through significant engagement and dedication to domain-related tasks. In that sense, psychosocial milestones can be establishing a professional identity, a resolute dedication to personal advancement within the professional sphere, active involvement within the professional community, and the invaluable guidance provided by professional mentorship. Furthermore, pivotal achievement milestones emerge, characterized by attaining notable accomplishments and the corresponding acknowledgment and validation from the social milieu.

7.2.3 Moving Beyond Technical Proficiency to Create a Distinct Niche for Contributions Transitioning beyond technical proficiency entails the strategic move of carving out a unique niche for one’s impactful contributions. This juncture represents a pivotal phase of development where individuals must skillfully navigate to not only achieve professional standards but also to foster a profound influence within their chosen field that goes beyond mere technical expertise. To revisit the previous analogy, consider the transformation of a young individual into a mathematician, having achieved an advanced degree in mathematics, physics, or AI. At this pivotal stage, the individual stands prepared to embark on an innovative journey, delving into cutting-edge research to develop groundbreaking mathematical models or methodologies to address real-world challenges or unravel long-standing enigmas within the mathematical realm.

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During this transformative transition, eminent contributions (Subotnik et  al. 2011) assume heightened significance. It denotes a shift beyond mere participation within a domain, indicating a progression from the periphery to the center of the field. An essential transition in this phase is a process of niche-picking amid various options and roles within professional communities, be it science or art, professional fields, or freelance endeavors (e.g., creative writing or business consulting). This process involves identifying and harnessing one’s distinctive strengths and comparative advantages amidst a myriad of equally competent and competitive peers. Illustrative examples lie within realms such as art: Beethoven discovered his distinctive “voice” that set him apart from Bach and Mozart; Van Gogh crafted a signature artistic style beyond the establishments of impressionism. Upon traversing this transition, individuals gain a more profound meta-­awareness of their cultivated domains and frequently infuse it with fresh creativity. Not everyone can complete this significant transition, and unlike other transitions, this particular transformation demands a synergistic fusion of individual knowledge, experiential insights, and/or honed skills. Yet, the intricacies of this transition are further compounded by the unpredictability of the elements that foster it—insights, fortuitous opportunities, and even serendipity. Consequently, some will stand to make significant contributions, and others will become increasingly obscured as major players. Thus, ECT predicts successful transitions to this advanced stage through an individual’s cultivating a distinctive niche where novel and unique contributions emerge within their chosen fields. In this regard, possible psychosocial milestones include proactive engagement with cutting-edge initiatives, forging symbiotic collaborations with accomplished mentors or eminent leaders who epitomize excellence in their respective domains, and nurturing a reservoir of personal insights that bear testament to a profound understanding of the intricacies within the field’s fabric. Furthermore, achievement milestones may include elevated social recognition emanating from the very heart of these fields—an acknowledgment that heralds the transition from peripheral involvement to central prominence and leadership. This accolade is paralleled by early but substantial achievements that indicate one’s burgeoning influence and potential in making creative contributions that significantly move the field forward.

7.2.4 Summary The three transitions mentioned above signify crucial progressions in TD, at least based on ECT (Dai 2021, 2024). In Type 4 research, aiming to create circumscribed and refined prediction models, the objective is not to predict an individual’s success in the distant future but to make predictions as to whether the individual is likely to progress to the next level of excellence. The goal is to develop predictive models that can offer insights into the progression of an individual’s TD journey, allowing for a more diagnostic and prognostic understanding of their advancement. It is

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important to note that each major transition (such as the three major transitions mentioned above based on ECT) may consist of several micro-level changes cumulatively and jointly leading to a major transition. These “small” changes might include financial conditions specific to that transition (e.g., the financial means to support full-time commitment to musical development; see McNamara et al. 2008), but there are more important changes that are truly milestone events (e.g., an enduring passion for music, or a crystalizing experience as a turning point for pursuing music, a new level of determination to pursue a music career despite financial constraints). Those milestone events can be incorporated into the models as indicators for prediction. Moreover, while childhood, adolescence, and adulthood are commonly used as developmental junctures when studying talent development, it should be recognized that these life transitions are mainly biologically determined and not specific to TD. Therefore, adopting more idiographic approaches that consider individualized transition experiences becomes crucial. For instance, pursuing personal interests through reading books and engaging in productive activities (e.g., Barron 2006) can be seen as transitioning to a distinct developmental state that is not universal but individual. This is also an example of using Type 3 research to identify critical transitions during talent development. The findings of Type 2 research can also be incorporated, for instance, the developmental timing of the onset of TD in foundational years, and its relationship with the timing of peak performance or productivity for a particular domain (Simonton 2018) and the possible importance of personality traits and their enduring influences on TD (Feist 1998; Hambrick et al. 2018).

7.3 Developing a Set of Predictors in Model Building: Achievement and Psychosocial Milestones as Predictors of a Successful Transition Fundamentally, the successful navigation of a key transition in talent development within the purview of Type 4 research should be distinguished from milestone events, which act as pivotal developmental markers predictive of a successful transition. In this regard, a combination of milestone attainment, coupled with more distant or pervasive developmental constraints, can be regarded as essential “prerequisites” for a critical transition, supported by existing literature (e.g., Simonton 1989, 1991a, b). Broadly speaking, milestone events serve as indicators of an individual’s capacity to transition effectively to the next level, potentially making or breaking their trajectory. Two distinct types of milestones exist: achievement milestones and psychosocial milestones. While a milestone event can hold personal or societal significance, the defining characteristic of a milestone is its enduring impact on life trajectories (Settersten 1999). Consequently, milestones can serve as predictive factors for significant transitions in talent development, and these milestone events can be identified through both Type 2 and Type 3 research.

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7.3.1 Achievement Milestones and Psychosocial Milestones Achievement milestones encompass external and observable accomplishments that an individual attains. For instance, consider the attainment of a professorship within contemporary academia. It is apparent that the likelihood of becoming a university professor is markedly low for an individual who has not pursued graduate education or secured a doctoral degree. Similarly, the timing of an individual’s first published article can foreshadow their prospective trajectory within the realm of scientific inquiry. Given that these achievement milestones are typically more objective and outwardly manifest, their assessment is relatively straightforward. In addition, it is essential to acknowledge the significance of psychosocial milestone events, even though they might be less overt or standardized than milestone fulfillment. These psychosocial landmark events can be intrapersonal events (e.g., a crystalizing experience) or epiphany (Walters and Gardner 1986) that prompt a TD-related decision to pursue an idea or career. A wealth of research is dedicated to the exploration of professional identity development (Kay et al. 2019; Ronkainen et al. 2019; Woolhouse and Cochrane 2010), exemplifying how psychosocial milestones can influence the trajectory of talent development. A transformation occurs as individuals shift from identifying as individuals who merely “enjoy mathematics” or “enjoy sports” to confidently asserting “I am a mathematician” or “I am an athlete.” This transition signifies a deeper integration of their chosen domain into their self-identity, marking their entrance into a new phase of TD. Psychosocial milestone events can also be impactful social events, such as an opportunity for a staged performance, joining a national science competition, or encounters with a friendship or a book that prompt the TD-related decision to take a critical step in life. These psychosocial milestone events and achievement milestones can substantially forecast crucial turning points in selecting a specific life path or achieving successful transitions toward more focused personal pursuits. These psychosocial events can be documented and assessed through self-reporting by individuals or, alternatively, by gathering information from reliable informants using a structured checklist or questionnaire. In essence, the notion is that individuals who successfully achieve a greater number of critical milestones are generally more inclined to make successful transitions to the next level in their talent development journey.

7.3.2 Developmental Constraints A pertinent and interconnected notion related to milestone events is developmental constraints or time-sensitive conditions that must be satisfied to take a particular TD path or transition to higher levels of excellence. An obvious example is that whether one has a timely onset of starting piano lessons would determine whether one even has a chance at all to become a piano virtuoso. These developmental constraints encompass conditions, often referred to as goal-related constraints, which must be

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met for the realization of specific objectives. Within the context of TD research, it becomes evident that these constraints are more appropriately classified as “soft constraints” rather than “hard constraints.” In other words, a degree of flexibility exists (e.g., a singer who has not received formal music training) whereby some obvious constraints or barriers (e.g., a basketball player who is apparently too short) can be disregarded or circumvented. On the positive side, some extreme form of precocity (e.g., in mathematics) or a strong family tradition that has produced many generations of great artists or scientists can be used as a distal predictor to enhance the efficacy of a prediction model. On the negative side, the presence of distinct intrapersonal vulnerabilities and external adversities can be detrimental to one’s competence development or persistence and otherwise present hindrances to long-term TD. Under more nuanced circumstances, adversity (e.g., the loss of a parent during childhood) can paradoxically serve as a catalyst that propels individuals toward compensation, leading to enhanced performance and motivation. This juxtaposition of positive and negative factors underscores the complexity of developmental constraints, calling for careful conceptualization of a refined prediction model through Type 4 research.

7.4 Predicting Talent Progression with Developmental Markers Fulfillment of developmental milestones holds the potential to function as predictors for the progression of talent, represented by the pivotal key transitions. However, constructing prediction models requires careful consideration of a few pertinent issues. In the following section, we will discuss some of the important issues to be considered with a brief introduction of a few candidate statistical techniques.

7.4.1 The Variable Characteristics of Predictors and Outcomes In the realm of TD research, developmental outcomes can be defined in many ways. Type 4 research primarily focuses on the successful transition to higher levels of the pursuit of excellence. Within this context, a clear-cut binary outcome measure becomes viable: successful transitions or its absence (or simply, make or break). To effectively define such binary outcomes, logistic regression stands out as a suitable analytical tool that deals with categorical outcome variables (including binary variables) while accommodating both continuous and categorical predictor variables. In parallel, decision tree analysis offers an alternative statistical approach that merits consideration. An evident distinction arises wherein logistic regression is characterized as more variable-centered, while decision tree analysis leans toward a person-­ centered perspective. In other words, logistic regression enables us to glean insights

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into the significance of predictors and their impact on outcomes through regression coefficients. In contrast, while also revealing predictor importance, decision tree analysis makes classifications so that it may furnish straightforward information regarding the likelihood of individuals who successfully transition when they reach a certain number of milestones. Statistical techniques, such as latent class growth modeling and pattern recognition analysis, empower researchers to discern discrete development trajectories over time and unravel the intertwined predictors (e.g., Gaudreau et al. 2009; Güllich et al. 2019). Those statistical methods can be helpful in the initial stages of building prediction models. For instance, Gaudreau et al. (2009) exemplify this methodology when exploring adolescent hockey players’ worlds. Their study proficiently unearthed three distinct trajectories for positive affect and an equal number for negative affect among these players. Impressively, they unveiled specific factors, including team selection at specific intervals during the season, and psychosocial factors, such as self-determination and identity-related constructs, that jointly played a pivotal role in predicting these trajectories. The deployment of latent class growth modeling and other similar statistical techniques can stand as a strategic approach, lending granularity to understanding TD outcomes and offering invaluable insights into the underlying mechanisms that steer diverse developmental pathways. For example, Gagné et al. (2019) applied group-based trajectory modeling to assess the stratified academic achievement trajectories of foreign-born Canadian adolescents from Grades 7 to 12. This method allows researchers to identify patterns (i.e., groups) in the data and track changes in outcomes that may indicate important transitions.

7.4.2 Timing of Predictors and Transitions as a Critical Factor to Be Considered in Prediction Models Integrating milestones into the landscape of TD research introduces a dynamic dimension that examines how pivotal life events occurring at different ages or developmental junctures can yield varying impacts on an individual’s developmental trajectory. This nuanced perspective underscores the recognition that the timing of significant events can profoundly influence an individual’s overall growth. For instance, consider a scenario where an individual achieves a transformative milestone, such as publishing their inaugural scholarly paper. Should this momentous accomplishment transpire at an earlier stage in life, its reverberations through their developmental journey might resonate more profoundly compared to a later occurrence (Feist 2006). The deliberate inclusion of turning points within the analytical framework embraces the temporal aspect, crafting predictive models that intricately align with the developmental progression rather than being relegated to an isolated component model.

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In situations characterized by such milestone-based trajectories, the potency of survival analysis (aka. Event history analysis, duration analysis, and hazard analysis) comes to the forefront, as demonstrated by research endeavors such as those conducted by Pion et al. (2015), Smith and Weir (2022), and Moulds et al. (2020). Survival analysis, traditionally associated with medical contexts, leverages time-to-­ event data analysis, with the “survival” element traced back to its application in studying time until specific terminal events (often mortality). However, in the domain of TD, “survival” usually pertains to whether an individual sustains a particular developmental trajectory (e.g., whether an athlete remains in the sports training program). Besides, “survival” in TD can also be conceptualized inversely as “not transitioning to the next level,” which is more consistent with our expectation for a typical Type 4 research. In that case, “survival” means non-development. The application of survival analyses accentuates the role of chronological positioning as a core factor, illuminating the interplay between events and their temporal context. To present the characteristics of survival analysis more visually comprehensibly, Fig. 7.1 illustrates the results of an analysis conducted using survival analysis based on artificially generated data (solely to demonstrate survival analysis). We can assume that this figure represents the progression of a group of artists in achieving their first domestic significant award (as a representation of a critical transition) after a particular time point. Specifically, the horizontal axis indicates the duration elapsed after the time point (measured in weeks), while the vertical axis shows the Survival Function Survival Function Censored

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percentage of individuals who have not yet received a significant domestic award. As time progresses, an increasing number of individuals receive awards, while the percentage of those who still “survive” without an award decrease. It is important to recognize that a more comprehensive analysis could extend beyond the temporal dimension, incorporating covariates to explore the diversity within the studied population. In Type 4 research, these covariates often take the form of milestone events, allowing researchers to investigate the impact of fulfilling—or not fulfilling—specific milestone events on the likelihood of successful transitions, as indicated by hazard ratio. Furthermore, the versatility of decision tree analysis extends its purview to incorporate temporal considerations. As a predictive modeling tool, decision trees are poised to encompass not only variables’ intrinsic characteristics but also the element of time. For example, the time duration a person spends in a specialized institution can be coded as a predictor and incorporated into a decision tree analysis model. In such instances, we can draw conclusions about whether an extended duration spent in a specialized institution is likely to correlate with a higher or lower probability of a successful transition to the next stage. This holistic integration enables a more comprehensive understanding of the multifaceted interplay between variables and their temporal context. In a nutshell, by accommodating temporal dimensions within decision tree analyses, researchers gain the capacity to discern not just what variables contribute to outcomes but also when these variables exert their influence, thereby capturing the intricate nuances of developmental trajectories.

Demo Study 1: Güllich et al. (2019) on Olympic Super-Elite and Elite Athletes Source: Güllich, A., Hardy, L., Kuncheva, L., Woodman, T., Laing, S., Barlow, M., ... & Wraith, E. (2019). Developmental biographies of Olympic superelite and elite athletes: A multidisciplinary pattern recognition analysis. Journal of Expertise, 2(1), 23–46. Description. This study addresses a significant question: What key features (such as specialized training, psychosocial elements, and competitive experiences) differentiate elite athletes from super-elite athletes? In other words, the research seeks to determine who can achieve elite athlete status and progress to super-elite athlete status. Methods. The research is structured into two primary phases. In the initial phase, inductive and deductive analyses are employed to discern a range of crucial features (factors) with potential impact on athletes’ career status. This serves as an initial exploration in Type 4 research, outlining predominant indicators—some proximal and others distal, some central and others peripheral. Subsequently, the second phase involves pattern recognition analysis to investigate which attributes most effectively differentiate

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between elite and super-elite athletes. This stage addresses a pivotal query inherent to Type 4 research: forecasting who is likely to remain at the current standing and who possesses the potential for further advancement. Contributions. This study yields significant contributions by not only showing how to identify potential predictors (e.g., significant milestones, demographic aspects) for “predictive” modeling but also suggesting an approach for assessing the “predictive validity” of these predictor variables. Crucially, through comparative analyses of shared and distinct characteristics among super-elite and elite athletes, this research identifies both common prerequisites for attaining elite athlete status and the elevated requisites for progression to a more advanced status. Additionally, the study exemplifies the incorporation of time-sensitive milestones into relevant analytical models (e.g., employing decision tree analysis). For example, career age structure (e.g., age when started to be a full-time athlete, age when achieving first national championships) is incorporated into analyses to emphasize the temporal significance of these milestones. Limitations: It is important to acknowledge that this study is primarily correlational. Furthermore, due to the limited sample size of participants (comprising 16 elite athletes and 16 super-elite athletes), the study’s findings may not be entirely generalizable for accurately predicting transitional success (e.g., progression from elite to super-elite) within a broader population. However, the study does lay a foundation for future research to assess the predictive validity of the identified predictors in a wider array of samples.

Demo Study 2: Habicht (2022) on German Psychologists’ Career Development Source: Habicht, I. M. (2022). Do mothers get lost at the postdoc stage? Event history analysis of psychologists at German universities (1980–2019). Higher Education, 1–20. Description. The study investigated the “leaky pipeline” phenomenon within academia, a term denoting the declining representation of women as they advance in their careers. Specifically, the research centered on the postdoctoral stage, one of the fundamental career stages in the professional trajectory of German psychologists, encompassing the doctoral stage, the postdoctoral stage, and the established researcher stage. In Germany’s postdoctoral stage, the pursuit of habilitation—a significant qualification—holds particular prominence. Of notable relevance to Type 4 research, the study not only corroborated the existing observation that

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women with children encounter added obstacles and disadvantages during the postdoctoral stage but also unearthed compelling insights. Particularly, the study discerned that accomplishing pivotal milestones, such as publishing articles in SSCI/SCIE journals and holding a foreign Ph.D., heightens the likelihood of achieving habilitation—an advancement that holds crucial significance for female psychologists, especially those who are mothers. Methods. The research data was gathered through a combination of CV data and email surveys involving psychologists within German universities during 2019. The study employed event history analysis to analyze the dynamics of achieving habilitation, which is a crucial qualification. This analysis, facilitated by semiparametric Cox regression modeling and Efron’s approximation for handling tied events clustered by scientists, was chosen due to its capacity to examine the temporal aspects and incidence of habilitation attainment. This method effectively accommodates right-censored data—scientists who had not yet achieved habilitation—and allows for evaluating diverse factors, such as publication records and attainment of a Ph.D., in influencing career progression trajectories. Contributions. In a broader context, the study addresses two pivotal inquiries pertinent to Type 4 research within the realm of academia: First, it delves into the estimation of the number of psychologists who are inclined to achieve habilitation more swiftly and effectively, thereby attaining eligibility for tenured professorships—a phase known as the established researcher stage (the third stage). Second, the study explores the factors contributing to the likelihood of securing habilitation. A significant merit of this study lies in its utilization of event history analysis, which intricately captures the evolving career development trajectory across multiple years. This approach permits a comprehensive understanding of the temporal dimensions involved and identifies the specific milestone events that substantially augment the probability of successfully attaining habilitation. Limitations. An inherent constraint of event history analysis lies in its retrospective nature, concentrating on historical occurrences and maintaining a correlational perspective. Consequently, the approach does not possess a predictive essence. This limitation is particularly relevant to Type 4 research, which is oriented toward recognizing pivotal milestone events capable of enhancing the prospects of successful transitions. Furthermore, the study’s potential for yielding more substantial insights would be magnified if it could encompass a broader spectrum of milestone events or analogous predictors. A precedent in this regard is the work of Güllich et al. (2019), which considered a wide array of milestone events or predictors to glean more comprehensive information.

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7.5 Recommendations for Future Research The primary objective of Type 4 research is to construct a predictive model, with the initial step involving the selection of an outcome variable at a particular juncture of TD, typically honing in on pivotal transitions during the progression of TD. It can take three steps as follows.

7.5.1 Step 1. Conceptualize the Targeted Problem The central concern in Type 4 research pertains to what factors, especially more proximal milestone events, account for the successful advancement of an individual to the next phase or level of excellence. In discerning these pivotal junctures, insights can be gleaned from Type 2 and 3 research, where longitudinal or retrospective studies commonly unveil important transitions. Through an amalgamation of empirical evidence and theoretical insights, researchers can pinpoint the most consequential developmental milestones in the midst of many possible determinants. It is prudent to acknowledge that not all milestone events may be manifested as externally observable phenomena, and some may be obfuscated from external scrutiny, necessitating alternative methodologies such as interviews or qualitative analyses for comprehensive comprehension. Thus, in orchestrating their investigations, researchers are well advised to encompass both overt external transitions and covert events for a significant transition, ensuring an encompassing grasp of the multifaceted processes underpinning TD.

7.5.2 Step 2. Building a Prediction Model with Proper Considerations of Statistic Models That Fit with the Mature of the Data and Variables In the process of model selection, a fundamental prerequisite is a discerning analysis of the characteristics of the variables. As expounded upon in this section, the variables employed in Type 4 research typically manifest as binary in nature. This salient feature necessitates a recalibration, as certain models tailored to continuous variables might no longer remain apt. Moreover, despite the proximal nature of the prediction task, including distal predictors within the model remains plausible. Nonetheless, overarching significance is the ultimate objective of these predictive models: to forecast, in the impending significant transition, whether an individual will successfully navigate it or encounter impediments. In addition, the intricate interplay among variables necessitates dedicated attention within the modeling process. It is imperative to point out that while instances may arise where a singular analytic approach falls short of encapsulating the entirety of scenarios, recourse can

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be sought in employing diverse models and analytical techniques to validate the robustness of outcomes. This strategic diversification bolsters the stability of results and broadens the scope to address assorted, more nuanced predictive inquiries.

7.5.3 Step 3. Interpreting the Results with Caution Although this section has introduced various statistical analysis techniques, it is paramount to remain aware that these analyses do not inherently confer the capacity of a prediction model for causal inference. Nor does a prediction model immediately render itself suitable for the immediate prognostication of TD based solely on analytical outcomes. As a prudent course of action, we suggest the application of a tested prediction model on novel samples, thereby facilitating empirical and conceptual replication of the model and the assessment of its predictive efficacy and explanatory power.

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Rose, T. (2016). The end of average: How we succeed in a world that values sameness. HarperOne. Settersten, R. A. (1999). Lives in time and place: The problems and promises of developmental science. Baywood. Silverstein, A. (1988). An Aristotelian resolution of the idiographic versus nomothetic tension. American Psychologist, 43, 425–430. Simonton, D. K. (1989). The swan-song phenomenon: Last-works effects for 172 classical composers. Psychology and Aging, 4(1), 42-47. Simonton, D. K. (1991a). Career landmarks in science: Individual differences and interdisciplinary contrasts. Developmental Psychology, 27(1), 119-130. Simonton, D. K. (1991b). Emergence and realization of genius: The lives and works of 120 classical composers. Journal of Personality and Social Psychology, 61(5), 829-840. Simonton, D. K. (1997). Creative productivity: A predictive and explanatory model of career trajectories and landmarks. Psychological Review, 104(1), 66-89. Simonton, D.  K. (1999). Talent and its development: An emergenic and epigenetic model. Psychological Review, 106(3), 435-457. Simonton, D. K. (2005). Giftedness and genetics: The emergenic-epigenetic model and its implications. Journal for the Education of the Gifted, 28(3-4), 270-286. Simonton, D. K. (2018). From giftedness to eminence: Developmental landmarks across the lifespan. In S. I. Pfeiffer (Ed.), APA handbook of giftedness and talent (pp. 273–285). Washington, DC: American Psychological Press. Smith, K. L., & Weir, P. L. (2022). An Examination of Relative Age and Athlete Dropout in Female Developmental Soccer. Sports, 10(5), 79. Sosniak, L.A. (2006). Retrospective interviews in the study of expertise and expert performance. In K.A. Ericsson, N. Charness, P.J. Feltovich, & RR. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance. Cambridge University Press. Subotnik, R. F., Olszewski-Kubilius, P., & Worrell, F. C. (2011). Rethinking giftedness and gifted education: A proposed direction forward based on psychological science. Psychological Science in the Public Interest, 12(1), 3-54. Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. Journal of Educational Psychology, 101(4), 817-835. Walters, J.  M., & Gardner, H. (1986). The theory of multiple intelligences: Some issues and answers. Practical intelligence: Nature and origins of competence in the everyday world, 163-182. Woolhouse, C., & Cochrane, M. (2010). ‘Now I think of myself as a physics teacher’: negotiating professional development and shifts in self-identity. Reflective Practice, 11(5), 607-618.

