Zeitschrift für Psychologie: Band 196, Heft 2 1988 [Reprint 2021 ed.] 9783112580165, 9783112580158

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ISSN 0044-3409 . Z. Psychol. • Leipzig • 196 (1988) 2 • S. 113-216

ZEITSCHRIFT FÜR

PSYCHOLOGIE mit Zeitschrift für angewandte Psychologie

Schriftleitung Friedhart Klix, Berlin • Joachim Hoffmann, Berlin Redaktion:

Jürgen Mehl, Berlin • Friedrich Kukla, Berlin

Unter Mitwirkung

von

J . E. Azcoaga (Buenos Aires) P . B. Baltes (Berlin/West) N. Bischof (Zürich) A. A. Bodaljow (Moskau) H. Dömer (Bamberg) J . Engelkamp (Saarbrücken) P. Fraisse (Paris) H.-G. Geißler (Leipzig) W. Hacker (Dresden) D. J . Herrmann (New York) A. Kossakowski (Berlin) D. Koväc (Bratislava)

B. F. Lomow (Moskau) D. Magnusson (Stockholm) K . Pawlik (Hamburg) P. Petzold (Jena) H.-D. Rösler (Rostock) E. Roth (Salzburg) H.-D. Schmidt (Berlin) L. S. Zvetkova (Moskau) H. Sydow (Berlin) B. M. Velichkowsky (Moskau) M. Wertheimer (Boulder) G. D'Ydewalle (Leuven)

VEB

JOHANN

AMBROSIUS

BARTH

LEIPZIG

Inhalt Klix, F. (Berlin). On the role of knowledge in sentence comprehension. With 14 fig

113

Küchler, Erika (Berlin). Untersuchungen zur Aktivierung von Wissensstrukturen im menschlichen Gedächtnis. Mit 5 Abb 129 Wandke, H. (Berlin). Online-Hilfen in der Mensch-Rechner-Interaktion: Von ausführlichen Erklärungen zu kurzen Stichworten oder umgekehrt? Mit 11 Abb 147 Hübner, R . (Regensburg). Die kognitive Regelung dynamischer Systeme und der Einfluß analoger versus digitaler Informationsdarbietung. Mit 2 Abb 161 Fritz, Annemarie; Funke, J . (Bonn). Komplexes Problemlösen bei Jugendlichen mit Hirnfunktionsstörungen 171 Reimann, B . (Berlin). Die Entwicklung des Objektbegriffes „Auto" und seiner sprachlichen Benennung im Zusammenspiel von Kommunikation und Kognition. Mit 7 Abb 199 Buchbesprechungen

146, 159, 188, 206

ZEITSCHRIFT

FÜR

PSYCHOLOGIE

Band 196, 1988 mit Zeitschrift fiir angewandte Psychologie Z. Psychol. 196 (1988) 1 1 3 - 1 2 8

Helt 2 Band 102 Y E B J . A. B a r t h , Leipzig

From the Department of Psychology, Humboldt-University Berlin

On the role of knowledge in sentence comprehension /By F. Klix With 14 figures

Introduction Present paradigms of text comprehension are to a great extent the outcome of interactions between linguistics and computer science. Historical reasons for this interaction are linked with the translation problem. After the word-to word or simple utterance substitutions failed (see the ALPAC-rep. (1966)) computer scientists recognized language as a highly structured entity with a lot of interdependeneies between non-connected words. On the other hand some linguistic approaches were strongly influenced by mathematical developments in recursive functions and a u t o m a t a theory. The Chomsky approach was the great pivot and was able to mediate between the recursively oriented description of sentences and new linguistics insights on generative or transformational rules. The step of realizing text-understanding b y computers seemed viable. The A T X parser paradigm in based on this idea (Wilks,1972; Woods, 1976; Simmons, 1976). Language families like L I S P or (partially) P R O L O G are well equipped to manipulate tree structures or transformations within or between symbol-concatenations. But progress was limited, especially in comparing human capabilities with computer procedures. Psychological experiments (Kintsch, 1974; Franks and Bransford, 1972: among others) gave evidence that even simple sentence comprehension includes much more than what was realized in A T X procedures. There the usual procedure was to process the simultaneously represented word-chain from left to right: Having examined first noun, the article and the (possible) adjective loop, the rule w a s : " t h e first noun is the s u b j e c t " (unreliable in G e r m a n ) : then go to the verb-phrase, checking in the file whether it is transitive or intransitive, opening the next slot for pushing in an object or a preposition phrase etc. Lengthy backtracking procedures are necessary if an assignment is ambiguous between VP and X P ( " D I E A L T E N G R U B E N G R A B E X MIT S C H A U F E L X , D I E J U X G E X G R A B E X G R U B E N M I T S P A T E X . " Human parsing seems to go another way. B u t which way, and by which means? Obviously by means of background knowledge. B u t again: What does this mean e x a c t l y : " „ b a c k g r o u n d knowledge"? 1

8

An english e x a m p l e : Watch her stop and stop her watch. Z. Psychologie 196-2