Chapter 8

Type 5 Research: The Foundation and Technology of Talent Identification

Talent identification (TI) is a widespread practice in modern society, serving both social and practical purposes. It is employed in various domains, such as selecting promising athletes in sports, auditioning to find the most talented candidates for music conservatories, or identifying academically gifted students for specialized schools or programs. In essence, TI acts as a cultural gateway that opens doors to deeper and more advanced professional experiences within a given field. However, it is important to recognize that TI goes beyond mere selection or placement. Suppose talent development (TD) is a prolonged process of cultivating one’s talent potential and systematically developing one’s skillset for productive use. In that case, TI also serves the diagnostic or prognostic purpose of identifying strengths and weaknesses at major developmental junctures to inform choices and actions. Whatever the case, TI should not be seen as a one-time determination of the presence of “talent;” it aims to make a reasonably informed assessment for practical decision-making despite incomplete information. Talent identification can take several forms. First, talent potential can be defined in many ways. For example, one’s talent potential can be seen as a distinct strength or penchant in one or more areas of human activity or in terms of the ease of learning with certain contents or situations (Chap. 5). As one progresses to more intermediate and advanced levels of TD, talent achievement can be viewed as potential for further development (Lohman 2009). In other words, one’s milestone achievements and demonstrated commitment and psychosocial skills are indicative of a potential for higher levels of achievement (Dai 2018; Feist 2006; Subotnik et al. 2019). Thus, TI is underpinned by differential, developmental, and sociocultural foundations that lay the groundwork for guiding identification practice. The research challenge, therefore, lies in determining suitable ways to define and conceptualize talent potential within the context of specific long-term human endeavors. This is not a one-off process but one that occurs at various critical junctures of TD. In addition to these foundational considerations, TI faces the task of creating specific tools, instruments, systems, and standards to fulfill its practical objectives effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_8

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Accordingly, this chapter is divided into four sections. The first section reviews the foundational issues of TI. The second section further explores the technological ramifications of fashioning effective tools and assessment systems for TI purposes. The third section presents a set of research questions for effective TI. Finally, the fourth section points out issues and problems in extant research and suggests guidelines for future research.

8.1 The Differential, Developmental, and Sociocultural Foundations of Talent Identification The traditional approach to TI is deeply rooted in the conception of talent as an enduring personal trait and even “natural endowment,” which can be quantified in certain ways (Galton 1869). Over time, this differential perspective has been modified by a developmental perspective that stresses the unfolding of one’s strengths and interests as the result of both natural penchant and environmental experiences often interacting reciprocally. An understanding of talent further enriches this developmental perspective as fundamentally a social-cultural endeavor in the larger scheme of human development (e.g., cultural selection and cultural enhancement). The evolution of this line of thinking has deeply influenced the logic and methods of TI.

8.1.1 The Differential Tradition: The Nomothetic-Idiographic Tension In history, studies of individual differences have been closely tied to the psychometric tradition, which attempts to measure a wide range of population-based human traits, including those associated with talent. Early studies using IQ as a proxy measure of intellectual potential (e.g., Terman 1925) or more recent efforts using SATs and spatial abilities (e.g., Lubinski and Benbow 2006) are just some examples of the prevalent practice of focusing on identifying psychometric traits as the markers of talent. The objective measures so obtained are contrasted with subjective assessment of talent by human experts such as teachers and coaches, who are directly working with those promising youths (Borland 2014), the latter of which has become more prevalent over the years (e.g., Witty 1958), as these observations and assessments, compared to the contrived generic psychometric tests, are closer to the authentic performance of interest (Subotnik and Jarvin 2005), and the authenticity criterion for talent (see Chap. 2 for a definition of talent). Here, the terms objective and subjective only indicate the types of assessment involved, not necessarily the degree of objectivity or subjectivity (hence, the validity) of the obtained assessment (see Borland 2014). Objective testing provides population-based estimates such as

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percentile rankings, whereas a more intimate, subjective assessment of behavior and performance provides an individual-based personal profile of distinct strengths and inclinations (Rose 2016). Consequently, these two approaches offer different operational definitions of what constitutes the manifestation of talent. This dichotomy has even sparked the talent-expertise debate regarding the acknowledgment of achieved excellence versus high test performance, with each side potentially discrediting evidence provided by the other (cf. Ericsson and Williams 2007 vs. Gagné 2009). A review of the three criteria for talent identification: excellence, domain specificity, and authenticity (Chap. 2) reveals further problems with the differential tradition of research, especially its own structural theory of human abilities (e.g., Guilford 1950; Carroll 1993). For example, Witty (1958) suggested a new definition and identification scheme that uses a range of more domain-specific performance in authentic settings as indicative of giftedness or talent (see Chap. 4). It is always meaningful to consider talent identification to be closely associated with specific domains of interest. High domain-specificity in TI usually leads to high predictive efficacy. As Lohman (2005) argued, “Selecting students for advanced instruction in science or literature using a measure G [general intelligence] is like selecting athletes for advanced training in gymnastics or basket ball using a measure of general physical fitness...[E]ven though a distal measure, such as an overall physical fitness, may work with tolerable accuracy in the entire population, it will fail abysmally in identifying high achievers in particular domains.” (p. 339) Beyond the issue of domain-specificity and the methods of observations and assessment, a deeper issue is the nomothetic-idiographic tension, which also impacts the approach of TI. Lykken (1991) identified it as a tension between the parametric approach and the structural approach to assessment; that is, population-based metrics are based on dimensional approaches to identification, assuming these dimensions work universally across all individuals, and for that matter, individual members within a population are interchangeable with respect to the functioning of that variable according to classical test theory (Novick 1966). This nomothetic approach is in contrast to the idiographic approach, which assumes that “people differ structurally from one another and, to that extent, cannot be understood in terms of the same theory” (Lykken 1991, p.  4). Nomothetic theory is predicated on structural isomorphism; that is, measured properties function in the same way for everyone. This assumption is often not met in the real operation of human behavior and mental processes (see Molenaar 2004 on ergodic switch). The issue is acute, particularly in the case of talent assessment, as the nomothetic approach can miss important idiographic complexity of talent, to use Alfred Binet’s words, such as peculiarities of individuals in questions as well as the operation of a domain. An example is the existence of child prodigies (Feldman 1986), which presents exceptionalities that do not seem to follow the parametric assumptions or nomothetic regularities, such as some highly nuanced artistic expressions or mathematical precocity in childhood.

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8.1.2 Developmental Underpinnings of Talent McCall (1981) proposed a scoop model of mental development by which individual development follows various pathways due to the interaction of genes and environments. Based on this theory, the nomothetic mapping of major parameters is still possible, including different talent trajectories that have parametric properties. Likewise, Robbie Case’s (1992) Neo-Piagetian theory of cognitive development postulates the existence of “central conceptual structures” along several dimensions of domain-specific development (mathematics, art, narrative, etc.). Accordingly, talent can be superimposed upon such developmental structuralism as a form of precocity (see Porath 2006, for a special issue on the topic), and TI can be based on a set of estimated common parameters. However, Feldman (1994) characterized TD as a form of non-universal development. Non-universal development involves qualitatively different experiences and trajectories, not merely a matter of accelerated rate of learning or more advanced development along a set of preordained universal pathways along which individual differ as a matter of degree. Translating this principle to the practice of TI, it is necessary to ask the following question: • Do talented children display qualitatively different structural and functional organizations of the mind compared to the rest of the population? • Does the development of domain-specific exceptional competence involve both qualitative and quantitative developmental changes? Feldman’s theory implies the unique organization of cognitive and affective functions leading to distinct trajectories and pathways and trajectories. If so, a distinct pattern of strengths and inclinations should be identified for each domain of interest. Regardless of the abovementioned nomothetic-idiographic tension, from a developmental perspective, talent cannot be identified with a timeless litmus test, as it were, in a once-and-for-all fashion. Rather, talent is considered emergenic-­epigenetic (Simonton 1999) and further developing (Lohman 2005), with emergent properties at different developmental junctures important for propelling TD to a higher level of excellence (Dai 2005, 2021; see also Chap. 6 on Type 3 research). Thus, TI has to be sensitive to these developmental changes every step of the way. More specifically, TI should follow the following tenets of developmental science: (A) Increasing differentiation and integration. Developmental changes occur in a structurally predictable manner, “from a state of relative globality and lack of differentiation to a state of increasing differentiation, articulation, and hierarchical integration” (Werner 1967, p.126). This emphasis, however, by no means negates the heterogeneity of the brain structures and functions from very early on (Gardner 1983; Karmiloff-Smith 2004). (B) Levels and stages. Talent identification should be grounded in varying criteria contingent on the levels and phases of talent development (Lohman 2005, 2009), and cognitive, affective-conative, and social factors, facilitative or

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inhibitive, should be considered as underlying developmental changes, for better or for worse (Dai 2021). (C) Developmental continuity and discontinuity. While the gradual, orderly nature of human development implies continuity, the levels and stages imply discontinuities in the form of unique developmental organizations (Simonton 1999), which defies measurement continuity.

8.1.3 Sociocultural Aspects of Talent Identification Talent itself is a cultural creation that serves important social functions. Two factors should be highlighted to guide TI: cultural selection and cultural enhancement. Cultural selection refers to the fact that certain areas of human activity enjoy higher cultural values, norms, and prestige than others. Thus, in agriculture-based Sparta, military prowess was valued more than the intellect; the opposite was the case for Athenian culture, who relied more on commerce and ocean travel for survival. Cultural selection creates a bias in what is considered “talented” and what is more likely to be socially recognized and identified as talent. Cultural enhancement refers to tools and resources (e.g., training) developed to enhance human functioning to the point that what we see as “talent” is always significantly nurtured (Dai 2020). For instance, suburban children often appear to exhibit traits more conducive to creative expression compared to children raised in inner-city environments, likely due to differing levels of intellectual stimulation rather than genetic disparities (Dai et al. 2012). Therefore, TI should not be based on the assumption of innate talent. Nature is always nurtured; conversely, nurturing reveals nature (Dai and Coleman 2005). The sociocultural dimension of talent further determines the nature of TI as value-laden and action-based, meant to cultivate qualities deemed valuable for society, whether the purpose is selection/placement or counseling/consultation, diagnosis, or prognosis.

8.2 Technical and Practical Considerations of Talent Identification Talent identification takes several forms in practical settings for selection, placement, and consultation purposes, each of which has different levels of stringency in criteria. It can be argued that unlike in mental health or other clinical situations, TI is more of a developmental prognosis in formative years with respect to one’s talent trajectories and promising areas of development (Dai 2020). Initially, TI serves as an exploratory process aimed at identifying an individual’s strengths, weaknesses,

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and potential areas for systematic development. As the need arises for selection or placement to facilitate more focused development, TI transforms into a prognosis of the extent to which a dynamic fit exists between the individual and the chosen domain amidst various available options. This information can be instrumental in making informed educational and career decisions. As development progresses, TI becomes capable of offering a more precise diagnosis of an individual’s strengths and weaknesses within a specific domain, as Stanley (1997) suggested. Whatever the case, TI is an inexact science of finding a valid and viable way of assessing and determining who is well fit to do well in an area of human endeavor and who will likely benefit from certain educational and training provisions and experiences. Technically, it is a matter of the probability of finding the right person in a practical context, with a trade-off between the chance of losing the right person (i.e., false negatives) and that of picking the wrong person (false positives), so to speak.

8.2.1 Statistical, Practical, and Clinical Significance TI relies on claims regarding the predictive validity of certain identification criteria, that is, how likely someone with certain characteristics will be successful in a particular area of human endeavor. The most frequently used indexes for this purpose are odds ratio and base rate. The odds ratio is defined as the ratio of the odds of A in the presence of B and the odds of A in the absence of B; thus, certain characteristics can be shown to increase the odds of a particular outcome, such as interest or achievement in music. The base rate, the proportion of individuals in a population with a certain characteristic or trait, is often used to estimate whether certain characteristics are associated with a higher incidence of a criterion outcome than the estimated base rate suggests. Statistical significance, as a probability determination, does not necessarily imply practical significance. For example, we can easily increase sample sizes to make a very small difference (say, blood pressure) statistically significant without any apparent practical significance (e.g., whether a tiny change in blood pressure makes a functional difference). The judgment of practical significance relies on evidence that such a tiny difference matters, at least in terms of its variations in a population. For identification purposes, determination of practical significance is typically made with respect to (a) non-trivial effects on certain criterion measures (e.g., levels of blood pressure associated with some diseases) for diagnostic or prognostic purposes, and, for that matter, what is the threshold level that can be considered practically “significant” (cut-offs of hypertension for a population). Something indicating practical significance does not necessarily imply “clinical significance,” which reflects a more stringent level of criterion for a diagnosis, be it autism or mathematical prodigy. Note that practical significance can be a matter of degree, whereas clinical significance always involves a categorical judgment (i.e., a matter of kind). The issue is further elaborated in the following sections.

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8.2.2 Determination of Threshold Requirements: The Issue of Trade-Off Between False Negatives and False Positives In practice, TI often works like a gatekeeper for a TD program. A key issue is how to set up threshold requirements for participation given the limited resources or specific goals for the program. TI can be either highly selective (e.g., selection of a national team for Olympic Games) or highly inclusive (e.g., participation in school enrichment programs). Selection criteria can be quite generic (e.g., IQ, GPA for academics) or highly specific to a well-defined task or domain (e.g., playing golf). Figure  8.1 shows four quadrants based on these two dimensions (selectivity and specificity). Talent identification for specialized high schools in New York City fits Quadrant 1 (selective and generic), as it used a relatively stringent cut-off (roughly top-5-to-10 percent of all applicants) for admissions and a relatively generic criterion (SHSAT, a verbal-math standardized test). The selection of national teams for the Olympics clearly fits Quadrant 2 (selective and specific). Renzulli’s notion of creating a talent pool fits Quadrant 3 (inclusive and generic), as it uses “above-average abilities” as a criterion for gifted programs. Selecting teenagers for a soccer camp fits Quadrant 4 (inclusive and specific). It is essential to acknowledge that the use of a more generic criterion is suitable when talent is broadly defined as a general potential at a young age. Conversely, it is generally inappropriate when talent has matured and become more differentiated (i.e., increasingly differentiated; see Chap. 4 for age-appropriate definitions of talent). In contrast, using highly selective versus highly liberal “threshold requirements” has a trade-off between missing the right person (Type I error; false negatives) and picking “the wrong person” (Type II error; false positives): A highly stringent, selective criterion has the advantage of avoiding many false positives, Selectivity Quadrant 1: Generic, more selective

High

(e.g. SHSAT in NYC)

Quadrant 2: Highly specific, more selective

(e.g. national teams for Olympics)

Specificity Quadrant 3: Generic, more inclusive

(e.g., top-15- as talent) pool) low

Quadrant 4: Highly specific, more inclusive

(e.g. a soccer camp)

Fig. 8.1  Selectivity and specificity as two dimensions of talent identification

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those who are either incapable or unwilling (see Dai and Chen 2021), but the trade-­ off is the risk of losing the capable and willing, and vice versa. There is no fixed formula or a golden ratio of tolerable Type I and Type II errors. However, if talent is not a static quality but developmental in nature, and if TI is always made with incomplete information, then it makes sense to use a more generic and inclusive approach (Quadrant 3) in formative years and a more specific and selective approach (Quadrant 2) when more advanced level of talent is achieved. A Case in Point: Using Pareto Principle to Model Selectivity in TI Renzulli (1986, 2005) advocates for a more inclusive criterion as the initial approach to TD. He perceives TD as a dynamic developmental process with long-term outcomes that cannot be accurately determined based on early TI information. Many essential qualities for TD (e.g., task commitment and creative potential) have yet to unfold. However, Lubinski and Benbow (2006, 2021) dismiss the notion of moderate threshold requirements (e.g., above-average abilities). They employ longitudinal evidence to argue in favor of greater selectivity in the selection process, particularly when specificity can be precisely calibrated. In other words, they favor a more structural rather than contextual-dynamic interpretation of talent potential. They showed that using the 1-in-10,000 selection criterion, as compared to the 1-in-100 criterion, one can predict a higher incidence of creative productivity in terms of the numbers of publications, and patents made decades later (Lubinski et al. 2001; Wai et al. 2005). To reconcile a more liberal, inclusive cut-off with a more stringent one, we can use the Pareto Principle to balance the trade-off between Type I (false negatives) and Type II (false positives) errors. The Pareto Principle stipulates a 20/80 ratio in the sense that for any economic phenomenon, a small number of factors or people (20%) make the most contributions (SAY 80% of the contributions). Suppose we view talent development as taking many stages, phases, and levels up to one’s peak performance. In that case, a marathon analogy can be used to model the selective strategy (including self-selection) in TI. If we assume that there are 10,000 runners in the marathon, then the top 20% would consist of 2000 runners forming the fastest group, and the next round will consist of 400 runners, and still the next 80. We can continue this process by taking the top 20% of the previous group of runners until we reach a small number of runners who are the top performers. Mathematically, it can be represented as: Number of runners in nth grouping   20 / 100   10, 000 n





After four rounds, the number narrows to roughly 13 people and then roughly 3 finalists. Race is not the best metaphor for all talent domains, as many human endeavors involve cooperation rather than mere competition. However, the example of applying the Pareto Principle demonstrates how a cumulative multi-tiered selection process (including self-selection; Sternberg 1996) can account for the rare feat achieved by only very few (e.g., one in 10,000). More importantly, the strategy of being liberal and more inclusive in early years does serve the purpose of avoiding talent loss if, instead, a more stringent criterion erroneously eliminates individuals

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with high potential, as Terman (1925) did when two to-be Nobel Prize winners in physics (William Shockley, 1956, and Luis Alvarez, 1968) were eliminated from his study because their IQ scores did not make the cut (135 or above). Regardless of what strategies to use based on the four quadrants in Fig. 8.1, it is important to highlight the contextual nature of TI, especially the developmental state and purpose of service programming. Researchers focusing on TI should acknowledge that the instruments and tools are not inherently valid. Instead, their utilization for a specific purpose determines their effectiveness and efficiency in identifying and retaining the right individuals. Many theorists postulated threshold requirements for TI (Simonton 2018a, b; Tannenbaum 1986). These postulates have to be tempered with the developmental view of talent as evolving with emergent properties that take time and development to unfold (Dai 2010; Renzulli 2005). As one progresses to a more advanced level of TD, indicated by the Pareto Principle, increasingly more stringent and specific standards of excellence will naturally apply.

8.2.3 Selectivity and Specificity as a Matter of Clinical Precision When considering the probability of accurate TI, our primary concern lies in predictive validity. Predictive validity provides a form of prognosis regarding who is more likely to succeed in a chosen endeavor. Talent identification permits clinical precision only when domain specificity or even task specificity of talent can be clearly defined and levels of excellence can be well calibrated in a population. To state more formally, talent identification reaches clinical precision and significance when someone meets a set of well-defined and well-calibrated criteria for a type and level of talent in question. Rarely have we achieved such precision. Research by Bamberger (1986) on musically talented teenagers or Ceci and Liker (1986) on professional gamblers is close to making such a diagnosis that reaches clinical precision. The tradition of expertise approach (Ericsson and Williams 2007) has achieved quite a high level of “clinical precision” with their analysis of the mediating cognitive processes and mechanisms underlying expert performance. Talent identification reaches a level of clinical significance when selectivity can be based on a set of criteria that can distinguish a specific category of individuals (e.g., math prodigy) from the rest of the population, much like how we identify cases of schizophrenia or chronic depression. To be sure, early pioneers of talent attempted to use broadly defined psychometric dimensions such as IQ to make such a “diagnosis” such as the level of IQ that reaches the “gifted” range (e.g., top-1 percent), which can be further divided into moderately gifted and profoundly gifted ranges (Gagné 2005; Lubinski et al. 2001). In hindsight, such a “diagnosis” is based on a very abstract notion of “general intelligence” and barely meets the stringent criteria of specificity in performance or productivity to justify a categorical assignment (i.e., the profoundly gifted have a unique identity and are qualitatively

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superior to moderately gifted). Based on the developmental principles of increasing differentiation/integration and developmental discontinuity, TI can be said as truly diagnostic if it meets the following criteria: (a) it delineates a particular talent trajectory with great details of developmental specificity; (b) it creates a set of well-­ defined criteria for assessing where the person in question fits in terms of levels and stages of TD, and benchmarks achieved; (c) the diagnosis is based on well-­ established empirical evidence regarding validity of the criteria used (e.g., construct representation and nomothetic span; Embretson 1983) for the categorical diagnosis. One might argue that the identification of child prodigies (Feldman 1986), savants (Miller 2005), or mathematical giftedness (Leikin 2019) meets these criteria. However, each of them should be empirically tested and verified rather than assumed a priori. While TI with clinical precision is ideal in many situations, a caveat is that not all talent can be identified with such precision. When it comes to mapping talent trajectories and developmental pathways leading to eminent achievements, making accurate predictions remains challenging in most talent domains, except for well-­ defined tasks such as skilled memory or golf expertise (Portenga 2019). In general, it is much easier to achieve clinical precision in performance-based domains (e.g., golf playing) than in production-based domains (e.g., making new scientific discoveries), wherein the developmental stages and levels can be well calibrated. Despite these limitations, it is still possible to make prognoses regarding trajectories and successful transitions using the research outlined in Chap. 7 as Type 4 research, albeit the fact that short-term predictions are always more accurate than long-term predictions because of the longitudinal decay, statistically or developmentally. Summary of Sect. 8.2  Talent identification at a more technical and practical level is contextually bound, involving many considerations, the most important being selectivity and specificity. Most TI contains errors; there is a trade-off between the error of missing the promising ones (false negatives) and picking too many “fakes,” so to speak (false positives). A research issue is to determine the level of tolerance for each of these errors when making TI for a particular programming goal, fully aware that some programs by nature are liberal and others conservative (i.e., more stringent). Another tension is making population-based predictions based on nomothetic assumptions and making individual-based prognoses based on individual profiles. To achieve developmental specificity, the latter is always preferred (see Chap. 7), which helps achieve better calibration and, in some cases, clinical precision in TI.

8.3 Research Questions on Talent Identification from a Developmental Science Perspective Developmental science treats human development as a multi-level phenomenon, and the manifestation of talent, however defined, is still a snapshot that needs to be properly placed in the larger context of individual development. At any moment,

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when TI is called for, three research questions should be asked, and research efforts follow: (a) what needs to be identified; (b) at what developmental juncture; and (c) for what purpose?

8.3.1 The Issue of What to Identify and How to Assess Talent is typically assessed in terms of either aptitude (stable trait differences) or achievement (as developmental outcomes; see Angoff 1988), typically with age peers as a frame of reference to determine the distinction. Research questions revolve around the following issues, from the theoretical to the technical and practical: Defining the Domain  The term “domain” is used loosely to define boundaries and distinctive features of a particular human activity that constrain and shape learning and development. Domain specificity should be defined rather than taken for granted. Tannenbaum (1983, 1997) defined a domain along three dimensions: mode of work (performing or producing), genre of content (thoughts or tangible products, artistry, and human service), and output (proficiency vs. creativity). Dai (2021, 2024) distinguished between five bio-ecological domains (e.g., making tools or creating mythology) and numerous culturally created domains (e.g., arts, sciences, and sports); the latter is built on the former in a variety of combinations. Regardless, broadly defined domains of human endeavor (e.g., music or sports) are not monolithically homogeneous but always show heterogeneity within a domain (different kinds of music or different roles in a game sport), as argued by Lohman (2005) in the opening quote in this chapter. Accordingly, aptitude and disposition measures can also better calibrate or fine-tune to the specificity of task constraints (e.g., center vs. point guard in basketball). Defining Aptitudes as Talent Potential  In childhood and sometimes during adolescence, talent is not fully developed to the point where outstanding achievement can be observed. Therefore, TI often relies on what are considered indicators of talent potential. The validity of aptitude measures is confirmed when they show predictive efficacy regarding short-term learning and problem-solving (Ackerman 1988; Kanevsky 1990) or long-term development (Lubinski and Benbow 2006). Controversies arise when aptitudes such as IQ or early manifestation of excellence are used to predict long-term developmental outcomes (Ceci and Liker 1986; Fransen and Güllich 2019). Research is warranted to further substantiate the claim that proximal determinants always work better than distal determinants due to regression to the mean as well as developmental complexity (Lohman and Korb 2006; Portenga 2019). In addition, domains vary in terms of what should be considered essential or for a specific domain (Sternberg 1986). For one, it has long been postulated that different domains entail different levels of general intelligence (e.g., achievement in

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physics entails higher intelligence than, say, biology; Simonton 1988; Lubinski 2004). As another example, Wai et al. (2009) showed that by adding a measure of spatial ability to the mix of verbal and math abilities, the prediction of talent for some domains (e.g., STEM) is significantly improved (see Demo 1 for details). Instead of relying on the predictive validity argument, researchers can further clarify this issue by empirically investigating the centrality of certain “general” aptitudes (e.g., IQ) to learning and performance in specific domain tasks (e.g., Bilalić et al. 2007), or determining whether certain “capacity limits” can be circumvented (Hambrick and Meinz 2011). Psychosocial Characteristics as Talent Potential  Snow (1992) argued strongly for the role of psychosocial characteristics as conative and affective aptitudes; some can be domain specific (an intellectual curiosity), and others can be domain general (e.g., openness to experience, goal-drivenness, persistence). Gagné (2005) defined them as enduring intrapersonal catalysts (i.e., trait or trait-like qualities). Olszewski-­ Kubilius et al. (2019) viewed these characteristics as more or less malleable and modifiable. The importance of psychosocial characteristics was recognized by Terman in his longitudinal follow-up (Terman and Oden 1959), which found what ultimately determines one’s success, with everything else being equal, is one’s steadfast pursuits of personal goals (ego strength) and self-efficacy. This finding is consistent with a more recent finding on the importance of intrinsic motivation (Gottfried and Gottfried (2004) and formulated grit as the key to the long-term pursuit of excellence (Duckworth 2016). The psychosocial characteristics seem to boil down to issues of intrinsic motivation, achievement drive, self-regulation/self-­ control, and coping capabilities, a formulation not that different from Galton’s conception of talent as consisting of capacity, zeal, and work ethic (see Dai et al. 1998; Fransen and Güllich 2019; Winner 1996; see Dai and Sternberg 2004, for an integrated understanding of intellectual functioning and development). Talent Achievement Is Dynamically Assessed as Developmental Potential  The traditional approach to talent potential tends to take a trait approach, thus more or less obscuring contextual factors and person-task interaction involved. This problem can be alleviated by adopting a more dynamic assessment strategy for talent identification; that is, what indicates talent potential can be dynamically assessed as differential rates of learning (or learning curves) as indicative of talent potential. Shiffrin et al. (1996) argued that the best way to ascertain talent is by observing in real time the rate of learning and asymptotic performance (i.e., the point where learning reaches a plateau; see Chap. 5). Dynamic assessment can also be made more diagnostic when specific obstacles and task constraints are identified and can be overcome by intervention. We can conceptualize an internship model of talent identification by which learning progression can be observed in real time when the person is working on authentic tasks to completion. It will be like an extension of the lab study by Kanevsky (1990), capable of obtaining all diagnostic information about strengths and weaknesses beyond the rate of learning for intervention purposes.