ZEITSCHRIFT

FÜR

PSYCHOLOGIE

Band 196, 1988 mit Zeitschrift fiir angewandte Psychologie Z. Psychol. 196 (1988) 1 1 3 - 1 2 8

Helt 2 Band 102 Y E B J . A. B a r t h , Leipzig

From the Department of Psychology, Humboldt-University Berlin

On the role of knowledge in sentence comprehension /By F. Klix With 14 figures

Introduction Present paradigms of text comprehension are to a great extent the outcome of interactions between linguistics and computer science. Historical reasons for this interaction are linked with the translation problem. After the word-to word or simple utterance substitutions failed (see the ALPAC-rep. (1966)) computer scientists recognized language as a highly structured entity with a lot of interdependeneies between non-connected words. On the other hand some linguistic approaches were strongly influenced by mathematical developments in recursive functions and a u t o m a t a theory. The Chomsky approach was the great pivot and was able to mediate between the recursively oriented description of sentences and new linguistics insights on generative or transformational rules. The step of realizing text-understanding b y computers seemed viable. The A T X parser paradigm in based on this idea (Wilks,1972; Woods, 1976; Simmons, 1976). Language families like L I S P or (partially) P R O L O G are well equipped to manipulate tree structures or transformations within or between symbol-concatenations. But progress was limited, especially in comparing human capabilities with computer procedures. Psychological experiments (Kintsch, 1974; Franks and Bransford, 1972: among others) gave evidence that even simple sentence comprehension includes much more than what was realized in A T X procedures. There the usual procedure was to process the simultaneously represented word-chain from left to right: Having examined first noun, the article and the (possible) adjective loop, the rule w a s : " t h e first noun is the s u b j e c t " (unreliable in G e r m a n ) : then go to the verb-phrase, checking in the file whether it is transitive or intransitive, opening the next slot for pushing in an object or a preposition phrase etc. Lengthy backtracking procedures are necessary if an assignment is ambiguous between VP and X P ( " D I E A L T E N G R U B E N G R A B E X MIT S C H A U F E L X , D I E J U X G E X G R A B E X G R U B E N M I T S P A T E X . " Human parsing seems to go another way. B u t which way, and by which means? Obviously by means of background knowledge. B u t again: What does this mean e x a c t l y : " „ b a c k g r o u n d knowledge"? 1

8

An english e x a m p l e : Watch her stop and stop her watch. Z. Psychologie 196-2

114

Z. Psychol. 196 (1988) 2

On knowledge representation Having recognized the limitations of the pure ATN capabilities, computer linguists saw the need to implement some kind of knowledge base which could assist the comprehension process. Knowledge models developed by Collins and Quillian, 1968; Simmons, 1973; Schank, 1975 (among others) are well-known. The Collins and Quillian approach for instance, suitable for solving some ambiguous word meanings, uses three kinds of relations between words: pointers which indicate sub-superordinated concepts, AND and OR connections and MODIFIERS like adjectives, attached to word markers. And what the Collins and Quillian network has in common with all the others is that the knowledge base is a compilation of words and relations between them. The impacts of these knowledge-like structures on language processing capabilities were outstanding: inferential rules were elaborated and applied in order to identify relations by using t r a n s i t i v i t y ; expensive elaborate backtracking procedures became partially automated and inferential rules became partially separated as "inference machines". So it might be lhat it is more advantageous for computer linguists to neglect the human touch of sentence (or text) understanding since human kinds of parsing woidd be more painstaking and therefore not conducive to simulating that touch. But—so long as this is not y e t proved—the opposite might also be true, at least regarding some aspects. To cover some of these aspects is the main intention behind our approach.

Words, Concepts and Inferences — a psychological approach The following claims and statements are supported by scries of psychological experiments, carried out by E. van der Meer (1984, 1985), M. Preuss (1985), M. Wolf (1984), 0 . Karzek (1985), G. Ricken (1987), R. Beyer (1986). Summarizing reports are given by J . Hoffmann (1984, 1986) and F. Klix (1984)' Besides the ability of human mental activity to produce images of perceivable objects, the specifically human knowledge consists of (1) concepts and concept relations (partly assigned to words or signs), (2) operational rules of how to use and to manipulate this stationary architecture in order to find answers in the face of queries or problems, and (3) rules for expressing the results of such processes in a verbal form, written or inserted in a communicative act. The interaction between a stationary structured knowledge base and suitable operations within it is—on the phenomenological level—called thinking. In this connection we are trying to explain how these two components interact (also) in language comprehension processes. The experiments mentioned gave sufficient evidence that there exist "subsymbolic processes" which allow a person to compare, to suppress or to activate conceptual properties after they have been triggered b y word-stimuli. Comparison procedures, applied to words, allow us to detect similarities between them (like TANNE and WANNE) ((like S L E E P and S H E E P ) ) , applied to conceptual properties they allow us to detect quite different kinds of similarity (e.g. between TANNE and