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8.3.2 When to Identify What: The Timing of TI as a Developmental Issue As alluded to in the previous section, what needs to be assessed is highly contingent on developmental timing; that is, at which developmental junctures talent identification (TI) is called upon to serve, whether formally or informally, reactively or proactively. Talent identification is reactive when, in natural settings, children’s distinct strengths in some areas evoke parents’ or teachers’ reactions, leading to further probes or referrals (e.g., a child who clearly shows mathematical talent). Talent identification is proactive when students with certain qualifications are sought for a particular purpose (e.g., organization of a robotics team in middle school). (A) Systematic domain differences in developmental timing and general developmental timetable. Several lines of research have charted the developmental onset and timeline of TD in various domains (Ericsson et al. 2007; Lehman 1953, Roe 1953; Simonton 1988, 2018). For example, the onset of TD in sports can be much earlier than TD in academic domains, apparently because psychomotor maturity has a developmental schedule much earlier than that of intellect. Likewise, peak creative productivity occurs much earlier in physics than in biology, likely because insights into physics can be more abstract and hypothetical-deductive than insights into biology, which are more cumulative and inductive, taking much more time to find. In short, the developmental constraints on TD are like a joint function of the talent domain and general developmental processes (physical, social, and mental). Of course, one can look further into the complexity of domain-person interaction in TD that affects TI. On the one hand, we know that there are early and late bloomers in biological development; thus, developmental precocity (e.g., child prodigies) gives us clues as to what domains appear amenable to an early onset of TD (e.g., chess, mathematics, and visual arts; Feldman 1986). On the other hand, not all early engagement and success predicts long-term success; in soccer, for example, aptitudes (developmental potential) for long-term success can only be identified much later (during adolescence; Fransen and Güllich 2019), likely because soccer as a game sport is inherently more complex (cognitively and socially) than, say, solo sports such as gymnastics. The nature-nurture debate further complicates the matter, such as the findings of relative age effect (Müller et  al. 2016); that is, an over-­ representation of athletes born early in a selection year, which is often attributed to the maturity factor (i.e., older ones get selected because they are physically stronger and socially more mature) rather than to talent or high potential. (B) The sequence of domain experience and developmental windows of opportunity. As the developmental nature of talent stipulates, TI cannot be a once-and-for-all decision but rather depends on stages and levels of TD, which poses new affordances and challenges; each level is indicated by qualitative changes in the person, not only in terms of competence but also in terms of personal relations with the

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domain (e.g., how individuals view the significance of the domain) and self-identity (how they view themselves in association with a particular personal striving; Sosniak, 2006; see Chap. 6 for elaboration). We can delineate a general framework for identification purposes (Bloom 1985; Dai and Li 2023), following the main themes of Chap. 6. As shown in Table 8.1, there are three dimensions underlying TD; competence development involves technical proficiency and conceptual understanding of the nature of a domain. It is true even for sports, which are seemingly psychomotor in nature, as there is always a meta-level control involved in developing technical proficiency and expertise, let alone for domains that involve creative experimentation with new ideas, designs, and expressions. Interest development comes along with competence development as an important affective-conative change responsible for intrinsic motivation; it also has to do with one’s particular penchant for particular objects or activities. In addition, interest development takes tenacity, as alluded to in the notion of grit (Duckworth 2016). Identity development mainly concerns niche-­ picking and making commitments, which hinges on choices one makes in the midst of various life options, opportunity costs, and risks involved in one’s commitment to a particular line of development. Integrating the three dimensions of TD, one can see the foundational stage of human development as revealing a set of aptitudes and dispositions, which vary from individual to individual. Note that different from the psychometric tradition of measuring important traits and capacities, with the nomothetic (universal) assumption of how they are distributed in a population, from a more idiographic perspective, any person’s talent potential can be portrayed as a “jagged profile” of aptitudes and dispositions (Rose 2016), some more manifest than others. In this stage, engagement in various activities can be spontaneous and playful or encouraged by parents. However, even in preschool years, children’s creative imagination in pretend play (Russ 2014) or “the rage to master” in artistic activity (Winner 1996) can already be observed, and intrinsic or instrumental interest starts to develop, so does identity (e.g., future selves), though talent trajectories may be still unclear. For this stage, the function of TI is to encourage exposure to activities of Table 8.1  A matrix of developmental benchmarks by developmental stages Developmental dimensions/stages Competence development Interest development Identity development

Foundational stage (typically in childhood) Demonstrated outstanding aptitudes and dispositions Showed playful, spontaneous engagement Observed person-­ object relations and identifications

Transitional stage (typically during adolescence) Demonstrated significant progress and achievement Demonstrated a significant bond and deeper understanding Indicated a significant level of commitment

Advanced stage (young adulthood and onward) Showed sophistication at conceptual and technical level Showed “grit” that is enduringly meaning-driven Showed a deep commitment and dedication

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individuals’ potential strengths and help build instruments and skills to support new endeavors. Methods of TI can be informal and individual-based, aiming to promote TD in whatever direction viewed desirable given the manifested developmental potential. The transitional stage, with its onset typically marked by puberty, should be considered critical and even decisive in shaping one’s developmental trajectories as one’s pattern of competencies and interests become more distinct and consolidated (Lubinski and Benbow 2006) and one’s identity (“who I am and what I want to be”) increasingly crystalized (Bloom 1985; Ceci and Williams 2010). In this stage, the most critical aspects of identifying for TD are (a) milestone achievements (e.g., winning a competition in sports, completing a challenging project in science, or completing creative writing or artwork; Feist 2006); (b) a deeper and more intensive engagement in a particular line of work (a scientific topic, computer technology, an artistic endeavor), which indicates an enduring interest or grit; and (c) psychological characteristics such as grit (Duckworth 2016). The function of TI is often selection for advanced work in secondary school or scholarship in college (e.g., Intel Science Talent Search, now Regeneron). Regardless, the key factors to consider are milestone events in achievement and psychosocial factors (see Chap. 7 for a detailed discussion of proximal predictors). The advanced stage is marked with important moments of TI for institutional selection/placement as well as personal decision-making as to whether one is to be committed to an exclusive commitment (e.g., “go pro” in sports or music, or pursuing a doctoral degree; MacNamara et al. 2008). Much more rigorous requirements for competence are imposed on selection. Beyond selection purposes, the function of TI is to inform individuals as to how well they fit with a particular endeavor among other available options. For that matter, one’s level of interest (e.g., how passionate one is and whether the interest reveals deep insights into a domain) and a sense of purpose and destiny (i.e., identity) are even more important considerations than the issue of competence. (C) Additional developmental and social considerations. Researchers in the field of TD often perceive it as a linear process characterized by a well-defined path and a predictable trajectory. A common example is the journey of new academics who follow a predetermined sequence of steps to establish themselves as valuable contributors in their specific areas of research. However, in reality, individuals frequently alter their life trajectories in pursuit of more promising opportunities for personal and social success. This phenomenon is not limited to ordinary individuals; even notable figures like Jean Piaget, who initially pursued biology before becoming captivated by child psychology, or Elon Musk, who abandoned graduate studies in economics to pursue entrepreneurship, have experienced significant shifts in their career paths. This fluidity in life choices is particularly pronounced in the information age, where new opportunities emerge rapidly, compelling people to adapt and make changes accordingly. Additionally, the development of one’s interests is more dynamic and flexible than conventionally assumed (Akkerman and Bakker 2019). ­Consequently, we must shift our perspective on TI

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away from an institutional viewpoint and toward an individual development-oriented approach. This shift emphasizes the importance of TI research, not only for the creation of effective selection tools and instruments from an administrative standpoint but also for assisting individuals in navigating their life journeys in a productive and fulfilling manner.

8.3.3 Talent Identification in the Larger Scheme of TD: Beyond the Selection/Placement Paradigm of TI Two assumptions regarding TI have become shaky in recent decades. The first assumption is that talent is an individual difference or a thing that is out there to be measured; what needs to be done is to develop a perfect measurement with high validity and fidelity. However, if talent itself is emergenic-epigenetic (Simonton 1999) and contextually shaped by extended experiences (Bronfenbrenner and Ceci 1994), then, only dynamic assessment discussed earlier can capture talent in the making. The second assumption reflects an administrative bias; that is, the main function of TI is for selection and placement; therefore, TI always comes first before any educational or training provisions can be delivered. However, this assumption is also problematic, as talent development nowadays occurs very often outside of institutional learning, at least not confined to situations calling for formal selection (Barron 2006). Borland (2014) was among very few who pointed out that Renzulli’s (1978, 1986) three-ring theory of giftedness is a working model of education intervention that embeds TI in the process of TD. While the threshold requirements (i.e., above-­ average abilities) for participation are purposefully set up at a liberal or inclusive level, two other components of giftedness (task commitment and creativity) are assessed in the dynamic process of developing necessary proficiency and working on a project that entails creative problem-solving. The justification for such a practice is that task commitment and creativity can only be observed in authentic, dynamic conditions. It deviates from the selection/placement-provision model of TI (see also Passow 1981). Decades later, in the new social context today, research on TI should explore new models of TI that make identification more flexible and developmentally responsible (Dai 2010). The following are some considerations for such R&D research. (A) The changing context for TI. In the information age, TD opportunities are more diverse and accessible, and the pathways to excellence are not confined to the rigid structure of administration. The model of TI before TD should be replaced by a more contextual, dynamic, developmental model of TD that situates TI in the process, fully recognizing the role of social and developmental processes along with the traditional focus on individual characteristics or “natural talent” (Dai and Renzulli 2008). This is not only because of the reciprocation of

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p­ rovision and identification (Passow 1981) but also because of the new insight that static assessment out of authentic contexts often falls short of capturing the dynamic aspects of personal qualities and emergent properties of the person-­ task relationship, whether it concerns the development of competence or interest and identity. (B) The changing functions of TI. A new conception of identification is that TI is more than just determining the presence and degree of a particular “talent” in a once-and-for-all fashion for selection or placement purposes; rather, it can facilitate practical decisions regarding what can be done for the optimal development of individuals in question. Granted that in sports, arts, and academics, among other domains, decisions for placement and selection depend on an assessment of qualifications through testing, auditioning, and admission portfolios. However, many occasions calling for TI are intervention oriented, for example, whether curricular differentiation is warranted for a student, whether an athlete reaches a benchmark performance, or whether a performing artist shows progress or encounter an issue in mastering a particular trade. More broadly, TI is often used for counseling and consultation (e.g., course selection and career choices), which involves assessment of the state of competence, interest, and identity in determining one’s niche potential and niche valence (Dai 2021, 2024). (C) Changing methods of TI. Talent identification (TI) clearly benefits from good measurements that help determine the strengths and qualifications. However, talent assessment can take advantage of a host of other information, such as using coaches and teachers as informants and gathering information in the field. More research is needed to treat TI as a system of assessment for TI (not merely one instrument) and test its ability to facilitate adaptive and developmentally responsive yet technically rigorous and practically viable decisions. In sum, we need a TI system that is well integrated into TD agendas (see Chap. 9).

Demo Study 1: Wai et al. (2009) on Spatial Ability for STEM Domains Source: Wai, J., Lubinski, D., & Benbow, C.  P. (2009). Spatial ability for STEM domains: Aligning over 50  years of cumulative psychological knowledge solidifies its importance. Journal of educational Psychology, 101(4), 817–835. Description. Wai, Lubinski, and Benbow (2009) examined the role of spatial ability for STEM domains using a longitudinal prediction design. Three representative samples of talented teenagers were used. The measurements consist of multiple indicators of math, verbal, and spatial abilities. Bachelor, master, and PhD degrees earned, as well as occupations, were used as criterion outcomes. The focal question was how spatial ability

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measured in early adolescence relates to academic and vocational achievements in STEM domains 10 years later. Methods. Several features stand out as significant impacting the findings: (a) three sets of large-scale longitudinal data sets; (b) using a trivariate radix statistical design that permits mapping a math/verbal/spatial three-­ dimensional space wherein each type of academic degrees earned and occupations obtained can be located as criterion outcomes; (c) corroborating and replicating patterns to enhance the consistency and reliability and validity claim. Contributions. The study has yielded the most compelling evidence for the predictive and discriminating power of spatial ability measured during early adolescence in predicting bachelor’s, master’s, and doctoral degrees earned and vocational careers in STEM domains 10 years later. By adding spatial ability as a variable, Wai et al. were able to estimate the proportion of “false negatives” when only the top-1 percent of verbal and/or mathematical ability was used for identification purposes (i.e., a large portion of individuals with top-1 percent spatial ability who would have earned STEM PhDs would not cut). The findings indicate the necessity and urgency of including spatial ability measures for identifying STEM talents. Limitations. While beyond the purpose and scope of Wai et al. (2009), there are further questions about why spatial ability is apparently so central to STEM domains for the most advanced levels of academic excellence (PhD degrees) and whether more specificity can be achieved on the centrality of spatial ability to specific aspects of STEM. Given the other research findings, vocational interest (Lubinski and Benbow 2006), STEM education doses (Wai et al. 2010), and commitment to advanced studies, as indicated in this study, can be used to strengthen the argument.

Demo Study 2: Aujla and Redding (2014) on Identification of Talented Young Dancers Source: Aujla, I. J., & Redding, E. (2014). The identification and development of talented young dancers with disabilities. Research in Dance Education, 15(1), 54–70. Description: This is an interview study investigating talent identification criteria applicable to dancers with specific disabilities. It gathered data from four existing gifted and talented integrated youth groups and training programs. The main goal of this study was to see how best to identify and develop a coherent set of identification criteria for this group of dancers. Methods: This study employed in-depth semi-structured interviews with focus groups of 18 experts and dance practitioners. Data were coded and

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analyzed to establish a set of criteria appropriate to the targeted youth dancers. The in-depth interviews with experts and practitioners capitalized on their insights based on their experience and expertise. A set of well-­ defined criteria with five categories of a level of dancing was explored. Contributions: This study makes a unique contribution by (a) situating talent identification in a specific context of serving the special needs of a group of dancers, (b) basing the study on previous research, especially longitudinal evidence for the malleability of dance-related characteristics, (c) using the semi-structured interview that permits the development of a set of better criteria of identification that fits its purpose; and (d) connecting identification closely with adaptation of training routines. Limitations: The study can be considered a preparation for the development of procedures and criteria for talent identification. As such, it is not sure which variables will be singled out as central for selection purposes and how they might be assessed. To improve the accuracy of identification, further research is clearly needed to focus on the identification of dancers with disabilities, especially regarding their distinct prognosis as compared to dancers without disabilities, which would help achieve greater developmental specificity as well as more targeted training adaptations and interventions.

8.4 Recommendations for Designing a Study on Talent Identification The above two demos as well as delineation of foundational and technical issues in this chapter can be used to guide research. Specifically, the following steps can be taken to map out one’s research project or agenda.

8.4.1 Step 1. Putting TI in the Larger Context of TD, and Considering Foundational Issues and Practical Contexts Involved As part of TD research, there is no such thing as identification for its own sake; TI always serves the practical function of advancing TD either for placement/selection purposes or for charting proper courses of action for individuals. Several questions can be asked about the context of TI: • What should be the main considerations for TI given a particular line of TD? What do we know about its developmental, domain, and social ramifications, and

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how can it properly inform TD? For instance, is a broad or narrow definition of the domain used based on programming goals as well as the stage or level of TD? Overall, does it represent a “developmentally responsive” or “developmentally proactive” design? • Is the set of criteria especially central for making proper decisions looking forward, and how can they be conceptualized dynamically, diagnostically, and prognostically to facilitate the programming goals in the larger context of TD? • Is such a study feasible in the practical context regarding its implementation (e.g., recruitment of participants, methods of assessment).

8.4.2 Step 2. Selecting Identification Criteria and Determining Appropriate Techniques of Assessment Given the Characteristics of the Targeted Population The second step is to consider the strategic and technical issue of TI, typically involving both an assessment/measurement component and a research design component. The following questions can be asked: • Is the strategy or method of identification appropriate to the circumstances? Is a nomothetic or idiographic approach used for the purpose? Would it be desirable to take a mixed approach of objective testing and subjective expert judgment? Is clinical precision and significance the goal of identification? • How can a set of selected criteria be defined operationally to facilitate assessment? What kind of indexes can be derived or aggregated as indicative of a good fit for the domain or a particular level of excellence? • Is the sensitivity or specificity of measurements used satisfactory? How well do the indicators combined predict future learning and performance? Which one stands out as apparently central, given its predictive or discriminant validity? • What is the satisfactory odds ratio for selection purposes, and what level of false negatives or positives can be tolerated in the context of selection?

8.4.3 Step 3. Consider the Overall Design of a Study as to Whether It Can Answer the Research Questions Adequately Regarding the Substantive, Technical, and Strategical Aspects of an Identification Situation Perform an overall assessment of the research design to make sure that a study “covers the ground” as far as what can be important for implementing the study, especially making sure of the following: • The study is well conceptualized in light of previous research and current contexts.

References

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The study is technically sound in implementing the identification scheme. The study has a distinct, well-developed assessment component. The study is well thought out in advance as to how the results will be interpreted. The study is well thought out as to how the results will be used in practical contexts.

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Chapter 9

Type 6 Research: Construction of Cultural Provisions and Interventions

Talent development (TD) is inherently a cultural phenomenon, with tools, resources, and support provided by a society that values particular human qualities and excellence, for intrinsic as well as instrumental reasons. As discussed in Chap. 2, talent development should be discussed from a dual perspective: individual development and human development. From an individual development perspective, TD is always influenced by both genetic and environmental factors, resulting in a distinct talent trajectory and outcomes. From a human development perspective, cultural components, as basic as spoken and written language, formally or informally constitute essential parts of individual development, from language-based knowledge of the world to skillsets developed through formal education, not to mention the elusive quality we call self-identity (Edelman 1995). In this sense, TD is fundamentally sociocultural mediated (Dai 2010). Bruner (1979) had this to say about the role of culture and education in shaping one’s unique individuality: “No person is the master of the whole culture. Each man live a fragment of it. To be whole, he must create his own version of the world, using part of his cultural heritage he has make his own through education” (p. 116). We learn from Type 1 research (Chap. 4) that TD involves multilevel interaction, from biology to culture; cultural provisions, social interactions, and psychological interventions are integral parts of TD. A central concern is how these provisions, interactions, and interventions mediate TD at the psychological and even neural-­ biological levels. This is what Type 6 research aims to achieve. Types 2 and 3 research (Chaps. 5 and 6, respectively) further reveal the differential, psychosocial, and developmental nature of talent. On the one hand, cultural artifacts and provisions and social interventions play an instrumental role in facilitating or mediating specific developmental changes, leading to culturally valued high performance or productivity. On the other hand, socially facilitated opportunities and imposed challenges can also induce differential learning and divergent development due to some enduring individual differences, some of which might even have a genetic basis. Growing up in a physically and culturally impoverished © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_9

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environment, of course, can hinder one’s individual development, revealing the importance of social equity and the significance of sufficient environmental challenges and support. The research on how cultural provisions’ and interventions’ structure and support TD ultimately can reveal the power and limits of pedagogical, institutional, and social support for the pursuit of human excellence. Types 4 and 5 research (Chaps. 7 and 8, respectively) explore specific transitions, milestone events, and behavioral and psychological indicators of talent, which are always developmental specific, sensitive to particular “developmental windows” depending on different domains (e.g., comparing dance with chess). This knowledge can inform practice in terms of what constitutes “timely” cultural provisions and psychological interventions and how specific provisions and interventions can be fashioned to promote and support TD, a main practical impetus of Type 6 research. If Type 5 research (in Chap. 8) indicates who will benefit from various cultural provisions for TD purposes, Type 6 research to be discussed in this chapter provides a broader picture of rich cultural experiences behind a myriad of TD phenomena, and how they structure and shape TD in a seamless but important way. Beyond such understandings, Type 6 research also explores specific ways and approaches with more practical and technical details as to how certain experiences of developmental importance can be pedagogically and institutionally engendered. It is only through evidence of these mediational processes that can we ascertain the beneficial effects of various programs and activities meant to promote TD. Accordingly, this chapter will be divided into four sections. We first delineate Type 6 research that explores the nature of cultural provisions and interventions for excellence in the larger context of human development. Second, we describe the implementation of cultural provisions and interventions that target specific age groups and different phases of TD. Third, we briefly review the most recent research on cultural provisions and interventions based on a set of criteria derived from a developmental science perspective. Finally, we provide recommendations regarding how to design a Type 6 research study in the spirit of developmental science.

9.1 Why Cultural Provisions and Interventions Are Essential for Talent Development From the perspective of developmental science, talent development (TD) within an individual’s life span is defined as the pursuit of excellence that integrates biological, personal, educational, and sociocultural influences. TD is fundamentally a cultural phenomenon, encompassing diverse cultural designs. Cultural provisions and interventions, conducted through social processes and interactions, form an integral component of TD. Distinction between provisions and interventions. Provisions and interventions are interrelated yet distinct facets of the talent support system. Provisions

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entail a broad spectrum of proactive learning experiences aimed at fostering TD. Interventions, on the other hand, involve a dynamic, real-time approach to addressing challenges and issues that arise during the TD process. Provisions establish an environment conducive to TD, providing diverse growth opportunities, while interventions address obstacles such as building confidence, making informed career choices, or offering mentorship.

9.1.1 Sociocultural Factors Shape the Expression of Talent If human biology is viewed as possessing “natural” potential for TD in various domains, culture can be seen as how the natural potential is structured and supported to achieve excellence. The multifaceted nature of talent is deeply molded by a multitude of sociocultural experiences, some becoming scientists or artists, other athletes or professional gamblers (see Chap. 4). Recent research by Treffinger and Selby (2023) delineated a wide range of services aiming to support TD. They emphasize the importance of environmental and contextual factors, personal and emotional aspects, and the innovative Levels of Service (LoS) approach. The LoS approach organizes TD programming with varied adaptive services, not fixed formulas. It does not categorize students by ability. Instead, it nurtures diverse strengths with flexible, inclusive programming to support students’ development. These provisions and services collectively shape diverse paths of TD and foster an all-­ encompassing learning milieu. Treffinger and Selby highlight the necessity of acknowledging and nurturing the unique strengths of individuals, fostering inclusivity and diversity in TD within educational settings. The programming strategies under the LoS approach equip educators with the means to orchestrate impactful TD programs, thereby ensuring the prospects of dynamic services for TD. In the same vein, Zielger et al. (2019), while exploring the Actiotope Model of Giftedness, suggested that talent goes beyond individual attributes; it is emerging as a result of the intricate interaction between individuals and their surroundings. At the core of this model lie the concepts of educational capital and learning capital, underscoring the influence of contextual factors in nurturing talent development. Supported by empirical investigations across various domains, Zielger et  al.’s research illustrates the pivotal role that educational and learning resources play in promoting and accelerating TD. Whether in STEM fields, long-distance running, or academia, the power of diverse and rich learning resources and expertise available often goes beyond facilitation; they empower the learner to move up a notch in TD, as suggested by Vygotsky (1978; see Dai 2020). The Actiotope model, coupled with the educational and learning capital approach, presents a more systems view of how to promote students’ TD by catering to their distinct needs and interests. Adding to this discourse, Dai’s (2021, 2024) Evolving Complexity Theory (ECT) emphasizes the importance of timely cultural provisions at specific phases of TD. He highlights three critical junctures for timely provisions and interventions: (a) the timely exposure to enriched stimulating environments that spark curiosity and

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encourage engagement, (b) the timely provision of immersive experiences with various domains to expand one’s horizon and develop enduring interests, and (c) the timely provision of opportunity for participation in advanced, professional-level endeavor for personal contributions. Dai’s proposal of the developmental progression not only underscores the necessity for timely provisions and interventions but also accentuates the role of proactive actions, instead of reactive measures, in promoting and augmenting TD. In summary, the amalgamation of Treffinger’s LoS approach, Zielger’s Actiotope model, and Dai’s ECT theory of cultural provisions clarifies the intricate interplay of sociocultural factors in shaping the development and expression of talent. Collectively, they emphasize the significance of cultivating environments that embrace diverse talents and align services with individual interests and developmental paths. This provides a comprehensive perspective on how sociocultural opportunities and supportive resources can be woven together to drive the expression and advancement of talent in the pursuit of excellence.

9.1.2 When and Where of the Interaction of Individual and Sociocultural Factors Responsible for Talent Development: Macro-, Meso-, and Micro-Level Analyses Csikszentmihalyi’s sociocultural model of TD, as outlined in Csikszentmihalyi and Robinson (1986), delineates four crucial developmental dimensions that must harmonize to foster TD: (a) cognitive and intellectual development, (b) personal development, (c) talent domains and experiences, and (d) the field or the social organization of a domain, which provide norms of practice, venues for communication and collaboration, training institutions, and gatekeepers, and so on. Of notable significance, the model underscores the central role of developmental timing in TD. For optimal progress, all four developmental conditions need to be aligned and synchronized. Following the principle of developmental specificity (as discussed in Chap. 3), a macro-level analysis underscores the crucial significance of comprehending the social and cultural contexts within which TD programs or provisions are formulated and executed. Primarily, this understanding aids researchers in deciphering the reasons behind the success or failure of specific TD programs or provisions within different contexts. For instance, family support acts as a pivotal mediator in the interaction between individual and sociocultural factors. In cultures where family involvement and encouragement are integral to educational pursuits, TD programs aligning with these cultural norms tend to flourish. For example, a program nurturing musical talent might thrive in a culture valuing music. Conversely, in contexts where family support for education is closely tied to economic pressures, TD programs might encounter challenges unrelated to educational goals. Second, this comprehension empowers researchers to design more responsive provisions that aptly cater to the diverse needs of individuals from various backgrounds. If a TD initiative aims to cultivate skills in traditional arts and crafts, it

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should exhibit sensitivity to the cultural heritage and values of the community it serves. In some societies, preserving traditional practices holds paramount importance, making a TD program that upholds and sustains these practices well received. However, introducing the same program to a culture where such practices are less esteemed could pose challenges. Third, affluent societies often offer well-funded TD programs encompassing private tutoring, specialized training, and cutting-edge equipment. Such programs are esteemed for the perceived prestige linked to these opportunities. Conversely, regions with limited financial and educational resources rely more on community engagement and cooperation in their TD efforts. They focus on fundamental areas such as reading, which may be taken for granted in wealthier communities, reflecting the cultural value of shared learning experiences. In essence, from a macro-level perspective, sociocultural backgrounds create a milieu for specific activities, enabling researchers to comprehend the effectiveness of certain provisions while identifying challenges in different cultures and social contexts. This understanding paves the way for informed TD program design and implementation strategies. Cultural provisions for TD extend beyond the realm of education/training, encompassing more than just programs and summer camps. Instead, they predominantly manifest within a meso-level framework, often rooted in local communities. This system comprises resources, tools, programs, and social as well as technical support, all contributing to fostering a rich and enabling learning ecology (Barron 2006). Illustrating this, Chowkase’s study (2022), framed within the bioecological systems theory, delves into such a learning ecosystem within rural Indian settings, holding profound implications for TD, or more precisely, its absence. The study underscores the critical provisions essential for nurturing talent in India’s rural landscapes. These provisions encompass the following: (a) a diverse range of projectand problem-based learning opportunities, (b) access to a variety of educational pathways extending beyond local schools and communities, and (c) consistent engagement maintained throughout the year. Through this exploration, the study sheds light on how local sociocultural factors such as poverty, inadequate resources (including educational and learning capital), and insufficient talent identification practices can impact TD within rural India. In essence, studies like Chowkase’s provide invaluable insights into the meso-level challenges that TD faces, advocating for systematic remedies grounded in the establishment of a sustainable learning ecosystem. To delve further into this comprehension, DiSessa and Cobb (2004) underscored the importance of theoretical frameworks within design experiments. They emphasized how these frameworks possess the potential to catalyze innovative learning methodologies, which they termed “ontological innovations.” These innovations entail a harmonious fusion of teaching strategies and classroom learning dynamics, underlining the necessity for learning communities to cultivate an optimal learning culture. DiSessa and Cobb’s exposition is exemplified through their work on quantitative reasoning. They illustrate how classroom discussions around mathematical concepts lead to a meta-level comprehension of underlying principles, culminating in a profound understanding. This process not only offers novel insights into

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instructional strategies in mathematics education but also provides a micro-level exploration of social, cognitive, and emotional processes that contribute to significant conceptual shifts—micro-developmental changes. Ultimately, this substantiates the process through which the sociocultural understanding of mathematics becomes assimilated and transformed at a psychological level, akin to Vygotsky’s (1978) concept of the zone of proximal development (ZPD). Within this context, DiSessa and Cobb emphasize the pivotal role of theoretical guidance in crafting effective learning environments that foster ontological innovations in practice. To effectively conduct research in this domain, the optimal approach is to employ design-based research methodologies (Barab and Squire 2016). In conclusion, the interplay between the individual and sociocultural provisions and interventions, as illustrated above at the macro, meso, and micro levels of operation, demonstrates a cultural web of influences shaping the development of individual talent. The emphasis on cultural milieu, learning ecology, and ontological innovation operating at different levels highlights the need for systematic approaches to TD, characterizing individual TD as intricately intertwined with sociocultural mediation, without which TD cannot go very far.

9.2 Implementing Cultural Provisions at Particular Developmental Junctures From Chap. 8 on talent identification (TI, Type 5 research), we know that identifying talent potential at different developmental junctures is conducive to developmentally responsive educational programming (Dai 2010). In other words, TI facilitates TD. While it is difficult for educators to have full control over the timing and duration of TD processes, proper TI can facilitate decision-making regarding timely opportunities to initiate or sustain TD. Cultural provisions and interventions are considered an integral part of TD with its pedagogical tools, counseling strategies, and social-cultural support, which helps develop and facilitate talent every step of the way. For that matter, researchers should be mindful of the match between what level of TD the learner demonstrates and what is to be offered. In principle, it should follow the principle of Vygotsky’s Zone of Proximal Development (Dai 2020). As shown in Table  9.1, provisions, interventions, and targeted developmental changes are presented as bullet-style points sitting in each cell of the table as a matrix of three dimensions of developmental processes (i.e., competence, interest, and identity development) occurring in three different TD phases (i.e., foundational, transitional, and advanced). It is important to note that the relationship among the three developmental processes is reciprocal rather than hierarchical; competence can lead to interest and identity, and a strong sense of identity (e.g., a strong sense of belief) can enhance interest and further facilitate competence development through committed and intensified efforts.

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Table 9.1  A matrix of provision categories by three TD phases Developmental process/phases Competence development

Interest development

Identity development

Foundational phase Authentic activities Hands-on experience Enrichment experience and thematical units Scaffolded problem-solving Self-expression and communication

Transitional phase Introducing disciplinary knowledge Project-based learning & inquiry-­ based activities “Legitimate Peripheral Participation” (LPP) Developing productive competence Game-based activities Evoking conceptual Playful engagement in interest sports, arts, gadgets, story Modeling task writing, etc. commitment Evoking competence-­ Developing selective driven motivation and affinity with curiosity-driven interest advanced topics Building meaningful person-object relations Promoting self-understandings Spontaneous niche-picking Learned delay of gratification

Building a strong identity with what one is doing Self-awareness of strengths & weaknesses Building personal aspirations and strivings Developing a personal vision of what is possible

Advanced phase Deeper engagement in professional practice Independent research projects Building technical & conceptual expertise Building a unique skillset

Promoting meaning-­ driven task motivation Inspiring cutting-edge ideas and working at the edge of one’s competence Developing a focused line of work Building a strong commitment to excellence Developing a sense of destiny and purpose Niche-picking within a domain for unique contributions Perfecting one’s trade

9.2.1 Competence, Interest, and Identity in the Foundational Phase: Developing Instruments and Habits During the foundational (or formative) phase of TD, which typically corresponds with childhood, a comprehensive TD curriculum should center around authentic activities and hands-on experiences. These activities should cater to various developmental potentials, encompassing psychomotor, expressive, technical, and intellectual activities, while simultaneously cultivating effective social and communication skills (Dai 2021, 2024). Within this period, children’s interest in activities or objects is often sparked by their curiosity, such as solving puzzles, and their newfound sense of personal agency, like engaging in games that involve overcoming physical challenges. Playful

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endeavors in sports, arts, technical gadgets, and storytelling are all pathways to effectively engage children. Moreover, extensive exposure to a diverse range of book genres (e.g., biography) is an additional approach to introduce children to a variety of realms of meaning-making (Robinson and Cotabish 2005). Enrichment experiences and thematic curriculum units play a pivotal role in this phase, featuring guest speakers, field trips, after-school clubs, and hands-on activities. The Schoolwide Enrichment Model (SEM; Renzulli and Reis 2021) serves as a prime example of provisions during this phase. Both educators and parents play an active role in scaffolding children’s experiences in this developmental stage. An illustration of the potency of scaffolding during the preschool years can be observed in the QEOSA pedagogical model of creative problem-solving. This approach leverages preschool children’s social interactions to foster their creative problem-solving skills within real-world scenarios. This simultaneously nurtures creative competence and a sense of personal agency (Dai et al. 2019; Cheng et al. 2021). The aforementioned experiences are designed to cultivate not only essential skills—such as reading, writing, drawing, reasoning, and problem-solving—to effectively tackle challenges and thrive, but also to foster habits of self-expression and self-initiated exploration. Such habits include delaying gratification for goal-­ directed productive activities, like collecting stamps or creating gadgets. Both the cultivation of key skills and the development of productive engagement habits contribute to the growth of interest and identity. This occurs by nurturing a selective affinity with specific activities, books, and individuals (Dai and Renzulli 2008), as well as enhancing self-understanding. This self-understanding involves recognizing one’s strong emotions and emotional connections with particular subjects, themes, and activities. This can act as a precursor to enduring personal interest and competence development.

9.2.2 Competence, Interest, and Identity in the Transitional Phase: Expanding One’s Personal Horizons and Developing an Enduring Interest During the transitional phase of talent development (TD), which commonly aligns with adolescence and early adulthood, the aim of cultural provisions and interventions is to facilitate a notable shift from relying on external guidance to self-directed endeavors, from passive engagement to purposeful pursuits (Dai 2021, 2024). Self-­ direction involves the use of acquired “instruments” for problem-solving, marking a new level of competence development. The TD curriculum should be meticulously fashioned to foster a profound grasp of disciplinary and technical knowledge, while actively promoting engagement in project-based and inquiry-driven activities where academic or technical proficiency is paramount. These undertakings might encompass grappling with “junior versions” of real-world challenges that demand innovative solutions (Perkins 2009)—akin to what Lave and Wenger (1991) termed

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as “legitimate peripheral participation” at the frontiers of human endeavor. Interdisciplinary projects may prompt learners to apply knowledge-based principles to real-world problem scenarios, thereby enhancing a good mindset or modus operandi conducive to innovative problem solutions. Concomitant with this phase of competence development, the transitional period heralds an expanded intellectual horizon as individuals start associating their activities with a broader realm of human pursuits. Alongside enhanced performance or productive competence, this phase in TD witnesses the emergence of emotional connections to specific domains. A robust identity, aligning one’s activities, interests, and self-concepts, has the potential to unfold. For instance, adolescents drawn to artistic pursuits may embark on advanced projects and actively seek platforms to showcase their creations, forging a link between their actions and a burgeoning self-­ identity as artists. Moreover, a heightened self-awareness of strengths and weaknesses takes center stage, fostering a more profound comprehension of personal aptitudes. Selective affinity for more intricate materials and challenging tasks becomes more apparent, acting as a motivational impetus for individuals to proactively seek out tasks that stretch their capabilities.

9.2.3 Competence, Interest, and Identity in the Advanced Phase: Developing Cutting-Edge Competence and Carving Out a Niche for Personal Contributions To comprehend the advanced phase of TD, characterized by advanced training and unwavering dedication, researchers must delve into how profound engagement with disciplines and immersion in professional practices can lead to transformative shifts in the individual involved. This stage signifies a heightened level of involvement, often within an institutional framework, exposing participants to the intricate nuances of a discipline or domain. This process nurtures a comprehensive understanding of the requirements for achieving excellence in the respective field. Independent or collaborative research projects play a pivotal role during this juncture, significantly contributing to the cultivation of both technical mastery and conceptual sophistication. While mentorship is valuable during the transitional phase (Stoeger et al. 2009), it becomes pivotal in the advanced phase, particularly in guiding advanced learners such as doctoral students. Mentorship facilitates the nurturing of critical and creative thinking within specialized domains. When coupled with immersive domain experiences, mentorship not only solidifies capabilities but also deepens the commitment to making meaningful contributions. The fact that Nobel laureate Ernest Rutherford had numerous students who themselves earned Nobel Prizes or achieved Nobel-caliber scientific accomplishments underscores the potent impact of mentorship on shaping groundbreaking advancements.

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The mentorship framework encompasses modeling, direct instruction, and diverse learning opportunities (Grassinger et  al. 2010; Subotnik et  al. 2010). According to this framework, as mentees adapt to the challenges of pioneering work, mentorship experiences immerse them in the intricacies of pushing the boundaries of knowledge. This empowers mentees to gain profound insights into recurring topics and issues. This process mirrors the concept of “legitimate peripheral participation” (Lave and Wenger 1991) at an advanced level, facilitating a gradual transition from the periphery to the core. A pertinent example is the young scientist Heisenberg seeking mentorship with Niels Bohr in Copenhagen. In the advanced phase of TD, interest becomes fundamentally epistemic and driven by meaning. Identity issues revolve around how an individual can carve a distinctive space for unique contributions. The relentless pursuit of cutting-edge ideas and the audacity to push one’s limits to challenge the seemingly impossible both bode well for such contributions. Studies focusing on this phase of TD illuminate the paramount significance of interest and identity development, coupled with the requisite technical, conceptual expertise, and creativity, for making distinctive contributions within chosen topics and domains.

9.2.4 Challenges of Studying Provisions and Interventions from a Developmental Science Perspective The evolving complexity of TD, as illustrated in the above exposition, presents challenges to researchers aspiring to conduct studies guided by developmental science. First, research designs focused on generic provisions and non-specific interventions do not meet the criterion of developmental responsiveness. More generally, it is crucial to conceptually situate provisions and interventions within a specific TD context, articulating why certain provisions or interventions constitute a developmentally responsive and proactive approach to the given challenge. Furthermore, the goals, structures, and components of a provision or intervention must be convincingly developed to facilitate evaluations of their efficacy. Second, there can be methodological challenges within Type 6 research. These challenges encompass not only the development of well-defined provisions and interventions and the determination of their “treatment” duration but also the conceptualization of developmental outcomes, both short term and long term. Addressing how to assess these changes also becomes pertinent. In the realm of gifted and talented studies, research often excels in establishing a developmental context, yet it can fall short in specifying anticipated developmental changes, as long-term developmental shifts might not be their primary focus. Comparatively, expert researchers tend to excel in explaining how guidance and coaching facilitate cognitive processes and changes in competence development. However, they might pay less attention to developmental aspects beyond the technical aspects of TD, such as interest and identity development. Researchers delving into creative productivity often concentrate on more advanced TD stages, resulting in provisions and interventions often eluding their scope, let alone being studied.

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To render Type 6 research developmentally responsive and to underscore cultural provisions and psychological and career-related interventions as integral components of TD, the manner in which various TD programs and services are studied must be transformed.

9.3 A Review and Critique of Research Conducted During 2010–2020 With the challenges discussed above in mind, we conducted a survey encompassing studies conducted from 2010 to 2020, with a particular focus on evaluating how well educational/training provisions and psychological/career-driven interventions align with the criteria of developmentally responsive and proactive practice. Two central questions guide this review and critique: (a) How well have the recent studies addressed the developmental responsiveness and proactiveness of specific cultural provisions and interventions in terms of appropriately addressing developmental needs during specific developmental phases or windows? (b) Does the study measure developmental changes, short-term or long-term, incompetence, interest, and identity, respectively or holistically? By addressing these questions, we intend to illustrate how research within the specified timeframe can effectively tackle these two vital questions regarding cultural provisions and interventions.

9.3.1 Studies on Developmental Responsiveness of Provisions and Interventions and Their Effects on Competence, Interest, and Identity in the Foundational Phase One-fourth of the studies between 2010 and 2020 fall into the category of Type 6 research (see Fig. 10.1, Chap. 10). However, whether they satisfy the criteria of good provision/intervention research is an open question. Research studies on provisions and interventions in the foundational phase can be seen as developmentally responsive if they address the issues of (a) how provisions and interventions are set up with appropriate TD goals given the characteristics of a group of children, and (b) whether specific provisions or interventions achieve the intended outcomes. Shah et al. (2018) undertook a quantitative investigation to ascertain whether curiosity can mediate a more pronounced effect of literacy provisions in academic accomplishments among kindergarten children, particularly those hailing from socioeconomically disadvantaged backgrounds. Shah and their colleagues meticulously established a suitable framework for the analysis, aiming to discern the correlation between intellectual inquisitiveness and scholastic proficiency, concentrating on cohorts from both low and high socioeconomic strata. Despite the absence of a systematically structured provision in this particular study, it nevertheless

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represents an exploration of the nascent phase of academic talent development. This inquiry suggests curiosity as a predecessor of children’s interest development, which can mediate academic achievement and development of basic “instruments” in formative years. Reis and McCoach et al. (2011a, b), in comparison, directly studied the provision of reading programs aimed at augmenting both oral reading fluency and comprehension within a cohort of 1192 students. The enrichment program, Schoolwide Enrichment Model-Reading (SEM-R), was differentiated based on student interests, affording interest-based autonomous choice of reading materials. Using a quasi-experimental design with multilevel modeling statistical techniques, the researchers identified substantial effects of this program on reading comprehension, particularly among students from high-poverty urban schools. This quasi-­ experimental inquiry meets the criterion of developmental responsiveness and proactiveness in its intent to address the reading development of students who are particularly “at risk” of being “left behind.” It was adaptive in terms of differentiating the enrichment curriculum based on student interest. With a quasi-experimental design capitalizing on the absence of a group of similar students, it furnishes empirical substantiation of the efficacy of a distinct reading provision or intervention, one that prioritizes both student engagement and personal interests, with the potential to surpass the conventional method of whole-group basal reading instruction. This assumes critical importance, as students originating from marginalized and economically challenged backgrounds often grapple with inherent disparities in achievement as well as interest within conventional educational contexts due to restricted resources, inadequate parental support, and prevailing cultural and linguistic diversities. In addition, some research studies do not satisfy at one of the two criteria. For example, Lovell et  al. (2018) examined the factors that differentiated those who were selected into playing levels in a school-based soccer program, and those who were not. The findings point to the role of individual characteristics. The study can be said to address only the first criteria in terms of developing a preliminary understanding of readiness for participating in TD in a sport. Studies of a soccer program can further address the second criterion by asking who among the selected groups benefits most from such a program on a short-term or long-term basis.

9.3.2 Studies on Developmental Responsiveness of Provisions and Interventions and Their Effects on Competence, Interest, and Identity Development in the Transitional Phase To systematically review research studies concerning provisions and interventions in the traditional developmental phase, it is essential not only to evaluate their alignment with appropriate TD goals vis-à-vis the characteristics of specific student

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groups but also to scrutinize their efficacy in effecting developmental changes. Tweedale and Kronborg (2015) conducted a qualitative interview investigation aimed at understanding the factors that either facilitated or impeded TD among students in a secondary girls’ school. While this study did not directly involve a treatment for high-achieving girls’ talent, it does satisfy the first criterion by delineating specific elements such as psychological attributes, individual capacities, and opportunities for building alliances within and beyond familial spheres, all of which contribute to fostering domain-specific TD. Likewise, Tedesqui and Young (2018) identified pivotal traits like conscientiousness, self-control, and grit as central for TD in sports They emphasized the necessity for further research into cultivating and integrating these attributes within Talent Identification and Development (TID) programs. Levav-Waynberg and Leikin (2012) implemented an instructional provision involving multiple solution tasks (MSTs) to amplify opportunities for mathematically talented students to showcase inventiveness within the realm of geometry. Employing a longitudinal experimental design, their study demonstrates how the utilization of MSTs aligns to nurture students’ creative capacities in geometry. The study shows that the use of MSTs can mediate the relationship between knowledge and creativity, putting knowledge to productive and creative use. Furthermore, as a case in point, it illustrates how a timely introduced instructional provision can facilitate a transition from a receptive to a productive mode of learning during the crucial transitional phase.

9.3.3 Studies of Provisions and Interventions in the Advanced Phase In contrast to the preceding phases, the advanced phase signifies an elevated level of engagement, accompanied by stringent institutional standards and robust support. This exposure equips participants with challenging materials that adhere to professional rigor. An apt depiction of this dynamic can be observed in the study conducted by Younger et al. (2015). This study delved into the emerging complexities associated with advancing and nurturing career trajectories for women in India. It illuminated the distinct challenges that talented young women encountered in the swiftly changing landscape, rife with gender-stereotyped expectations. Employing an exploratory case study approach, the research evaluates the impact of an innovative, group-based career support intervention. This intervention encompasses a diverse range of cognitive, affective, and social strategies aimed at enhancing women’s assertiveness, career planning, branding, mentoring, networking, and other career-related proficiencies. The study discerns the alignment between this intervention and individualized objectives, thereby promoting sustainable and impactful development.

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Furthermore, apart from its emphasis on addressing developmental responsiveness, the concept of fostering sustained development satisfies the second criterion of sustainable development within the context of Type 6 research. This is exemplified by the work of Pummell and Lavallee (2019), who conducted a thorough investigation that comprehensively outlines the trajectory of a within-career transition intervention program. This program was meticulously developed, implemented, and evaluated with the specific aim of facilitating the transition from junior-to-senior roles within the domain of sports. The study serves as a prime example of how targeted interventions, coupled with clearly defined transitional developmental goals, can effectively accomplish the intended objective of facilitating junior-to-senior career transitions.

9.3.4 Summary In summary, the research conducted in recent decades appears to align with the primary concerns of Type 6 research, recognizing the significance of developmental responsiveness in cultural provisions and interventions, as well as their role in sustaining TD by fostering positive trajectories. However, the review also reveals varying degrees of adherence to these priorities and criteria. Overall, what remains lacking is a systematic and programmatic approach to research that follows a specific line of inquiry on provisions and interventions over extended periods, akin to the way some developmental psychopathology research has been carried out (see Cicchetti and Toth 2009), even though the latter benefits from more extensive research grants to sustain such long-term programmatic investigations. In this regard, longitudinal studies that track individuals across different provisions and interventions have the potential to provide invaluable insights into the effectiveness of these strategies. Such studies could contribute to a more systematic understanding of how cultural provisions contribute to the comprehensive development of individuals’ competence, interest, and identity within specific domains, spanning the foundational, transitional, and advanced phases of TD.

Demo Study 1: Stoeger et al. (2019) on Effects of Mentorship on STEMTalented Girls Source: Stoeger, H., Debatin, T., Heilemann, M., & Ziegler, A. (2019). Online mentoring for talented girls in STEM: The role of relationship quality and changes in learning environments in explaining mentoring success. In R.  F. Subotnik, S.  G. Assouline, P.  Olszewski-Kubilius, H.  Stoeger, & A. Ziegler (Eds.), The future of research in talent development: Promising trends, evidence, and implications of innovative scholarship for policy and practice. New Directions for Child and Adolescent Development, 168, 75–99.

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Description. A study reported effectiveness of a 1-year online mentoring program with talented STEM female students with an average of 14 years of age, with respect to (a) whether the program enhanced the educational environments of these young girls regarding STEM-related development opportunities and resources, to use the author’s conceptual framework, the educational capital for these talented girls; (b) whether the enhanced educational capital helped increase these young girls’ STEM-related activities and enhanced their intention to select STEM-related subjects and pursue STEM-related careers; and (c) how the enhanced educational capital might mediate the relationship between the overall quality of mentorship experiences and the mentees’ developmental outcomes. Methods. A total sample of 998 female students participated in the online mentorship program in different years. Latent growth curve modeling was run using two measurement points with the longitudinal dataset with the first-time measures serving as baseline and the second-time measures as changes from the baseline measures (slopes). This approach permits a mediational analysis of the possible effects of mentorship on female students’ STEM activities and their likely intent to pursue STEM-related majors in college, mediated by increased educational capital. From a technical point of view, such a statistical design enables researchers to model a complex dataset from which the causal relationship can be inferred from correlational data and repeated measures with more confidence. Contribution. The context (adolescent girls in STEM programs) the study selected to situate the research questions cannot be more appropriate from a TD point of view, as it constitutes a sensitive period when STEM-­talented girls need extra support to veer toward a STEM-related career (Ceci 2018). The conceptualization of educational capital as a possible mediator of mentorship created a rich texture of learning ecology in line with the actiotope model of TD, and the longitudinal data, repeated measures over time, and the growth curve modeling further strengthened the interpretation of causal chains of events that mentorship experiences can trigger. Limitations. A self-report measure of the mentor-mentee relationship, serving as a proxy measure of mentorship quality, can sometimes obscure the exact dynamics underlying the effects observed in mentorship. In light of this, employing a mixed-methods design that incorporates qualitative data (such as interviews) could help triangulate the findings obtained from quantitative variables. Given the intricate nature of mentorship, a study of this nature calls for an interpretative component that could illuminate the essence of mentorship—how it fortified the convictions of talented young girls about their future and even crystallized their sense of destiny and life purpose. While the scope of any individual study is naturally limited, subsequent research that extends beyond short-term effects has the potential to reveal the lasting impact that the mentorship experience might exert on the long-term talent development of these girls (Dai and Li 2020).

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Demo Study 2: Henriksen et al. (2019) on What Makes Successful Interventions in Sports Source: Henriksen, K., Storm, L. K., Stambulova, N., Pyrdol, N., & Larsen, C. H. (2019). Successful and less successful interventions with youth and senior athletes: Insights from expert sport psychology practitioners. Journal of Clinical Sport Psychology, 13(1), 72-94. Description. Henriksen et al. (2019) investigated key structural components of successful interventions by interviewing expert sport psychology practitioners (SPPs) on their intervention experiences with emerging young elite athletes versus senior well-established elite athletes. Through careful analysis of the interview data, different successful interventions for youth athletes in contrast to senior athletes were found in terms of content and focus as well as delivery methods. The study discusses the results and emphasizes the importance of being sensitive to contexts with interventions. Methods. This study applied a semi-structured interview with 12 SPPs on psychological interventions with youth and senior elite athletes in competitive sports. The authors were able to present a clear empirically based conceptual framework on psychological interventions, which enabled them to organize and interpret the data in a meaningful and structured manner. Contributions. A major contribution of the study is the clarification of proper psychological interventions in the context of specific developmental tasks that promising young athletes encountered as compared to their senior counterparts. For example, younger elite athletes show both competence-­ based and personal growth issues (using a whole-person approach), whereas the issue with senior elite athletes is more focused on performance and results. It tackles a wide range of skills (e.g., goal setting, self-talk) relevant to TD of young athletes but can also be generalized to other domains, thus consistent with the tenet of developmental specificity. A second contribution of the study is using a distinct source of informants with SPPs, on what they advocate as context-sensitive practice, as these SPPs know first-hand what kind of interventions are more adaptive to the needs of youth athletes as compared to senior ones. A third contribution of the study is the development of a more systematic, rather than piecemeal, approach to working effectively with athletes at different levels of TD. Limitations. The preliminary nature of the interview study does not provide any definitive answers to the questions posed. Given that data reflect the subjective opinions of the informants, the conceptual framework developed by the study awaits more objective measures of real-world practice and outcome measures that can indicate the effectiveness of these interventions involved. The authors seemed to be well aware of not only the limitations of the methods but also the tentative nature of the conceptual framework. The generalizability of such a framework or model also remains to be tested across other sports domains.

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9.4 Recommendations for Type 6 Research Type 6 research is unique in that it usually involves a distinct practical and technical focus on how cultural provisions or psychological and career-related interventions should be implemented; however, a main impetus of Type 6 research is to place them in a developmental and social context (e.g., Demo 1, Stoeger et al. 2019) that makes the developmental significance of provisions and interventions theoretically compelling and practically effective. The following steps can be taken to build such a study.

9.4.1 Step 1. Situating a Study in a TD Context and Determining Its Main Rationale To design such a study, conceptualization needs to honor the main thrust of research for practical purposes (Phase 3 Research; see Fig. 3.1, Chap. 3), yet provide sufficient rationale that specifies the developmental processes and changes (i.e., the tenet of developmental specificity). For example, researchers should specify (a) what to provide, (b) when (i.e., developmental timing), and (c) for whom it is meant to produce a developmental change (Type 4 and 5 research). Although such provisions and interventions cannot be pre-planned in every detail as to how exactly they assist in shaping a developmental trajectory or pathway, the general rationale should provide sufficient theoretical justification.

9.4.2 Step 2. Deciding on the State of Research on the Issue and Decide What Methods Are Appropriate for a Fruitful Investigation Type 6 research concerning provisions and interventions can be categorized as preliminary when the objective is to delineate the context, content, and structure of a provision or intervention. It can be considered formative if it involves an ongoing effort to enhance a specific practice or approach. Furthermore, it takes on the role of generalizing research when aiming to expand a particular practice or model of provision or intervention. For preliminary research, methods like field interviews and case studies, which fall under the qualitative spectrum, are suitable. This phase of research might involve exploring “ontological innovations” (DiSessa and Cobb 2004) or mapping out an effective learning ecology (Barron 2006). In the formative research phase, design-based research emerges as a key approach. This typically entails a multiyear endeavor (e.g., Zhang et al. 2012) that allows researchers to construct and enhance components while simultaneously implementing a specific model. When it comes to scaling up efforts, a more quantitative design, such as

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hierarchical linear modeling (HLM), can aid researchers in determining the conditions and demographic groups for which the model seems to perform well. An instance of this is showcased in the work of Reis and Maniaci et  al. (2011a, b), wherein an existing provision model (Enrichment-Revised) was applied to a new group of children, exemplifying the concept of scaling up in action.

9.4.3 Step 3. Addressing Theoretical Questions Rather Than Simply Asking Whether the Cultural Provisions or Psychological Interventions Are Practically “Effective” When interpreting the results of a Type 6 research, it is not sufficient to merely determine whether a provision or intervention is effective or not. Because cultural provisions and psychological interventions rarely produce uniform effects under all conditions, in all contexts, and for everyone involved, a gross measure of effects, such as effect sizes, does not tell much, theoretically, as to the conditions and processes that lead to the successful facilitation of developmental changes, and for that matter, intervening processes, including impeding and adverse conditions that nullify a positive change. Stoeger et al. (2019) provide an example of how proper interpretation of the results can be made when they are cast in the rich context of mentoring STEM-talented adolescent girls (Demo Study 1).

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Subotnik, R. F., Edmiston, A. M., Cook, L., & Ross, M. D. (2010). Mentoring for talent development, creativity, social skills, and insider knowledge: The APA Catalyst Program. Journal of Advanced Academics, 21(4), 714-739. Tedesqui, R. A., & Young, B. W. (2018). Comparing the contribution of conscientiousness, self-­ control, and grit to key criteria of sport expertise development. Psychology of Sport and Exercise, 34, 110-118. Treffinger, D. J., & Selby, E. C. (2023). Levels of service: A contemporary approach to programming for talent development. In Systems and models for developing programs for the gifted and talented (pp. 629-654). Routledge. Tweedale, C., & Kronborg, L. (2015). What contributes to gifted adolescent females’ talent development at a high-achieving, secondary girls’ school? Gifted and Talented International, 30, 6-18. Vygotsky, L. S. (1978). The role of play in development. Harvard University Press. Younger, B., Tatavarti, K., Poorswani, N., Gordon-Mandel, D., Hannon, C., McGowan, I. K., & Mandayam, G. (2015). Innovative career support services for professional women in India: Pathways to success. Journal of Workplace Behavioral Health, 30(1-2), 112-137. Zhang, G., Zhao, Y., & Lei, J. (2012). Between a rock and a hard place: Higher education reform and innovation in China. On the Horizon, 20(4), 263-273. Ziegler, A., Debatin, T., & Stoeger, H. (2019). Learning resources and talent development from a systemic point of view. Annals of the New York Academy of Sciences, 1445(1), 39-51.

Chapter 10

The Current State of Research and the Future Promise

In this book, we propose a novel perspective on talent development (TD), one that underscores TD as a pertinent concern for the majority of individuals, a broad topic of realizing one’s developmental potential and living a productive, fulfilling life through the pursuit of excellence, whatever ways deemed appropriate and fitting. This vision is in contrast to treating TD as merely a technical matter of training for specialized skillsets (i.e., expertise) or a topic that is only relevant to a very few “geniuses” and luminaries. Accordingly, we define and conceptualize TD as part of developmental science, in the same way we view developmental psychopathology or criminology as part of developmental science. All these fields of research are of critical importance for the well-being of individuals as well as society. The developmental science perspective is a burgeoning metatheoretical framework (Cairns et  al. 1996; Bronfenbrenner and Evans 2000) designed to provide guidance and synthesis for research across a wide spectrum of topics concerning human development. Its primary objective is to formulate theories and models aimed at elucidating various developmental trajectories and pathways, including those leading to adverse outcomes such as criminal behavior or mental illness, as well as those leading to favorable outcomes such as excellence in various human endeavors. It encompasses areas of human development that typically fall outside the purview of traditional developmental psychology. In contrast to traditional developmental psychology, which predominantly focuses on describing “normative” age-graded changes in individual development, the developmental science framework is fundamentally oriented toward intervention. It seeks ways to enhance human conditions based on a profound comprehension of human nature and human development. The incorporation of cultural artifacts and instruments into developmental science, coupled with its inherent concern for human potential, developmental norms, and values, extends the scope of developmental science beyond the biological conception of human development, making it a truly human science. This broader perspective encompasses human excellence as a legitimate subject, including strategies for promoting and facilitating talent development to the ultimate goal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_10

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of creating a better world. Therefore, what has been covered historically by different research traditions, differential, cognitive, educational, and developmental, is now considered an integrated developmental system (Overton 2014). For example, TD is historically viewed as a peripheral issue for most developmental psychologists (David Henry Feldman, Howard Gardner, Frances Horowitz, and Ellen Winner are a few exceptions). Now from a developmental science perspective, it becomes a multidisciplinary undertaking of understanding and harnessing developmental potential for optimal human development that can benefit everyone (Lerner 2004). Has the research on TD headed in the direction we promote? Which parts of research are relatively strong and which parts are weak based on the research cycle of TD research we delineate in Chap. 3? How does research on TD compare with two other fields, developmental criminology and psychopathology? These are the issues addressed in this chapter. Specifically, this chapter tries to answer the following questions: A. What is the distribution of research based on the three-phase, six-type framework of TD research? Based on a survey of research we conducted, how well each type of research is framed or conceptualized to reflect a developmental focus, what are the strong and weak links in the cycle of research, and what are some areas that need to be improved according to the perspective of developmental science? B. At the empirical level, how well are developmental diversity and specificity tackled by this body of research? How well does the research design address these key issues? Do the methods and designs use to keep up with the approaches advocated by the developmental science framework? C. At a more theoretical level, how well is the research of different foci (e.g., differential, cognitive, educational, and developmental) integrated to address developmental complexity (e.g., the possible insulation of educational research from developmental research)? What issues remain to be addressed for such integration? D. When compared with developmental criminology and psychopathology, what kind of advances the other two fields have achieved in basic and applied research? In what way do advances in these two fields inspire the field of TD as an aspiring field of research? This chapter is organized into four sections to address the above four questions, respectively.

10.1 A Survey Study of Extant Research Literature Between 2010 and 2020: Is Research Heading in the Right Direction? To assess the current state of research within the framework outlined in Chap. 3, a survey was conducted to identify research studies falling into any of the six research categories illustrated in Fig. 3.1. The primary source for identifying empirical

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research in this field was PsycINFO.  Two rounds of searches were executed to locate relevant empirical studies. The first round of searches was conducted in January 2021. Three sets of search terms were used (see Appendix) to retrieve relevant empirical research. With several selection criteria being used, the search results were limited to (1) peer-reviewed academic papers and dissertations, (2) articles published in English, (3) articles published from April 2020 to January 2021, and (4) empirical studies. After duplicate publications were removed, 3293 were left. The second round of searches was conducted in June 2021. The aim of this round of searches was to retrieve extra publications through relevant journals in the field and key researchers in talent development research (see Appendix). In the end, 171 new publications were identified in this round. Therefore, a total of 3464 publications were identified. Under the senior author’s supervision, all those publications were examined to determine (1) if they fell within the field of talent development and (2) the specific category or type to which each publication belonged. After those steps, 2928 publications were excluded, leaving 536 publications for inclusion in the survey. Each publication was coded as mainly belonging to one of the six categories. The coding was done by three book authors in a consensual process. When an item was coded in more than one category, the decision was made as to the main category (type) it belongs to. Table 10.1 and Fig. 10.1 show the breakdowns of six categories or types of research (with subcategories of topics). Quantitatively, Fig.  10.1 illustrates that foundational research (Type 1) and Provision/Intervention research (Type 6) collectively comprise the largest proportions among the six types of research, amounting to 52% of the total studies. It is important to note that sampling biases could potentially influence this distribution, as factors such as the selection of keywords and specific journals searched may have skewed the representation toward the practical end, including miscellaneous foundational research. Conversely, refined prediction model research (Type 4 research) constitutes the smallest proportion of published studies. Nonetheless, it is noteworthy that, on the whole, a reasonably balanced representation exists across the six categories of research. Qualitatively, taking a further look at specific types of research, we can ask: A. To what extent does research in Phase 2 of the research cycle (Types 2, 3, and 4) work according to the guidelines prescribed in Chap. 3? B. To what extent is the more applied and practically focused research in Phase 3 (Types 5 and 6) informed by the research in Phase 2 and framed and designed truly in a developmentally responsible and proactive manner, which we argue is a necessary step toward a developmental science approach? Regarding the first question, it is noteworthy to observe a substantial body of work within Type 2 research, spearheaded by researchers such as Feist, Gobet, Howard, Hambrick, Lubinski, and Benbow. This research delves into the factors contributing to differential talent trajectories (interindividual differences), offering valuable insights into the ongoing nature-nurture debate. Additionally, there appears to be a reasonable amount of research dedicated to comprehending intrapersonal

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Table 10.1  Six main types or categories of research, subcategories, and sample studies Major categories (types) 1. Talent manifestation

Subcategories Manifestation of talent and expertise

2. Differential learning and development

Neural basis of talent manifestation Social-contextual factors Behavioral and molecular genetics paradigms Placement-prediction paradigm The Shiffrin paradigm

3. Intrapersonal processes and changes

4. Likelihood of success at critical developmental junctures

5. Talent identification tools and systems

6. Cultural provisions and interventions

Sample articles Lima and Castro (2011) Bilalić et al. (2010) Belsky (2016)

Kaufman (2014) Searston and Tangen (2017) Differential susceptibility paradigm Ellis et al. (2011) Mediating process and mechanisms Martindale et al. (2010) Microprocesses of developmental changes Fischer and van Geert (2014) Long-term developmental perspective Lubinski and Benbow (2021) Evolving individuality Hoffman (2015) and Carlsson (2012) Time-sensitive developmentally calibrated Røynesdal et al. constraints responsible for success or failure (2018) (qualitative) Prediction for proximal successful Höner and Votteler outcomes (quantitative) (2016) Foundation of talent identification Russ and APA (2014) The technology of talent identification Aujla and Redding (2014) Foundations of TD provisions Stoeger et al. (2019)

developmental processes, transitions, and psychosocial changes, including aspects such as identity and commitment (Dai et  al. 2015; Dai and Li 2020, 2023; MacNamara et al. 2008). These studies provide fresh perspectives on developmental processes and changes often overlooked by the between-person design employed in Type 2 research. In contrast, Type 4 research lags, with a limited number of welldesigned studies addressing issues related to short-term changes and developmental transitions. Several factors may contribute to this gap. First, the developmental understanding of talent development (Types 2 and 3 research) may not yet be refined enough to facilitate the development of precise prediction models. Alternatively, the field may still be in the process of devising approaches for applying the middlerange theory approach (Merton 1996) to model predictive relationships effectively. Perhaps, the simplest explanation is that researchers may not have recognized the necessity of constructing such prediction models, a step we contend is crucial in bridging the divide between theory and practical application.

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Fig. 10.1  A survey study of TD Research between 2010 and 2020

As for the second question, whether the research designs of Types 5 and 6 research make them truly developmentally responsive and proactive, we looked for the specification of what domains are relevant and when to intervene, as well as the articulation of what is crucial at a particular developmental juncture. In the realm of talent identification research, there exists a body of studies that place talent identification within the context of intervention and programming. At times, these studies articulate the developmental rationale for a specific approach to talent identification. Many of these studies adhere to the principle of domain specificity by pinpointing precise task demands and the corresponding characteristics to consider. However, in studies of talent identification (Type 5 research), framing talent identification beyond the goal of instrument validation in developmental contexts is not common. This is likely because the main concerns of many researchers on talent identification are practical rather than theoretical. By the same token, consequently, the developmental concerns that have prompted researchers to raise questions about the proper developmental timing of specific provisions (e.g., enrichment or mentorship) or to examine the lasting effects of deep learning experiences or mentorship over time extend beyond immediate feedback on the “intervention” itself and remain relatively rare, often due to budgetary constraints. Without some form of longitudinal follow-up, the developmental foundations of such experiences cannot be fully comprehended. In a broader sense, the challenge lies in how to encourage researchers primarily oriented toward practical objectives to adopt a more “developmentally minded” perspective. This challenge persists largely because the primary concerns of these research projects are pragmatic in nature, and many researchers may not be well versed in adopting a developmental science perspective on TD issues. A caveat is necessary to acknowledge that all the research studies we identified and surveyed in the literature between 2010 and 2020 originate from various research traditions. The typology we have applied to them and the criteria we have

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employed to evaluate this body of research are based on our conceptualization of use-inspired TD research, derived from a developmental science perspective. Therefore, anything we view as “problematic” does not indicate inherent “flaws” of the research design developed by the researchers involved; rather, what we find “wanting” reflects what we think an integrated field of TD studies should strive for. The following sections further tap into specifics of the developmental science perspective.

10.2 How Well Are Key Issues of Developmental Diversity and Specificity Addressed Empirically? At a more conceptual level, we can look at the body of research and ask how well the issues of developmental diversity, specificity, and complexity are addressed.

10.2.1 Developmental Diversity (Divergence) and the Emergence of Domain-Specific Talent and Individuality For talent development, developmental diversity takes the form of the emergence and development of talent as the result of increasing differentiation and integration (Werner 1967). On a societal level, it is evident through a wide array of increasingly distinct talent trajectories within a population, as exemplified by the findings from Lubinski and Benbow’s SMPY studies (2006, 2021) and the research conducted by Simonton (1999, 2008, 2018). Most efforts, whether aimed at generating individual difference explanations or expertise accounts, tend to concentrate on well-defined domains (e.g., chess, sports, performing arts, and certain academic domains; see Subotnik et al. 2019). However, there are relatively fewer studies exploring professional domains such as entrepreneurship, creative writing, game designing, or legal practice, which entail a more intricate combination of technical, social, and creative dimensions. In the future, talent development may not be confined to a single domain but may resemble these professional domains, incorporating various skills and knowledge areas in a rapidly evolving world and talent market (e.g., prompt engineers for the AI enterprise). This is a new form of talent diversity that researchers have yet to cover in their research. Another aspect of developmental diversity, related to the increasing differentiation and integration, is evolving individuality (Dai 2024; Emde 1994), which is responsible for a distinct representation of the world (in the case of science or art) or for a distinct personal way of engaging in the social world (as in the case of talented entrepreneurs such as Elon Musk). We have some evidence that researchers have tapped into this part of TD using an idiographic approach (see Chap. 6; also

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see Gruber and Wallace 2001, on Evolving Systems Approach or ESA). Overall, however, developmental diversity accounts in TD research are still dominated by a variable-centered, nomothetic approach.

10.2.2 Developmental Specificity and Properly Situating Talent Development Research Developmental specificity as an issue of TD encompasses a host of parameters and considerations. The developmental timing of the onset, duration, and peak of a TD event (including the classic issue of age and achievement brought up by Lehman 1953) is of main concern for much research (Simonton 2018), especially for Type 2 research. Beyond developmental timing and social contexts, what transpires in proximal processes (Bronfenbrenner and Ceci 1994) across the developmental span, with facilitative and inhibitory conditions, needs to be explicated every step of the way. Types 3 and 4 research are meant to address this question. The preponderance of the most recent literature indicates that research addressing the developmental diversity of TD to the level of specificity that rivals that of a micro-genetic study (Siegler 1996) is rare. Most studies in Type 3 research predominantly used retrospective methods. Because the research has barely reached the level of developmental specificity as prescribed in Chap. 3, it is understandable why Type 4 research on proximal prediction models, which needs sufficient specification of main benchmarks and milestones, is also lacking. Looking forward, Type 4 research, in the spirit of middle-range theory approach (Merton 1996), which helps in producing prediction models that are fine-tuned to specific domains and contexts, should be strengthened for bridging the theoretical (explanatory factors) and practical (assessment, identification, and provision/intervention).

10.3 Is Research of Different Foci Well Integrated to Address Developmental Complexity? Developmental complexity refers to the properties of human development as a multi-level dynamic system, wherein genetic, neural, the behavioral-psychological, and the social-cultural environments interact and reciprocate at multiple levels to create epigenetic and organizational changes (Gottlieb 1998). From this point of view, TD should be seen, not as an additive collection of one-to-one linear causal relationships and effects, but as the emergence of new talent-related properties out of the complex multi-level person-environment interactions. Talent development is fundamentally embedded in such a dynamic, relational developmental system, which is illustrated by Dai’s (2020, 2021) illustration of embedded or nested four layers of human agencies:

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Aptitudes & dispositions

(

Characteristic adaptations

social-cultural mediation)

Construction of self/future

Sociocultural mediation

Maximal Adaptation

Fig. 10.2  A nested, multi-layered developmental system of human agencies. (Originally published in Dai 2017)

In the smallest oval circle in Fig. 10.2, aptitudes and dispositions represent the initial personal agency that is developmentally instigative (Bronfenbrenner 1989). The terms “aptitudes” and “dispositions” are used in the sense that Snow (1992) used them, to denote someone’s inclinations, proneness, or suitability for a task or social situation (see also Ackerman 2003; Lohman 2005). The next level of analysis helps capture a growing pattern of characteristic adaptation to an array of opportunities and challenges. Characteristic adaptation as a new form of personal agency is spontaneous and fledgling, which includes aptitudes and dispositions but is contextually shaped, including dynamic, reciprocal interaction with tasks involved, and environmental conditions such as available resources, tools, and support (Gresalfi et al. 2012). Construction of self and future in the next, larger oval shape represents the emergence of new properties, which MacNamara et al. (2008) called psychological characteristics of developing excellence (PCDEs), or in a form of a growing interest and identity (Dai 2021) that self-engenders developmental changes that facilitate maximal adaptation to a new level of challenges and lead to a new level of excellence. The all-inclusive oval shape, labeled sociocultural mediation, represents a form of sociocultural agency that helps shape the way all the developed forms of personal agency are expressed and observed in Fig. 10.2. This force can consist of real people but can also be symbolic (books read, movies watched), or more formal pedagogical and technical tools and support that scaffold competence development and inspire personal visions (Vygotsky 1978; Dai 2020). Taken together, Fig. 10.2 shows TD as embedded in a developmental system, with two main regulatory forces: (a) a person of biological inclinations and propensities developing his/her own “agenda” while harnessing the resources and opportunities available in the environment for the purpose, and (b) a cultural force selectively nurturing certain human traits and tendencies for a broader, collective “agenda.” Cast in such a multi-level analytic framework, levels of analysis represented by six types of research delineated in Chap. 3 clearly show distinct significance and limitations of each type of research. Moreover, findings of different types of research can be integrated to show their complementarity and respective contributions, and a more integrated conceptual and theoretical edifice can be achieved to account for developmental complexity.

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Questions can be asked, then, as to how much effort researchers have made to deliberately engage in such a multi-level analysis to integrate research. This kind of effort may not be found in any single empirical studies covered in the survey but can be identified in review articles that synthesize a large body of research. Ullén et al. (2016), for example, pointed out the limitations of the expertise research that exclusively focuses on “proximal mediating mechanisms” to the effect of neglecting slue of factors not covered by the controlled lab research; they attempted to integrate the effects of deliberate practice and evidence of effects of individual differences in a wide range of cognitive and psychosocial characteristics on expertise development. Howard (2009) sorted through longitudinal data to identify patterns of the growth of chess competence from the onset of serious play to peak performance, with an effort to discern the role of practice and ability differences. den Hartigh et  al. (2016) employed statistical techniques of dynamic network modeling to elucidate differential curves as an outcome of the intricate interplay between multiple endogenous and exogenous factors. All these endeavors have contributed to achieving a certain degree of integration within a multi-level developmental system. Nonetheless, a truly developmental synthesis in line with the spirit of developmental science awaits more systematic efforts to generate evidence that tracks developmental progressions, analyzed with the appropriate granularity and at a level of analysis befitting the phenomenon under examination. Overall, however, as a fledgling field of research, we are far from reaching the point of sophistication in capturing developmental complexity in all its multi-level richness, nuances, and complexities. For example, grit is a construct developed by Duckworth (2016) to account for success and excellence in many domains of human endeavor. The conception of grit as perseverance of interest is reminiscent of Francis Galton’s (1869) notion of giftedness as consisting of capacity, zeal, and willingness for devoted work. If grit or giftedness works like a trait and all TD can be reduced to this trait, then there is no need to go to great lengths to trace developmental complexity, as TD can be viewed reductively as a simple matter of personality traits. However, if grit is not a unitary construct but includes passion, persistence of interest, tolerance for setbacks, and action control (Duckworth 2016), then, developmentally unpacking “grit” (like unpacking the Galtonian capacity) would be a very important undertaking in TD research, which likely involves cognitive as well as conative-affective components unfolding over time as one’s individuality evolves while interacting with particular functional environments (Dai 2021; Dai and Sternberg 2021). A perusal of the research literature in the past two decades, including the survey reported in this chapter of the research conducted between 2010 and 2020, and an earlier survey of the research published between 2000 and 2010 (Dai et  al. 2011), shows no sign of progressive deepening of our understanding in this regard. In the initial chapters of this book, we discuss the fact that the field of TD research is far from unified. Rather, it consists of several pockets of research from different traditions (e.g., psychometric tradition (e.g., Ackerman, Lubinski, Feist), expertise research from the cognitive psychology (e.g., Ericsson 2006; Hambrick et al. 2018; Howard 2009); exceptional brain from neural science tradition (e.g., Obler and Fein

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1988); gifted and talented education from education tradition (e.g., Piirto 1994; Subotnik et al. 2011); and creative productivity from creativity research tradition (Glaveanu et al. 2013; Sawyer 2012; Simonton 2018; Weisberg 2006). It is challenging to address the complexities of TD when different research traditions develop their own research paradigms and carve out specific niches that primarily focus on one aspect of TD, often neglecting other aspects and overlooking systemic issues and complexities. Additionally, researchers do not uniformly agree on the purposes and scope of TD research. There exist dedicated journals for giftedness, expertise, creativity, and a few explicitly earmarked for talent development. Consequently, it is understandable why discussions regarding developmental complexity tend to be marginalized in scholarly discourse. The primary goal of this book is to direct research attention toward a developmental systems perspective, an approach often acknowledged but rarely embraced for its implications in research.

10.4 How Can Developmental Criminology and Developmental Psychopathology Teach Us About Research on Talent Development? In 1996, when developmental psychopathology was exploring the topics of equifinality and multifinality (Cicchetti and Rogosch 1996), the field of gifted and talented education was in the midst of advocating for a transition to the talent development model (Treffinger and Feldhusen 1996). Simultaneously, expertise researchers had just begun to collaborate with scholars specializing in giftedness, talent, and creativity, such as Dean Keith Simonton, Robert Sternberg, and Ellen Winner, to address broader questions related to understanding “the road to excellence” (Ericsson 2014). Fast forward to 2005, when the fields of developmental criminology and psychopathology had already established solid foundations with dedicated journals and organizations (Cicchetti et al. 2000; Cicchetti and Toth 2009; Farrington 2003, 2017), researchers on talent development just started to synthesize their research and coordinate their efforts. The senior author of this book, in conjunction with Larry Coleman, the then editor of the Journal for the Education of the Gifted, initiated the groundwork for the integration of research on the development of exceptional performance and productivity. They accomplished this by assembling  a group of prominent scholars and researchers from diverse backgrounds, including Ceci, Ericsson, Lohman, Miller, and Simonton (Dai and Coleman 2005). It is important to recognize that as a field, TD research remains relatively young and continues to evolve.

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10.4.1 Structural Commonalities Between TD Research and the Other Two Fields of Research Fortunately, there exist fields of research that not only bear a family resemblance to TD research but are also more advanced in comparison to the state of research in TD. In the following section, we undertake a comparative analysis between the field of TD research and the fields of developmental criminology and psychopathology for two primary reasons. First, by examining the structural similarities and differences between these research domains, we can glean insights into the inherent nature of the phenomena each field addresses, as well as the methodologies and principal concerns they employ. Second, since the field of TD is still in the process of defining its mission, agenda, and methodology, studying how other fields establish and conceptualize their problems while making progress can offer valuable guidance for clearly and productively defining the field of TD. A. Defining the domain, central issues, and the boundary Developmental psychopathology squarely situates itself within “the interplay of biological, psychological, and social-contextual aspects of normal and abnormal development” (Cicchetti and Toth 2009, p. 16). In contrast, developmental criminology focuses on developmental trajectories leading to criminal behavior and antisocial conduct, risk factors at various life stages, and the influence of life events (social-contextual) in shaping these developmental paths (Farrington 2003). A slight modification of these statements can readily encapsulate the essence of TD research. However, the delineation of end states is more well-defined in the former fields (with various “offending” or pathological diagnoses) than it is in TD research. In other words, the boundaries of TD remain somewhat ill-defined. Moreover, the nature-nurture debate has persisted in TD research since Galton’s time (1869). As of 2007, Ericsson continued to challenge Galton’s capacity view of talent (Ericsson et al. 2007). The challenge in TD is that this debate has evolved into an ideological battle over time. One of the contributing factors is that the definitions of “genius” or “talent” are not as clear-cut as those for criminality or psychopathology. The conventional practice of research, as outlined by Kuhn’s (1962) concept of a paradigm (e.g., a shared system of concepts), is difficult to establish when the field is fraught with numerous controversies. B. Models of development The situation-disposition model has been well-established in clinical psychology to account for the development of mental illness (e.g., the diathesis-stress model of depression). Youth psychopathology conceptualized as developmental disruption following exposure to adversity reflects such a basic understanding (Mclaughlin 2016). It is also a basic model of personality development (Mischel and Shoda 1995). Various talent models are explicitly or implicitly situation-disposition models (e.g., Gagne’s DMGT or Simonton’s emergenic-epigenetic model). The most recent version of the model is the differential susceptibility model proposed by

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evolutionary psychologists (Ellis et al. 2011). A central feature of the model is the specification of person-environmental dynamic interaction as revealing low and high susceptibility for development enhancement (positive) versus vulnerability (negative). It provides a more nuanced model of developmental “risks” toward social aggression or psychopathology, but it can also shed light on cases of Van Gogh or Michael Jackson as to how their strengths and vulnerabilities play out in their TD, vis-à-vis their interaction with the environment. This model is effective in explaining the biological aspects of TD but is less suitable for addressing the cultural dimension. As a result, many culturally oriented and personality-oriented psychologists do not endorse this approach (e.g., Bloom 1985; Csikszentmihalyi and Robinson 1986; Gruber 1986). They prefer non-reductionistic, self-development models of TD, which start with real-world achievement and retrospectively reconstruct the developmental processes leading to talent achievement. C. Mapping the timing of onset and peak, and progressions in-between The issue of age and crime is a preoccupation of developmental criminologists, just as the issue of age and achievement is of TD researchers (e.g., Lehman 1953; Roe 1953). For developmental psychopathology researchers, the kick-in of a mental illness can also work like a time bomb (e.g., a particular psychosis). Understanding the timing of a developmental course is important as it provides clues to developmental trajectories, pathways, transitions, and critical turning points for life-course criminology, the development of mental illness, and talent achievement alike. In this regard, TD research seems to have made less progress, likely due to the heterogeneity of talent development pathways, which are difficult to chart in terms of specific step-by-step sequences (e.g., activation, escalation, de-escalation, and resistance of juvenile delinquent behavior; Ayers et al. 1999; or epigenetic progressions of drug addiction that involve unfolding biochemical processes; see Nestler 2014). This is the level of developmental specificity TD research should aspire to. Alas, very little research has managed to achieve such a level of specificity (except, probably, for TD in solo sports). D. Facilitative and inhibitive factors (as parallel to risk and protective factors for the two fields) Preventive strategies and measures hold a significant place in the domains of developmental psychopathology and criminology. Consequently, a distinct research priority involves the identification of both risk and protective factors. The empirical definition of these factors has often achieved a level of clinical precision, encompassing considerations such as the clustering of risk factors, their multiplicative rather than additive effects, and their age-graded influence (Farrington 2003). Similarly, researchers in the field of TD devote considerable attention to the developmental specificity of facilitative, inhibitory, or deterring factors (Subotnik et al. 2019). For instance, a range of enrichment activities is considered crucial for fostering interest during formative years, while deep experiences and mentorship play pivotal roles in transitioning to advanced levels of TD during adolescence and young adulthood (Dai and Li 2020, 2023; Feist 2006; see Chap. 9 for further details).

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Additionally, it is recognized that a lack of self-efficacy, insufficient support at critical junctures, and sometimes financial constraints can impede or inhibit talent development (MacNamara et al. 2008). However, in terms of diagnostic intricacies and sophistication, the state of TD research in this regard lags considerably behind the other two fields. E. Proximal processes A noticeable shift in recent research within developmental psychopathology and criminology is the transition from merely identifying components and predictors to gaining an understanding of proximal processes (Cicchetti and Toth 2009). This shift entails a focus on comprehending the precise dynamics occurring during events such as traumatic experiences, as opposed to solely predicting them. More broadly, the central concern lies with proximal processes and the emergent patterns of interactions and associated changes (Bronfenbrenner and Ceci 1994), rather than adhering to a component-dominant approach. In the realm of TD research, this trend has also been apparent from its early stages (Bloom 1985; Feldman 1986), particularly with the adoption of experience-sampling techniques (Csikszentmihalyi et al. 1993). Proximal processes are prominently featured in Dai’s ECT theory (2020, 2021), as this theory aligns with the logic of emergence and self-organization, in contrast to a causal reductionist perspective. However, despite recent advancements in research methodologies advocating for intensive analyses of time-dependent and relationship-intensive units of learning processes and developmental changes (Hilpert and Marchand 2018), systematic investigations into the extended proximal processes underpinning TD remain relatively scarce and infrequent. F. Inductive versus deductive approaches It is instructive to note that, in the other two fields, research typically starts with a specific phenomenon of interest, be it depression or stealing. One builds generalizations from a class of phenomena. Although some research on TD starts with cases of talent (David Henry Feldman’s seminal work on child prodigies, or Bloom and colleagues’ work on young eminent scientists, artists, and athletes), much work in gifted and talented studies starts psychometrically defined abilities and traits, a distinct deductive approach, making general assumptions first and then generalize to cases. Lykken (1991) reflected on the parametric approaches to psychopathology (i.e., using nomothetic dimensions and psychometrics) and surmised that a clinical approach that captures the structural properties can be more productive (p. 18). It seems clear that case-based reasoning provides more accurate diagnosis and prognosis than rule-based reasoning in clinical assessment. By the same token, in developmental science, person-centered approaches are preferred to variable-centered approaches (Bergman and Magnusson 1997; Laursen and Hoff 2006). Ultimately, we need to reckon with the basic premise of TD work: Are we working on universal laws (with nomothetic assumptions) which Spearman (1904) believes we are with “general intelligence,” or are we building highly contextualized local models as Type 4 research (Chap. 7) is trying to do, with all their nuances and “idiographic complexities,” to use Alfred Binet’s words?

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G. Multi-level systems analysis versus radical reductionism Deductive logic seeks simplicity and inductive logic builds complexity. Cicchetti and Toth (2009), by focusing on developmental processes, favor an approach to tackling developmental complexity, including the phenomena of equifinality and multifinality. LeBlanc’s multi-layer control theory of offending behaviors, whose development is subject to four mechanisms of control, also favors developmental complexity (see Farrington 2003; cf. Dai’s multi-layered TD model shown in Fig.  10.2). However, Gottfredson and Hirschi’s theory is distinctly “anti-­ developmental” (see Farrington 2003, p.  228), as it favors a radical reductionist explanation: everything about offending behaviors boils down to self-control, just like almost all social problems can boil down to general intelligence (Gottfredson 1997; it is a capacity issue, stupid!). From a radical reductionist point of view, there is no need to resort to development for explanation, as development is epiphenomenal. For TD research and theory, the question is whether a TD theory should rely on a wide spectrum of manifestations of human potentials and achievements, or a very few extreme cases, as if Einstein’s mind is a standard model of a scientific talent. There seems to be some measure of intellectual laziness as far as one dismisses the role of human development. One can argue that, even in Einstein’s case, cultural niche construction and the existence of a scientific community were indispensable for Einstein’s scientific contributions.

10.4.2 Distinct Features of Talent Development and Unique Considerations of TD Research Despite the structural similarities shared among the three domains of development, it is crucial to quickly highlight the significant distinction of TD from the other two domains. TD is centered on nurturing developmental potential to foster a better, more productive life and to enhance the vitality of society. In contrast, the other two fields primarily have a preventive orientation: they aim to prevent and mitigate maladaptive individual development and personal suffering, and to avert or reduce individual development that strays from cultural norms to an extent that harms individuals’ well-being and jeopardizes the fabric of a healthy society. One domain is dedicated to transcending oneself for the pursuit of human excellence, while the other two domains revolve around the exploration of human flaws and vulnerabilities rooted in biology, psychology, and sociology. Certainly, some instances intersect these three domains, such as individuals like Adolf Hitler or Ted Kaczynski, whose talent was harnessed for power and criminal acts, or individuals like Baudelaire or Van Gogh, whose talent was intricately linked to personal vulnerabilities. To distinguish TD from the other two aspects of human concerns, we can identify at least three distinct features that set TD apart from the others.

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A. Talent Development is more diverse and pluralistic Many difficulties we allude to for TD research in the above comparison are because TD phenomena are more expansive and pluralistic than those in the other two domains. The phenomena in the latter are more circumscribed compared to the former, with origins and ontogenies easier to define and symptoms and biological and social causes more tractable. In comparison, there are numerous ways to achieve human excellence. Moreover, in TD there is always something new in the making, and it is difficult to foretell what is going to happen next, let alone what kind of accomplishments one can anticipate and the impact it would generate. Also, domain boundaries in TD are not clear-cut but penetrable from one to another. In psychopathology and criminology, behavioral and physiological symptoms can be documented more precisely (e.g., brain imaging). In TD, however, what one constructs in the mind or body (e.g., an embodied or imagined dance movement) is more difficult to capture. B. Talent development entails prolonged education, training, and institutional and technical support The most important distinction of TD from the other two fields is that talent is made possible by opportunities, resources, and tools, or, more theoretically, various cultural niche constructions, including educational and training institutions, pedagogy, and technology. Although there can be sociocultural reasons why  people develop anxiety and depressive disorders, or proactive aggression against others, the roots of them can be traced to human nature (e.g., genetics). Talent is a different matter; although all talents have biological constraints, they are “crafted” according to some kind of design that serves a purpose (think of developing hunting techniques or fashioning dance movements by Homo sapiens). In modern times, TD is even more of a cultural event with distinct cultural selection and support for enhancement and long-term support. Therefore, to study TD, one inevitably has to delve into the issue of cultural evolution in terms of human-created environments and tools that shape human learning and development. Consequently, one has to go beyond the confines of the developmental functional history (DFH; Silverstein 1988) of individuals to make TD a multidisciplinary endeavor, encompassing how culture is created to serve some social and instrumental functions, and how talent domains themselves evolve over the generations. C. Talent development involves more complex reciprocation of behavioral, internal, and environmental factors as per Bandura (1986), and the irreducibility of talent The reason why developmental psychopathology chooses to study micro-level processes rather than identifying macro-level predictors (Cicchetti and Toth 2009), and why it insists on searching equifinal and multifinal pathological outcomes (Cichetti and Rogosch 1996), is that they recognize the complex interplay of biological, psychological, and social factors responsible for abnormal development and psychological deviations. It is more so with TD. It is more challenging to track the

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biological, cognitive, motivational, and social processes underlying TD, and structural and functional changes in the brain/mind (e.g., a sense of musicality or physics intuition), which cannot be reduced to some “brain abnormality,” as some researchers have us believe about the brain anatomy of Einstein (Diamond et al. 1985). Even though cognitive neuroscience might explain how long-term musical training leads to structural changes in the brain of musicians (Schlaug 2001), which presumably enhances neural efficiency, it cannot explain how a musician comes to appreciate deeper meaning in musical expression. In other words, something like highly developed musical or physics intuition is irreducible to physiology or neurobiology.

10.4.3 Sum-Up: Silver Lining for a New Fledgling Field of Research There are encouraging indications of the convergence of multiple lines of research centered on TD, which are integrated into the research cycle in the form of six types of research. Several years ago, the Special Interest Group (SIG) within the American Educational Research Association (AERA) focused on research on the gifted and talented. They voted to change the SIG’s name by adding creativity to its scope, signifying consensus not only on broadening the perspective regarding human excellence but also on uniting various traditions to establish a profound understanding of high potential. Recently, in 2021, a new volume was published, featuring 19 leading scholars from fields such as differential and cognitive psychology, gifted and talented studies, and creativity research. This volume serves to review their intellectual journeys in the realm of high potential (Dai and Sternberg 2021). On the practical front, the longstanding tradition of gifted education has come to acknowledge the significance of long-term development in achieving excellence (Dai and Chen 2013). Fortunately, it is the developmental science movement that has finally offered a metatheoretical perspective for integrating TD into a broader framework of human development. This framework not only addresses issues of developmental diversity in terms of distinct adaptive challenges and problematic development but also encompasses the kind of human development responsible for modern civilization and human prosperity, namely, talent development. Positioned within this larger context, we hold an optimistic view that TD research will evolve into a robust tradition within the field of use-inspired research. Much like its more advanced counterparts, developmental psychopathology or criminology, it may eventually reach a level of sophistication that merits the title of developmental talentology.

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 ppendix: Search Terms Used, Relevant Journals, and Key A Scholars Sampled Search Terms Used for Search on PsycInfo Database • ti(“talent*” OR “gifted*” OR “expert*” OR “professional” OR “excellence*”) AND ti(“emergence*” OR “process” OR “development*” OR “transition” OR “transformation” OR “trajectory*” OR “pathway*” OR “productivity” OR “achievement*” OR “identification” OR “recognition” OR “intervention*”) • ti(“gifted*”) AND ti(“talent*”) • ti(“creative contribution” OR “creative productivity”)

Relevant Journals Sampled (1) Talent Development and Excellence, (2) Expertise, (3) Gifted and Talented International, (4) Gifted Child Quarterly, (5) Psychology of Aesthetics, Creativity, and the Arts, (6) Psychology of music, (7) Psychology of Sport And Exercise, and (8) Sport Psychologist

Leading Researchers Sampled (1) Aaron Kozbelt, (2) Arne Güllich, (3) Camilla Benbow, (4) David Lubinski, (5) David Hambrick, (6) David Yun Dai, (7) Dean Keith Simonton, (8) Fernand Gobet, (9) Fredrik Ullen, (10) Jonathan Wai, (11) Keith Davids, (12) Rena Subotnik, (13) Robert Howard, (14) Roza Leikin, (15) Sally Reis, and (16) Steven Portenga.

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Chapter 11

Toward an Epistemology of Talent Development and Human Excellence

Throughout history, behavioral and psychological research has often mirrored the practices of the physical sciences by identifying and isolating variables that define the underlying patterns of behavior and psychological phenomena. However, the quantitative approaches have long been criticized as putting the horse before the cart: determining the methods before what to study (Koch 1992). In contemporary research, scholars have adopted a more balanced approach, incorporating both variable-­centered and person-centered methodologies, as suggested by Bergman and Magnusson (1997). This means that researchers now employ a combination of quantitative and qualitative methods, selecting the most appropriate approach based on the specific issues and questions they aim to address. In most cases, the choice of methodology is determined by the conceptualization of the research issues. For instance, if one embraces Gagné’s (2005) Differentiated Model of Giftedness and Talent (DMGT), then constructing prediction models involves the identification of variables related to natural aptitude, significant environmental and intrapersonal catalysts as predictors, and specific talent achievement outcomes as criteria. Alternatively, if an action/interaction theory of excellence, such as the one proposed by Glaveanu et al. (2013), is adopted, then the focus shifts to observing person-situation interactions or proximal processes, as outlined by Bronfenbrenner and Ceci (1994). In this case, person-centered approaches are utilized to capture the specific patterns of action and interaction, shedding light on the processes that unfold along the way. Conversely, methodology, which refers to a specific empirical approach to examining realities, can also exert an influence on theoretical perspectives. Take, for instance, the conventional outlook on individual development, which typically assumes that physical, cognitive, and socioemotional development progress linearly and orderly, guided by biologically determined universal patterns. However, by applying dynamic systems approaches, as demonstrated by Smith and Thelen (1993), it becomes apparent that even a seemingly straightforward normative process, such as infant motor development, is heavily relying on real-time adaptations © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4_11

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to task constraints in a dynamic way. This insight underscores the notion that nothing can be taken for granted in individual development. What we consider “norms” or standards of age-grade developmental changes, around which everything else is just deviations, happen to be a problematic a priori assumption. Thus, different methods used in research can reveal new aspects of the phenomenon under investigation that would be otherwise masked or obscured. Talent development (TD) has many ramifications regarding individual differences, environmental affordances and challenges, distinct developmental patterns of interaction and constraints, and trajectories and pathways, so on and so forth. The six types of research delineated in Chap. 3 and further elaborated in Chaps. 4, 5, 6, 7, 8 and 9 are meant to capture essential features of TD. As shown in these chapters, each type of research has its own methodological implications, contributions, and limitations. This concluding chapter aims to draw general guidelines and considerations from past research for guiding future research efforts. This chapter will be divided into three main sections. The first section discusses changes in research paradigm that reflect our changing understandings of TD; the second section discusses the distinction between variable-centered and person-centered approaches to TD, and how they complement each other; and the final section proposes a new epistemology of TD and human excellence that helps frame research in light of five central concepts with respective methodological implications.

11.1 How Different Research Paradigms Address Developmental Diversity, Specificity, and Complexity In history, epistemic stance toward talent and TD has experienced three phases: (a) talent was initially viewed as a trait or constellation of traits (e.g., Gagné 1985; Tannenbaum 1983; Terman 1925); (b) then, the focus was switching to a person-­ environment interactive approach (e.g., Treffinger and Feldhusen 1996; Papierno et al. 2005), and (c) finally, a developmental systems view was introduced in the discourse (Ceci et al. 2016; Dai 2021; Dai and Renzulli 2008). The following sections delineate the methodological implications of a particular epistemic stance mentioned above.

11.1.1 Addressing Developmental Diversity from a Parametric, Nomothetic Perspective Undoubtedly, comprehending talent and TD heavily relies on the examination of individual differences in developmental potential and changes. Yet, historically, this inquiry has predominantly belonged to the realm of psychometric research. Psychometrics is firmly grounded in the nomothetic perspective, positing that

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parametric assessments of various fundamental individual differences can provide universally applicable human traits or stable characteristics that maintain measurement unity, identity, and continuity across situations and over time. As an illustration, general intelligence is regarded as a unified construct that remains consistent across diverse situations and age groups, even though its content appears predominantly academic and lacks a single underlying psychological foundation (Neisser 1979). In other words, it is not a unitary construct. A label of general intelligence lends itself easily to a unidimensional conception of human potential and an IQ-stratified society (Herrnstein and Murray 1994; Gottfredson 1997). Later, mathematical, verbal, and spatial abilities are introduced as undergirding a general conception of intelligence (Snow 1992; Lubinski and Benbow 2006). In gifted and talented studies, TD models typically start with an assumption of some innate quality or high potential, like seeds, which, with the suitable soil and climate, will blossom and bear fruition (e.g., Terman 1925; see also Tannenbaum 1986; Gagné 2005, 2020; cf. Simonton 1999, 2018). The tendency to attribute various talent achievements to some simple genetic and environmental components or personal traits reflects a reductive logic in that the complexity of developmental diversity can be boiled down to presumably genetically based traits and environmental factors. What has exacerbated the situation is that developmental psychology focuses on age-graded normative development while framing developmental diversity or “deviations” from the “norm” as a separate issue of developmental consistency of individual differences over time (including intelligence and talent as well as personality traits); the practice of separation of normative development and individual differences was dubbed by McCall (1981) as “two disciplines of developmental psychology,” named after “two disciplines of psychology (Cronbach 1957).” As a result, the entire matter of TD, like developmental psychopathology and criminology, tends to be overlooked or relegated to a secondary “applied” position in developmental science. This occurs despite the early critique put forth by Feldman (1981), challenging the Piagetian tradition by emphasizing that some aspects of individual development follow non-universal trajectories and pathways. Furthermore, early scholars, such as Werner (1967), have urged researchers to explore the origins of personal creativity by studying the evolving individuality, a perspective echoed by Emde (1994) and Horowitz (2000). The nomothetic versus idiographic tension permeates the history of psychology in general (e.g., Allport 1937; Molenaar 2004), and TD and human exceptionalities research in particular (Dai 2010). The endeavors to chart a parametric landscape of talent distributions, as seen in the work of Feist (2006) and Lubinski and Benbow (2006), run parallel to the efforts directed at investigating individual cases of TD, as exemplified by Feldman (2003) and Glaveanu et al. (2013). Similarly, the recent initiatives aimed at merging the cognitive tradition of tracing expert performance with the psychometric tradition of identifying interindividual differences (Hambrick et al. 2018; Ullén et al. 2016) reflect an ongoing struggle in this field.

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11.1.2 Dynamic Interaction Approaches Studies of individual differences in the context of individual development inevitably face the fact that simply knowing individual traits does not tell us much about how individuals function in real social contexts (Emde 1994). In order to understand how developmental outcomes are contingent on certain traits such as aptitudes and dispositions, at least these traits have to be shown as “developmentally instigative” (Bronfenbrenner 1989) and thus intimately related to processes (Snow 1995). Thus, the term interaction denotes two different meanings: the first use refers to dynamic, reciprocal transactions with certain environments essential for some aspects of development, which can be as basic as crawling or as complex as scientific creativity; the second use of the term “interaction” refers to differential sensitivity or effectivity vis-à-vis a given environment in a way that creates a person-by-environment interaction effect: for example, some individuals enjoy a cognitive advantage and will benefit from a learning environment more than others, even when everyone benefits from it (Ceci and Papierno 2005). Multiplicative effects (or Matthew effects) are often used to describe a faster growth rate of some individuals with certain traits (see Chap. 5). Papierno et al. (2005) argued that the multiplicative effects of interaction do not necessarily mean that only those who possess the putative “natural talent” can proceed successfully; multiple origins and pathways are possible. This argument brings to the forefront the issue of equifinality (see Gottfried and Gottfried 2004 for an argument for the distinction of a motivational advantage). Ultimately, the issue of development itself concerns emergence; that is, development is not linearly determined as any single causal factor but demonstrated as structures, properties, and patterns emergent out of the real-time interplay of many factors.

11.1.3 A Focus on Emergence in Complex Systems Goldstein (1999) defined emergence as “the arising of novel and coherent structures, patterns, and properties during the process of self-­organization in complex systems” (p.  49). Note that the notion of emergence implies irreducibility: an observed new property or structure cannot be said to be caused by either personal or environmental conditions; rather, it emerges as a result of the self-organization of all relevant components through real-time interaction. When the long-term process of development creates structures, patterns, and properties leading to high-level performance or productivity (or, for that matter, horrible crimes or mental illness), it typically involves interactions across multiple levels of biological, cognitive-­ affective, social-cultural processes through epigenesis and sociocultural mediation (hence, developmental complexity). This theoretical formulation sharply contrasts a reductionist, unidirectional model of genetic determinism (see Gottlieb 1998) or environmental determinism (e.g., Howe et al. 1998) in accounting for TD. To sum up, what we observe as developmental diversity is just a summary of observations of many emergent and development patterns and regularities due to the

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complex interplay of endogenous and exogenous factors, not caused by any single factor alone, genetic or environmental. This is where the nomothetic trait explanations break down. Compared to the reductive logic of identifying a set of “first principles” capable of predicting long-term developmental outcomes, the logic of contextual-temporal emergence (Dai 2021 see Fig. 2.1, Chap. 2) inevitably leads to an epistemology of evolving complexity through identifying emergent structures, patterns, and properties every step of the way.

11.2 The Changing Methodology in Studying Developmental Diversity The changing ways we think about what needs to be known, which is delineated above, have influenced the methods used to tackle the problems. As an illustration, McCall (1981) introduced his “scoop model” of differential developmental changes and growth. In this model, development is initially perceived as uniform due to canalization and early childhood rearing experiences. However, then, it follows diverse trajectories, typically commencing around the entry into elementary school. While the model emphasizes differential mental development, the model remains unidimensional and quantitative. In other words, mental development continues to be perceived in normative terms along a hierarchy of high to low values (see also Carroll 1997). However, with micro-genetic, idiographic observations, Siegler (1988) was able to identify three different kinds of characteristic adaptation of first-­ grade students (i.e., good students, not-so-good students, and perfectionists), which are truly emergent patterns and properties while dealing with basic mathematical problems at hand. Increasingly, researchers have realized that traditional component-­ dominant statistics cannot handle the operations of complex systems and that interaction-­dominant models have to be introduced to account for dynamic changes in interactive and relational patterns (Hilpert and Marchand 2018). In the following sections, three central methodological issues for studying DT will be discussed: (a) variable-centered approach versus person-centered approach, (b) predictions based on soft constraint satisfaction (see Sect. 11.3.1), and (c) integrating different levels of analysis (see Sect. 11.3.2).

11.2.1 From Variable-Centered to Person-Centered Approaches As epitomized by Type 2 research (Chap. 5), variable-centered approaches aim to find out how components are associated with each other so that some kind of causal patterns can be inferred from longitudinal or cross-sectional data. In recent decades, however, the more interactionist and developmental systems focus calls for a more organismic, person-centered approach. Instead of partitioning complex variations of developmental outcomes into a manageable set of “control variables,” the

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person-­centered approach aims to identify developmental adaptations holistically. The following section provides an overview of methods that have been or could be used to tackle TD issues. Modeling Individual Differences  Bauer and Reyes (2010) identified three ways of modeling individual differences in the development of a particular characteristic (e.g., antisocial behavior or some aspect of talent development); each can be used to predict differential learning rates and accelerated talent development trajectories. A. Statistical models assuming homogeneity and continuity. When we view trajectories as continuous and individual paths as existing along a continuum (i.e., a variable distinguishing one individual from others in terms of degree), statistical methods such as hierarchical linear modeling (HLM), latent curve analysis (LCA), or growth curve modeling (Willett and Sayer 1994) can be effectively employed. Such modeling approaches can be employed to identify fluctuations in performance over time, particularly when performance can be assessed using a standardized measure that is easily interpretable and maintains measurement consistency. Examples of such measures include records in a hundred-meter sprint or elo ratings in chess or Go. When used in academic performance (e.g., test scores), adjustments would have to be made for developmental equivalence and measurement scale equivalency. Sometimes, the targeted measure is the trajectory of productivity, such as the number of publications or patents used (Simonton 1988, 2018). A caveat for such modeling is that relatively homogeneous groups, with group members sharing experiences of schooling (a school of science or arts) or training (e.g., a soccer camp), should be used to ensure proper interpretation. This kind of modeling helps assess a central tenet of TD: differential learning curves and asymptotes (Shiffrin 1996) or cumulative advantage over time (e.g., Matthew effects). B. Statistical models assuming heterogeneity and developmental discontinuity. When individual trajectories are not continuous, indicating that the group or population being studied is heterogeneous and varies in nature, Bauer and Reyes (2010) have proposed the use of Nagin’s (1999) semi-parametric, group-based trajectory model. In a diverse population, it becomes more plausible to acknowledge developmental diversity in terms of specific talent development pathways exhibiting significantly different trajectories. Consequently, it is reasonable to anticipate accelerated rates of talent development for certain individuals, implying a form of developmental discontinuity. Even within a homogeneous group of talented adolescents, much heterogeneity exists. For example, Lubinski and Benbow (2006) show that in a highly selective group of talented teenagers, the bottom quarter and top quarter significantly differ in long-term achievements and productivity. In contrast, Papierno et  al. (2005) suggested a hypothetical model of TD by which one might start low (i.e., late bloomer) but have a steep growth curve that surpasses many, while another might start reasonably high but have a growth curve that flattens faster than others. The reasons for such observed heterogeneity must be complex. Though unwieldy, such structured

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modeling can still reveal important regularities with parametric stability and nomothetical reach. C. Statistical models assuming the presence of both degree and kind. This analytic approach retains the parametric features for estimating individual trajectories while allowing for the presence of different kinds (i.e., qualitative differences and the homogeneity of subgroups within the sample). Bauer and Reyes (2010) suggested using Muthén’s (2001) general growth mixture model (see also Muthén and Muthén 2000) as one way to tackle the complexity of data. Basically, it attempts to nest trajectories that differ in degrees into several discrete classes so that one ends up with several latent classes in terms of developmental patterns. In TD research, such an approach can be used to distinguish developmentally more robust groups from more fragile ones. When we assume the population to be heterogeneous (with different homogeneous subgroups), we are bordering on person-centered approaches. Before we look into the person-­ centered approach, let us begin by introducing the variable-centered approach. Variable-Centered Approach  Variable-centered statistical modeling helps address accelerated talent development, especially the Mathew effects. Conceptualizing and modeling talent trajectories in a quantified manner faces several constraints. Certain talent domains lend themselves well to variable-centered statistical modeling, such as most performance-based fields. On the other hand, some domains, like leadership and entrepreneurship, may resist such quantification efforts. Then, there are domains, such as production, which fall somewhere in between these two extremes. Even when these statistical models hold, what is behind these trajectories (distal and proximal causes for observed patterns) still needs to be explicated with properly selected predictor variables. Den Hartig et al. (2016) suggested a versatile dynamic network modeling of several interactive variables for the purpose. The dynamic network modeling allows effects to be propagated to other variables, thus overcoming the limitations of traditional linear modeling of prediction (i.e., not capable of modeling complex dynamic interaction and reciprocation effects). Variable-centered approaches encounter a significant challenge when applied to talent development. Many trajectories deviate from the conventional patterns of acceleration or earlier peaks and instead demonstrate diverse directions and a sequence of distinct life events. These developmental patterns are not just quantitatively distinct; they represent fundamental differences. What develops in these cases is not a straightforward, continuous performance measure like chess ratings. Instead, it involves a succession of personal strivings, evolving concerns, or the emergence of novel properties, such as adopting a new perspective on one’s pursuits. For instance, Jean Piaget’s shift from biology to philosophy during his teenage years played a pivotal role in shaping his later contributions to child development. However, these evolving trajectories defy quantitative measurement in comparison to others. When a scientist transitions their focus from developmental biology to gene editing or when an antisocial tendency transforms into a criminal intent, these changes signify not just an increase in the same attributes but a qualitative shift in development. It is the emergence of new structures,

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properties, and patterns through interaction with the environment. Consequently, it becomes essential to shift to a different mode of inquiry: person-centered approaches. Person-Centered Approach  Person-centered approaches are based on the basic assumption that the person is an organism and has to be studied as a whole (Magnusson 2001) rather than represented as a set of variables calibrated based on comparison with the norms of peers. The example of micro-genetic study of first-­ grade students mentioned earlier (Siegler 1988) is an example of a person-centered approach (see Dai et  al. 2015 for a TD study). Bergman and Magnusson (1997) summarized the theoretical underpinnings of individual development as follows: A. The process is partly specific to individuals. B. The process is complex and is conceptualized as involving many factors that interact at various levels, which may be mutually related in a complex manner. C. There is a meaningful coherence and structure in individual growth and its distributions. D. Processes occur in a lawful way as patterns of operating interacting factors. E. There are intraindividual and interindividual consistencies in process characteristics. Type 3 research, as discussed in Chap. 6, aligns well with these characterizations. A more refined definition of person-centered approaches rejects the notion of variables as strictly parametric and instead considers them as operating within a homogeneous group (Laursen and Hoff 2006; Muthén and Muthén 2000). Here, the central aspect of person-centered approaches, for the purposes of our discussion, is the acknowledgment of the person as an open, functional, adaptive agent rather than reducing them to mere variables, as if the person were a puppet with numerous variables pulling the strings behind the scenes. Within this framework, various distinctive forms of person-situation interaction that reveal the capacities and tendencies of an agent can be conceptualized (Mischel and Shoda 1995; Snow 1992; see Chap. 6). When defined in this manner, person-centered approaches are fundamentally interactionist (Bergman and Magnusson 1997), emphasizing the use of the person-in-context/time as the unit of analysis (Bronfenbrenner 1989) rather than treating the person as an array of decontextualized variables.

11.2.2 Person-Centered Approach I: Group Research Tracking Qualitative Different Patterns of Talent Trajectories Instead of determining a general model of developmental trajectories that can cast one individual’s growth curve against the norm of peers to reveal its trajectory, research in the spirit of person-centered approaches can track individual patterning of multiple traits or variables to identify differential developmental trends in knowledge/skill set, career interests, and value orientations (Ackerman and Heggestad

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1997). Typically, non-parametric methods, such as cluster analysis or latent class analysis, are used to sort out homogeneous subgroups, and each group can be further analyzed to reveal their distinct trajectories, pathways, and even long-term achievements (Lubinski and Benbow 2006). Alternatively, a homogeneous group of individuals (e.g., child prodigies) can be investigated in depth (e.g., Feldman 1986) and compared with another group of comparable ages (e.g., mono savants) to reveal important differences in functional and developmental patterns between the two (e.g., Miller 2005; see also Feldman 2003).

11.2.3 Person-Centered Approach II: Field Research Identifying Distinct Contextualized Developmental Events and Patterns In contrast to group-based studies that focus on distinct subgroups and specific types of individuals, person-centered field research in individual development places a higher emphasis on fully contextualized events with significant developmental implications. This approach is akin to person-centered personality research, which explored contextualized concerns (McAdams 1996), life tasks (Cantor 1990), or personal strivings (Emmons 1986). Within the realm of TD, critical junctures often serve as focal points for investigation. These junctures could involve negotiating transitions in domains like musical development (e.g., MacNamara et al. 2008) or transitioning into highly selective early college entrance programs (Dai et  al. 2015). For instance, MacNamara et  al. (2008) conducted an interview study and identified a set of behavioral and psychosocial indicators crucial for adaptation to full-time professional training or a career. These indicators included qualities like determination, adaptability, and realistic evaluation, which the authors summarized as psychological characteristics of developing excellence (PCDE). Similarly, Dai et al. (2015) conducted a retrospective interview study and identified divergent patterns of coping and growth (Cope-and-Grow) over the course of a 4-year college experience. In comparison to previous research types, field studies like those by MacNamara et al. (2008) or Dai et al. offer a more intimate and up-and-close examination of the “negotiation” process that is pivotal for understanding success and failure when striving for achievement and transitioning to higher levels of the pursuit of excellence.

11.2.4 Person-Centered Approach III: Psycho-biographical Studies Mapping Long-Term Trajectories of TD and Creativity The person-centered field research discussed above is situated in a specific context of TD involving extended proximal processes and long-term trajectories (i.e., career prospects). In contrast, psycho-biographical studies provide an idiographic look at

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critical events and psychological changes of some prominent historical figures or living individuals in their development. Using what Gruber called an Evolving Systems Approach (ESA; see Gruber and Wallace 2001), these studies show how developmental events and changes led to major discoveries or innovations (e.g., Gruber 1981 on Darwin; Gardner 1993 on creative minds). While psycho-­ biographical studies tend to study renowned, highly accomplished individuals, the hope is that once we understand the most advanced and cultivated minds, we can better understand many minds of the same kind, even with less stellar accomplishments. Summary on Person-Centered Approaches  A common thread running through all three person-centered approaches is the belief that truth is to be gained, not by seeking nomothetic “first principles” (such as the idea of a Galtonian essence of genius or general intelligence) and treating the developmental process as derivative, but by examining developmental patterns that occur in real-time and real-life situations for specific individuals. In essence, it involves describing and elucidating the developmental emergence of patterns, structures, and properties as they unfold in their unique contexts and with their distinct timing/duration. In short, it aims to achieve developmental specificity. The person-centered approach eschews relying on a priori assumptions about “control variables.” Admittedly, this type of research may appear less rigorous from a positivist standpoint, as it unavoidably involves subjectivity when interpreting observed and reported behavioral and psychological events. However, in exchange for this subjectivity, it offers the freedom to explore personal and situational nuances of talent and TD in a clinical manner that conventional scientific methods cannot access. For instance, based on person-centered approaches, one could argue that personal vulnerabilities, as exemplified by individuals like Van Gogh or Michael Jackson, are not merely liabilities; they might have been catalysts for their creative impulses and expressions (Tordjman et  al. 2020). Through careful analysis, such cases might reveal relevant talent prototypes across a wide spectrum of talent domains. When rigorously studied, these cases can evolve into inductive developmental systems (Wood 2014) capable of withstanding the tests of time and scrutiny.

11.3 Seeking Developmental Specificity and Complexity in Explaining Developmental Diversity As already mentioned, if TD ultimately reveals developmental diversity in a population, qualitative or quantitative, seeking developmental specificity and complexity is the only way to understand what exactly transpires behind the surface of developmental diversity.

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11.3.1 Developmental Specificity: Person/Process in Context/Time If developmental processes, transitions, and changes have to be taken seriously rather than for granted, as repeatedly stressed in this book, can we predict these events given the particular functional history of the person in question? If emergence is more prevalent in human development compared to our ancestors and non-­ language animals, whose developmental paths are more or less predetermined by their biology, can we predict what leads to the emergence of novel structures, patterns, and properties, be it the development of criminal behavior, psychopathology, or extraordinary talent accomplishments? Smith and Thelen (1993) employed dynamic systems theory to elucidate the non-linear aspects of motor development, highlighting that changes within subsystems can trigger phase transitions through self-organization. Such a theory can fill the gap in Simonton’s (1999) emergenic-epigenetic theory of TD by providing a process explanation (e.g., phase transitions). Siegler’s (1996) overlapping wave theory characterizes development as a process of variation and selection analogous to evolution rather than assuming development as linear and orderly changes, as a staircase development model suggests (Case 1992). Developmental specificity entails explications of micro-level developmental processes rather than relying on the normative assumption of development. Even when it comes to the timing of pubertal changes, presumably following strictly a biological timetable, the absence of the father, a social condition, can affect the onset of menstrual periods (Schlomer and Marceau 2022). When TD is concerned, developmental specificity becomes even more important, given its largely non-universal nature (Feldman 2003, 2020). Soft Constraint Satisfaction  The aim of constructing more precise and nuanced prediction models for developmental transitions in TD, as discussed in the exploration of Type 4 research in Chap. 7, is rooted in the principle of domain specificity. To genuinely craft TD models as developmental models, it becomes imperative to elucidate the processes and changes occurring during development. When Renzulli asserts that gifted and talented behavior occurs for some individuals in some places at some times, it indicates the condition of several constraining factors, including what the person brings to the situation (see also Simonton 1999), as well as ­facilitating or impeding environmental and developmental conditions (Subotnik et al. 2011). In Gibson’s (1979) ecological theory, affordances are conditions that afford certain actions for goal attainment, while constraints are conditions that must be satisfied to reach a goal. Thus, an apple on the table affords a baby the action of grabbing the apple; however, the baby needs to satisfy the constraint of being able to reach out with motor action (with visual-motor coordination) to get the apple. In computer science, a constraining factor that always holds is called hard constraint (i.e., always working as a necessary condition). An example can be specific body metabolism rates that constrain the swimming speed. Constraints that are allowed to be violated are called soft constraints. When we say that the prediction models in TD

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fit the criterion of soft constraint satisfaction (see Chap. 7), we are suggesting the robustness (or resilience) of a complex system for certain changes. Risks and protective factors identified in developmental criminology are likely soft constraints, just like facilitative or inhibitory conditions for the transition to the next level of excellence. For example: • A particular weakness in talent may be compensated for by other members when working as a team. • Instruction or mentorship is often needed when one deals with a more advanced topic, but some individuals capable of autodidactic study (learning by oneself) often move ahead without being impeded by this constraint. • Adversity is usually considered a hindrance to TD. However, some individuals are highly resilient and can quickly rebound from challenging circumstances and setbacks. Although the Karenina Principle suggests all components need to be in place for TD to move forward smoothly, the principle of soft constraint satisfaction suggests the plausible equifinality for a particular talent trajectory or pathway. Therefore, in a discriminant function analysis, some predictors may prove essential and necessary, while others are significant but secondary. The principle of soft constraint satisfaction determines that talent trajectories, pathways, and achievement patterns are indeterminate but principled (Lewis 2000). In other words, they are predictable once we know that certain constraints are more salient and dominant than others. For example, in India or China, parental expectations, compared to those of Western parents, play a more important role than one’s own personal interests in determining one’s career paths. Then, technically, in a prediction model, this variable as a predictor should carry different weights in the prediction equation, depending on culture.

11.3.2 Developmental Complexity That Integrates the Biological, Psychosocial, and Existential In specifying developmental diversity, we will inevitably encounter the issue of TD cutting across several levels of analysis. It is much easier to describe emergent physical structures, patterns, and properties than biological, psychosocial, and spiritual ones. For example, we have no problem demonstrating that prolonged musical training likely changes musicians’ brains structurally as well as functionally (Schlaug 2001). We also can demonstrate that certain psychological properties arise from social structures and relations, such as kinship that enhances emotional bonds or a community of practitioners pushing its members to work on the edge of their competence and surpass themselves (Bereiter and Scardamalia 1993). However, we often suffer from aphasia, or a loss in language with which to address something that seems to come from our personal and spiritual existence, concerns that are existential in nature (Emmons 1986). Indeed, many aesthetical, intellectual, or

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spiritual properties can be observed not only in adulthood but also in childhood (Shavinina 2009). The issue is not whether we are prone to the error of Cartesian dualism, elevating some mental properties or subjective states to a level that transcends our physical, social, and biological existence. Instead, the issue is how to create a language that allows us to describe phenomena that run across biological, psychosocial, and existential. Functionalism is one way to deal with the problem of tackling subjective experiences and mental content. In the next section, we will discuss functional representational coherence as another way to deal with multi-­ layered realities and overcome the problem of dualism.

11.4 Toward an Epistemology of Talent Development (TD) and Human Excellence Unlike fields like developmental psychopathology or criminology, TD lacks a well-­ articulated disciplinary foundation, as discussed in Chap. 10. Instead, it consists of numerous isolated pockets of researchers who often pursue their individual interests (“do their own things”) rather than coming together to form a cohesive community of scholars and researchers with a shared vision for the future of developmental talentology. Developing a consensus on the nature, scope, and methodology of TD research is essential but currently elusive. In the meantime, considering a tentative proposal for an epistemology of TD and human excellence may serve as a step toward achieving this common goal. An epistemology of TD and human excellence consists of (a) what we believe to be the nature of various phenomena of human excellence (i.e., ontological commitments), (b) how we should go about investigating relevant phenomena, and (c) what counts as valid knowledge claims about TD and human excellence. George Box, a British statistician, used to say that all models are wrong, but some are useful (Box 1976). Functionally, an epistemology of TD suggests ways and approaches that do not guarantee success but can potentially guide research in terms of thinking through a research project conceptually, strategically, and methodologically. For this purpose, the following section uses five basic concepts to form a “central conceptual structure” (Case 1992) for TD research: engagement, divergence, emergence, excellence, and coherence. The sequence of these concepts is not random; it adheres to a logic of developmental changes. Within each concept, at least three facets can be investigated to potentially unveil the fundamental principles underlying the comprehension of talent development. It is important to note that the resulting list of topics and issues presented here does not encompass all aspects of talent and TD. Instead, the aim of this discussion is to delve into the foundational concepts of TD and their strategic and methodological implications for constructing a robust research framework. Such a framework should ideally be convergent, ultimately leading to the formulation of guiding principles for policy and practice in the realm of TD.

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11.4.1 Three Features of Engagement That Promote Talent Development (TD) The term engagement indicates an intentional act to build a meaningful person-­ object relation, be it listening to a piece of music or participating in a sports event. Some engagements can be shallow, and other engagements are extended and deeper and can be developmentally consequential (Bronfenbrenner and Ceci 1994). As a central concept of TD, “engagement” emphasizes the primacy of action over innate capacity. In other words, extended and dynamic interactions with specific tasks and social environments are crucial for giving rise to talent and fostering talent development. This type of TD-facilitating engagement exhibits at least three distinctive characteristics. Engagement That Is Adaptive  Adaptation here means attunement. When an infant is engaged with a mobile hanging above the crib, the mobile affords an action of kicking (hence, affordances); however, to enact kicking, the infant has to satisfy or overcome the constraints of an action (e.g., reaching out with one’s leg). By the same token, what Piaget (1950) viewed as adaption that leads to cognitive development is a special form of engagement that either brings the world in line with one’s perspective (assimilation) or constantly updates one’s perspective to keep up with the complexity of the world (accommodation). The notion of adaptation also implies that individuals wittingly or unwittingly seek a personal niche well suited not merely for survival but for thriving as human beings. Thus, extended engagement means building a functional and personal relationship with one’s environment that is intrinsically rewarding and extrinsically instrumental. One can still conceptualize individual differences in capacities as essential for understanding “natural talent.” However, the adaptive nature of real-time engagement itself should not be obscured. Engagement That Is Progressively Deepening  Engagement with a task environment can vary in depth, depending on several factors. From an external perspective, we can consider whether there is enough environmental stimulation to arouse one’s interest and motivation for further, deeper exploration, as well as continuous opportunities to engage with a particular activity or subject. In this regard, the availability of educational resources and provisions, both in formal and informal settings, plays a crucial role (as discussed in Type 6 research in Chap. 9). Vygotsky’s concept of the zone of proximal development, which includes the influence of more knowledgeable or competent peers or individuals (Dai 2020), is clearly pertinent. Internally, deep engagement implies a higher level of personal investment and dedication, accompanied by improved mastery and enhanced agency for productive or performance-related activities. Deep engagement typically blends elements of playfulness with seriousness, both intrinsic and instrumental, and can be observed through laboratory or field research (e.g., Csikszentmihalyi et al. 1993).

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Engagement That Is Sustained  Human engagement entails an active individual taking action within a task involving performance or production, such as deliberate practice in a piano studio (Ericsson 2006) or extended problem-solving in a science classroom (Bereiter and Scardamalia 1993; Scardamalia and Bereiter 2006). Such a task environment not only encourages specific actions (as highlighted by Glaveanu et al. in 2013), but it also possesses structural characteristics that influence the manner of extended participation that facilitates progressive deepening, whether it involves a strong narrative or a creative design. Although sustained engagement is occasionally attributed to internal factors like grit (as discussed by Duckworth in 2016) or external factors like a conducive niche construction or a supportive learning ecology (as described by Barron in 2006), it is more accurately conceptualized as relational. If we view engagement through a relational lens, our units of analysis must encompass the relational aspects of task engagement that contribute to its sustainability. In this context, deep engagement signifies a micro-level progression of talent in response to new opportunities and affordances, as well as emerging constraints and challenges.

11.4.2 Three Ways of Divergence As mentioned before, Lykken (1992) highlighted the inherent tension between the parametric and structural approaches we employ to delineate the concept of “nature.” In essence, we may succeed in quantitatively mapping the growth curves and quantitative trajectories of various interests, yet remain challenged when it comes to comprehending the evolving structural properties of the mind and the patterns of interaction that underlie them. For a simple example, while Charles Spearman (1904) declared that general intelligence is once and for all “objectively determined and measured” (p.  201), Alfred Binet was still fascinated by the “ideographical complexity” of how an individual child handles a task of intelligence at hand (see Brody 2000, p. 19). Thus, the parametric mapping (interindividual differences) does not solve the problem of what exactly transpires at a structural-functional level (within-person dynamics or idiographical complexity). The central task of TD research is twofold: (a) to map out different growth curves and developmental trajectories, including long-term cumulative advantages (e.g., Matthew effects; Ceci and Papierno 2005) and talent distributions (Lubinski and Benbow 2021), which is the main task of Type 2 research (Chap. 5), and (b) to identify the contextual-­ temporal emergence of novel structures, properties, and patterns as adaptation to demands and constraints of various task environments, which is the main task Type 3 research (Chap. 6). Divergence can be studied in three ways. Divergence as a Function of Characteristic Adaptation vis-à-vis a Variety of Cultural Niches  In most cases, divergence is due to characteristic adaptation (McAdams and Pals 2006), leading to individual differences in a wide range of talent trajectories and pathways. These distinctions can be quantitatively observed

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through accelerated career trajectories (as detailed in Chap. 5) or qualitatively seen in individuals’ choices regarding pursuing different career paths that align with their unique potential and preferences (as explored in Chap. 6). Quantitatively, the Pareto Principle (20/80 ratio) might apply every step of the way to account for the diversity within a group in terms of the likelihood of advancing to the next level of excellence, and social distribution of domain talents can be charted from a sociometric perspective (see Chaps. 5 and 8). Divergence as a Function of Age and Domain  McCall (1981) proposed that the divergence in individual development typically begins at around 6  years of age. Behavioral genetics research conducted by Plomin et al. (2003) suggested that this developmental divergence becomes more pronounced during adolescence, likely as a result of increased autonomy and self-direction. It is also worth noting that different talent domains have their unique developmental timelines concerning the onset and peak performance (Simonton 1988, 2018; Subotnik et al. 2011). The nature of talent domains would prompt the onset of non-universal development (Feldman 1994, 2003) as well, given the optimal opportunities and provisions. From an environmental facilitation point of view, with the exposure and experiences, certain doors for TD open up, and other paths close down (Silverstein 1988). Divergence as a Function of “Abnormal” Development  “Abnormal” development in the context of TD can refer to any phenomenon significantly deviating from what is commonly observed, such as extreme forms of precocity (child prodigies), savant syndrome, twice exceptionalities (e.g., gifted individuals with Asperger’s Syndrome), or anything that fits with Geschwind and Galaburda’s (1987) hypothesis of “a pathology of superiority”; that is, “minor malformations may often be associated, not with malfunctions, but with distinct superior capacities in some areas” (p.  65). Such abnormal developmental trajectories serve as a reminder to exercise caution and avoid assuming nomothetic regularities, as these instances demonstrate that alternative developmental pathways to excellence can and do exist.

11.4.3 Three Levels of Emergence The basic concept of emergence hinges on the notion of irreducibility of emergent structure, patterns, and properties to lower-level explanations, genetic or environmental. It becomes clear that the majority of what we commonly refer to as “development” constitutes a prime example of emergence and self-organization. Thus, the key to understanding developmental complexity of talent lies in explicating what exactly emerges every step of the way, leading to some form of peak performance or creative productivity. The notion of emergence implies non-linearity in the sense that there is a single one-to-one cause or source of origins (e.g., unidirectional genetic causation or environmental conditioning) that can explain the emergence; rather, the emergence of new system properties and patterns is more like a

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combustion effect of several factors operating in concert. Consequently, a talent trajectory can have growth spurts and ups and downs. The Emergence of Talent  The term “talent” is often used in two different ways. One is inherently psychometric and social-comparative (e.g., she is the most talented in the group), and the other is an ascription of a distinct, discrete personal quality (e.g., she has a talent for music). When we refer to TD as the emergence of talent (Simonton 1999), we are using the term in the second sense, which is in line with the criteria of excellence, domain specificity, and authenticity (see Chap. 2). If we discuss mathematical, artistic, verbal-narrative aspects of cognitive development as domain-specific development (involving “central conceptual structures;” Case 1992; see Porath 2006), the question regarding the emergence of talent is as follows: when does this intuitive apparatus emerge, and how it is incorporated into a real-life pursuit of excellence? Given the substantial heterogeneity observed in the human brain (Karmiloff-Smith 2004), without an innate foundation (i.e., not hard-wired by genetic instructions) for talent, an essential question arises: how do various rudimentary forms of bio-ecological effectivity get constructed during formative years, operating at neural, psychological, and behavioral levels through the proximal processes of interaction with task environments? The notion of emergence helps problematize talent not as a capacity or structure pre-ordained at birth, only to be brought into play, but as the emergent property of a functional relationship with specific task environments. The very emergence of talent is not to be taken for granted (Simonton 1999). The Emergence of Practices in Culturally Defined Domains  A cultural domain consists of a set of conventions and practices meant to achieve specific goals; it can be formally institutionalized with norms and gatekeepers (conservatories and universities) or informally created niche constructions that are more flexibly accessible (e.g., libraries and museums). In this sense, TD is largely a process of social participation and enculturation for a variety of ecological and cultural niches available at a particular social-historical moment; talent development goes hand-in-hand with cultural affordances, tools, and constraints (Cole 2006). Thus, collaboration or creative productivity can be structural properties of a system, with little to do with an individual’s intention. In other words, tasks and actions are structured or “designed” through conventions and practices of a domain to foster collaboration and creativity (Glaveanu et al. 2013). Emergence as a New Level of Self-Organization  TD entails the emergence of new levels of self-organization. The evolution of competence, interest, and identity, as discussed in Chap. 6, exemplifies these novel self-organizational levels. In essence, TD is characterized by a shift from a more spontaneous, bottom-up self-­ organization toward a progressively purposeful, top-down self-organization. It becomes increasingly self-aware of one’s niche potential and valence, as proposed by Dai (2021, 2024). A person-centered approach must take precedence over a variable-­centered approach, as only the former can provide insights into the devel-

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opment of evolving individuality rather than merely focusing on individual trait differences. A person-centered approach permits the observation and integration of a cluster of observations of milestone achievements (talent strengths), interests, self-concepts, and aspirations into a distinct pattern and direction (developmental corridors, trajectories, and pathways).

11.4.4 Three Perspectives on Excellence In most cases, TD involves sustained goal-directed activities aiming to achieve something outstanding by professional and cultural standards. Just as there is much developmental divergence regarding talent strengths, interests, and directions, there are many kinds of excellence. For example, some talents help improve human conditions and make society more prosperous (e.g., engineering); others enrich many people’s inner lives (e.g., arts). Eminence, or rewarding excellence with social accolades based on the social importance of contributions to society, is clearly an essential way of recognizing and promoting excellence (Subotnik et al. 2011). However, we should also consider excellence as pervasive in all human endeavors (not just the prestigious ones) and should be equally encouraged and recognized regardless of the degrees of eminence and social importance. Thus, the outstanding craftsmanship and creativity of a chef, carpenter, or plumber are not intrinsically of less value compared to the achievements of an eminent scientist, artist, or athlete. All of them are worth careful research attention, as expertise researchers have done (Ericsson et al. 2006). Excellence Through Different Paths  The idea of having a single account of excellence is tempting whether it favors a nature account or a nurture explanation; Ockham’s razor is considered not merely simple but sharper, as it were. Alas, from the viewpoint of emergence or evolving complexity, not only all roads lead to Rome (i.e., equifinality); the same road can branch out to reach different destinations (i.e., multifinality). Tannenbaum (1997) identified two modes of functioning, production (of thoughts and tangible products) and performance (staged artistry and others). Such a classification is helpful as a first approximation but still too broad-brushed (see Lohman 2005). The talent potential for gymnastics is quite different from that for basketball, and even within basketball, what is considered a good fit for a point guard is usually a poor fit for a center. Glaveanu et al. (2013) studied five production domains (science, design, art, scriptwriting, music composition), which show differences as well as commonalities in terms of patterns of action/interaction in these “creative” domains (see also Feist 1998). Niche potential and valence are always specific to the person in question (i.e., idiographic), which can be as narrow as a mono-savant and as versatile as a polymath (e.g., Da Vinci).

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Excellence Through Different Forms  Although Anders Ericsson insisted on using “reproducible superior performance” in controlled settings as the criterion for judging levels of expertise objectively (Ericsson et  al. 2005, 2007; Ericsson and Williams 2007), it can be argued that the criterion is more applicable to performance domains (e.g., chess, track and field, or figure skating) than to production or human service (leadership) domains, as the latter involves more than skill execution, and the achievements in the latter are often assessed in terms of social impact they make, rather than based on performance of structured tasks amenable to reliable assessment in controlled settings, as in expertise research. Excellence as a Social Process  The path to excellence is a journey of social engagement, starting with legitimate peripheral participation (LPP; Lave and Wenger 1991). Gradually, individuals transition from the periphery to the center of their respective domains. For instance, one may start as an apprentice and, over time, ascend to the status of a master in performance domains. Alternatively, a newcomer might introduce innovative ideas that were initially met with skepticism and viewed as unconventional but eventually gain recognition as genuinely original and insightful (Kagan 2002). The progression toward a leadership role inherently relies on social interactions. Consequently, a distinct cultural dimension is embedded within the concept of excellence, driven by values and norms. Notably, some talents, such as talented robbers or criminal computer hackers, remain as outcasts and are never accepted by mainstream culture for moral or legal reasons.

11.4.5 Three Issues of Coherence Coherence represents a distinctive characteristic of a system in which all its components operate harmoniously to achieve its functional objectives. In the context of TD, coherence operates on two levels. First, functional and representational coherence pertains to the essence of TD as a process of building coherence. This is evident in cases such as the intellectual journeys of Charles Darwin or Jean Piaget, where individuals coordinate their strengths, sensitivities, and interests to craft innovative life paths, similar to the approach taken by figures like Steve Jobs or Elon Musk. Second, epistemic coherence relates to the epistemology of TD, which seeks a coherent approach to gain a profound understanding of its intricate richness and complexity. Functional Coherence  Both the concepts of engagement and emergence underscore the significance of functional coherence and autonomy within TD. Adaptive, deep, and sustained engagement requires coherence-building in terms of well-­ coordinated personal aspirations and the selection of niches conducive to self-­ actualization. The idea of emergence and self-organization in dynamic, complex systems highlights the bottom-up emergence of structures, patterns, and properties (Goldstein 1999) during interactions with the environment. In this context, one’s

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developmental experience naturally organizes itself. Therefore, Steve Jobs’s concept of “connecting the dots” reflects a process of bottom-up self-organization without the need for a deliberate plan, resulting in a more coherent organization of individual functioning. Simonton (1999) described scientific creativity as a constrained stochastic process involving variation and selection. Nevertheless, there are limits to the effectiveness of bottom-up coherence-building. The emergence of new understandings through neural network modeling, such as how GPT-4 absorbs and processes human discourse to generate its own, and the emergence of social networks of ideas and their propagation (Ferguson 2018), can undoubtedly drive human development and excellence. This argument applies to macro-level patterns of development. However, to maintain order and coherence, top-down control is not only necessary but also crucial. Hence, Dai and Renzulli (2008) stressed both experience-­producing (bottom-up) and experience-organizing (top-down) characteristics that drive TD at a micro-level. The tendency toward functional coherence reflects the principle of autopoiesis in that the living system can maintain and renew itself through cognition for effective adaptation (Maturana and Varela 1980). Representational Coherence  Human representations of the world and the self can be implicit, situated, and embodied, as well as explicit, abstract, and symbolic. Sameroff (2010) argued that a representational model is needed for any developmental theory to specify one’s evolving relationship with the world in which one finds himself/herself. These representations are intimately associated with one’s action and potential action. In this regard, building representational coherence at a micro-level in individual development is just a more covert process of building functional coherence. Bloom (1985) and Sosniak (2006) described a process of TD by which a person’s representations of the relationship with a task environment go through significant shifts. Using a dynamic system’s language, the relationship itself is evolving and self-organized toward a higher-order coherence (Lewis 2000). This form of representational creativity is how one’s evolving individuality ultimately shines in personal excellence and how it is shared and propagated in communities to make a difference. Epistemic Coherence  While functional and representational coherence provides a backbone of a theory of TD that is non-reductionistic and truly developmental, epistemic coherence is concerned with how researchers of TD themselves maintain their conceptual coherence while ensuring responsiveness and sensitivity to the changing realities and new evidence so that their epistemic stance does not become stagnant over time. Once we understand developmental complexity as starting with the assumption of engagement, proceeding with divergence and emergence, and ending with excellence and functional and representational coherence, we have a better sense of the basic logic of TD from a developmental science perspective.

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11.5 Conclusion In the final analysis, the purpose of an epistemology of TD and human excellence is to provide researchers with insights into the primary developmental issues and foundations of TD. It aims to guide researchers in addressing these issues and elucidating these foundations in a methodologically rigorous yet appropriate manner. As advocated in Chap. 3, there remains a necessity to “divide and conquer” the various facets of TD leading to excellence. Chaps. 4, 5, 6, 7, 8 and 9 extensively explore each of the six types of research, allowing for informed decisions regarding the selection of appropriate methods for specific research questions and issues. Nevertheless, this concluding chapter offers an overarching perspective on TD in all its richness and complexity. Science, akin to Neurath’s boat metaphor (Neurath 1952), is in a perpetual state of construction. It retains certain established components while continually adding new ones, serving as a temporary yet functional structure, much like a vessel afloat and in motion. This TD book serves as a guide for use-inspired research (Stokes 1997), aiming to synthesize diverse research strands into a unified research agenda to counteract fragmentation. Moreover, its primary objective is to ignite novel research endeavors that seek profound understanding and pave the way for innovative and practical solutions to advance TD. Indeed, the pursuit of TD research is inherently iterative, with foundational work informing applied research and vice versa. Finally, it is essential to remember that TD research is inherently normative and value-driven, aiming not only to find who are talented and in what ways they become so, but also to find ways to help the majority of the willing to realize their potential and achieve excellence in their own ways. It extends beyond research and understanding, intended to inspire as well as inform.

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Index

D Developmental changes developmental complexity, 7, 28, 42, 43, 45, 82, 106, 117, 118, 155, 192, 197–200, 204, 216, 224–225, 228, 232 developmental continuity vs. discontinuity, 149 developmental specificity, 7, 41–44, 82, 85, 88, 97, 106, 114, 117, 154, 163, 172, 183, 184, 197, 202, 222–225 developmental transitions, 43, 106, 108, 112, 125, 129, 130, 194, 223 measurement continuity, 126, 149 Developmental constraints hard constraints, 135, 223 soft constraints, 135, 217, 223, 224 Developmental diversity developmental criminology, vii, viii, 2–4, 8, 41, 46, 52, 192, 200–206, 224 developmental precocity, 15, 157 developmental psychopathology, vii, viii, 2–4, 8, 41, 43, 46, 52, 182, 191, 200–206, 215, 225 differential sensitivity/effectivity, 216 differential susceptibility paradigm, 194 divergent development, 41, 47, 50, 79–97, 104, 111, 126, 130, 169 equifinality and multifinality, 43, 200, 204 Developmental milestones achievement milestones, 126, 131–134 psychosocial milestones, 125, 131–135

Developmental sciences, vii, viii, 2, 3, 7, 8, 29, 30, 37–46, 49, 81, 82, 91, 94–96, 119, 128, 148, 154–163, 170, 178–179, 191–193, 195, 196, 199, 203, 206, 215, 232 Developmental timing onset, 26, 30, 50, 125, 133, 157, 197, 202, 228 peak performance, 86, 89, 90, 94, 95, 133, 152, 199, 228 precocity, 11, 14–15, 44, 62, 86, 89, 94, 109, 135, 147, 148, 157, 228 Distal factors vs. proximal factors, 84–85, 155, 219 E Emergence equifinality and multifinality, 43, 200, 204 See also Reductive logic Epistemology Bohr’s Quadrant, 5, 6 component-dominant vs. interaction-­ dominant, 44, 93, 94, 108 cycle of research, 192 Excellence child prodigies, 14, 15, 19, 20, 41, 44, 58, 59, 62, 91, 103, 147, 154, 157, 203, 221, 228 creative productivity, viii, 1, 2, 16, 17, 44, 57, 61, 64–66, 69, 70, 85, 86, 90, 92, 104, 119, 152, 157, 178, 200, 207, 228, 229

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Y. Dai, Talent Development from the Perspective of Developmental Science, https://doi.org/10.1007/978-3-031-46205-4

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240 Excellence (cont.) expertise, 1, 2, 16–17, 19, 26, 57, 61, 64, 66, 69, 71, 80, 91, 104, 158, 163, 191, 200, 207 long-term differences in, 18, 19 long-term prediction of, 25, 87, 154 polymathy, 11, 18, 92, 97, 111, 230 F Foundations of talent domain-specificity, 12, 14, 60, 61, 70–72, 147, 153, 155, 195, 223, 229 heritability, 82 neural basis, 194 H Histriometric investigations, 20 I Innovation, 6, 17, 18, 30, 49, 50, 69, 173, 174, 185, 222 M Multi-level analysis and integration, 199 N Nature vs. nurture Being vs. doing, 11–30 Neural-physiological foundations of giftedness, 62–63 Nomothetic vs. idiographic approach circumscribed prediction model, 125, 126, 132 immediate phenomenology, 46, 51, 58–59, 68, 104 middle-range theory approach, 127, 128, 197 non-universal development, 20, 28, 129, 148, 228 parametric vs. structural, 147, 227 pattern recognition analysis, 136, 138 person-centered approaches, 96, 105, 111, 112, 116, 203, 213, 214, 217–222, 229, 230 positivist logic, 38 psychometric traditions, 2, 23, 85, 146, 158, 199, 215 universal-unique continuum, 96, 126 variable-centered approaches, 203, 217, 219, 229

Index P Personal development competence development, 105, 109–110, 112, 117, 128, 135, 158, 174–178, 198 evolving individuality, 103, 104, 108, 194, 196, 215, 230, 232 identity development, 111–116, 128, 134, 158, 174, 175, 178, 180–181 interest development, 104, 108–112, 116, 129, 130, 158, 175, 180 intrapersonal changes, 48, 80, 104, 105, 115, 116, 118, 128 self-organization, 28, 45, 203, 216, 223, 228, 229, 231, 232 Positive psychology, vii, 3 Predictive validity, 87, 139, 150, 153, 156 Probabilistic epigenesis gene-environment interaction, 25, 26 proximal processes, 5, 7, 27, 28, 42, 48, 65, 104, 105, 107–109, 112, 113, 197, 203, 213, 221, 229 R Reductionism Cartesian divide, 38 reductive logic, 44, 45, 215, 217 reductive methodologies, 38 Relational developmental systems, 40, 94, 104, 107, 117, 197 S Self-actualization, 4, 231 SMPY longitudinal project, 22 Statistical analysis base rate, 150 decision tree analysis, 135 latent class growth modeling, 136 logistic regression, 135 odds ratio, 150 statistical significance vs. practical significance, 150 survival analysis, 96, 125, 137 T Talent development (TD) cultural niche constructions, 204, 205 cultural provisions/interventions for, 3, 5, 49, 169–174, 176, 179, 182, 194 deliberate practice, 17, 66, 103 developmental responsiveness, 178, 182 grit, 45, 104, 108, 181, 199

Index infrastructure for, 5, 6, 50 learning ecology, 3, 39, 49, 50, 174, 183 legitimate peripheral participation, 50, 175 mentorship, 171, 183, 202 musical talent development, 42, 43, 114 ontological innovations, 6, 50, 174 pedagogy and technical support, 6 psychosocial skills/characteristics, 16, 26 scaffolding, 110 stages and phases of, 2, 26, 28, 148, 154, 170, 171, 182 talent trajectories, 5, 85, 127, 169 Talent domains performance domains, 2, 17, 61, 66, 67, 69, 106, 231 polymathy/polymaths, 11, 18, 92, 97, 111, 230 production domains, 17, 61, 66, 69, 107, 230 taxonomy of talent, 61 Talent identification classical test theory, 147 clinical precision, 153–154, 164 clinical significance, 150, 153 creative potential, 152 developmental prognosis, 149 dynamic assessment, 156, 160 prognostic function, 145, 150, 164 selectivity, 151–154 talent manifestations, 67–72 talent potential, 145, 152, 155, 156, 158 threshold requirements, 13, 151–153, 160

241 Theoretical models bioecological model (Ceci), 27 component models, 11, 23–25, 44, 88, 94, 104, 105, 114 developmental systems models, 11, 26–29 differentiated model of giftedness and talent, DMGT (Gagné), 23, 201, 213 differential models intellectual vs. non-intellectual factors, 23 interindividual differences, 79, 125, 127, 193, 215, 227 emergenic-epigenetic model (Simonton), 25, 27, 201 evolving complexity theory, ECT (Dai), 28, 29, 111, 129–133, 171, 172, 203 megamodel (Subotnik et al), 26 process models, 11, 25–26 psychosocial theory (Tannenbaum), 22 three-ring theory (Renzulli), 25, 160 The Shiffrin paradigm differential learning, 194 rate of learning and asymptotic performance, 80, 156 U Use-inspired research Edison’s Quadrant, 5, 6 Pasteur’s Quadrant, 6, 49