115

Ivlix, Knowledge in sentence comprehension

E I B E (between S H E E P and G O A T S ) ) . Obviously, there is a significant kind of knowledge behind the words as lexical entries, and it governs a good deal of |]ie understanding of meaning. P a r t (but how much?) of language understanding seems to take place beyond language representation in human memory. The experiments mentioned produced sufficient evidence for distinguishing ihree families of concepts: (1) those which represent classes of objects like O A K , or S H A R K or P I A N O etc. Words which denominate these concepts are attached to a (non-closed) set of properties which allow us to draw the characteristics of the classified set and which are results of (averaged) sensory inputs like colours, sizes, form features etc. Concrete realizations of these properties as memory entries depend in their concrete realization on the age, on special interests, on specific training effects in a given area of knowledge etc. A content-invariant description of such property-determined object concepts is given in figure I. PLANT

* AN ¡M

ANIMAL

* AB SHIM

FLOWER

* * AN Q MM

BIRO

* * AB CMM

SUilMERFLOWER

* * * ANQ p taw

PET

* * A63C DIM

AUTUflNFLOWER

* * * ANQ S MM

POULTRY

* * * ABCO E IM

ROSE

* * * * ANQP 0 fMHH

FOWL

* * * * A8C0E F Ml

COSflcA

* * * * * ANQP R SIM

HEN

* * * * ABCOEF w

ASTER

* * * * * ANQS T MM

ROOSTER

* * * * ABCOEF m a

DAHLIA

* * * * * ANQS U MM

CHICKEN

* * * * ABCOEF n u

u

Fig. 1. Example of a subset of object-concepts. Words are assigned to a (non-closed) property set. Operations at the (subsymbolic) property level allow to detect concept relations: the revealing of properties leads to subconcepts, suppression of the most specifying to superordinate concepts, detection of common AND mutually specific properties allow to identify coordinated concepts with a common superordinate concept, whose difference (or similarity) depends on the relation: common vs. specific properties. 3 for pet indicates a restriction for the subset: the specificity of C shows whether a bird is a pet. These rules produce any kind of hierarchy among property assigned object concepts. Their specificity depends on those properties which are taken as relevant to classification

(2) Concepts which classify events. They are (in general) denominated classes of situations, mapping regular interactions between subjects and things in space and time and sometimes including the speaker as an actor or recipient. E v e n t concepts are defined b y a semantic core, oftened labelled by a verb (like T E A C H or S E L L or E A T or P L A Y ) , b u t not exclusively (cf. T R E A T M E N T , C H E S S or V I C T O R Y ) . Semantic cores are sources of a set of semantic (case) relations like A G E N T , R E C I P I E N T , O B J E C T , I N S T R U M E N T , and F I N A L I T Y . These relations slress properties of possibly linkable concepts: the relevant O B J E C T - p r o p e r t i e s for piano are differently stressed b y event-cores like " C O N C E R T " vs. " M O V E " . E v e n t concepts also include transitions between object properties as induced b y " O P E N " or " B R E A K " . A semantic core with a typical group of relations is shown in figure 2. (3) The third class of concepts embraces event sequences like " M E N U " or " S O C C E R G A M E " , event concatenations like " V A C A T I O N " , " A D V E N T U R E " or " S H O P P I N G " .

116

Z. I'sychol. 196 (1938) 2

MEDICINE

INSTR

DOCTOR

MEO. TREATMENT

PATIENT CURE CLINIC

Fig. 2. A typical event concept, specifying (medical) treatment by a set of semantic relations which bind concept properties on the highest (semanticallv allowed) level.

Conceptual linkages of events are in general mappings of goal-orienled activities which e m b r a c e physical or social conditions, causality, changes in life space and time-perspective. Concepts of this kind in their verbal-surface form often need compound sentences or sentence c o n c a t e n a t i o n s , linked b y words expressing time or condition, like

"while",

" s i n c e " , " a l t h o u g h " , " b e c a u s e " , " a f t e r w a r d s " , " b e f o r e " etc.

TRANS: OBJ l«> UT ! 1NSTK to IIT 2 LOC

MlRCHASKfl

BUY

OBJ _ ARTICLE

IHSTR HONEY

GIVER GIFT RECEIVER

TO PLEASE or TO ANHOY

TRAMS: OBJ to HT 2

Fig. 3. Conceptual concatenation of two events. The result is a more complex but (in this example) a lexically unlabelled event. The rule of this case: if there is the same concept in two different events linked by the same relation there is also a unification of the two events

K l i x , Knowledge in sentence comprehension

117

The second main pari of human conceptual memory is constituted by the operative machin e r y : a set of procedural modules or cognitive operators which allow manipulating of stationarily stored entities like properties and to link concepts by binding core sequences with their respective object-conceptual refinements. Operations concerning conceptual properties are: (J.) suppression (or inhibition) of properties. This allows superordinated concepts to be produced, like the deletion of the most specific properties in figure 1. Unfolding properties allows the inverse relation to be produced: subordinated concepts (in fig. 1 : to a more specific property). Comparison procedures like tracing and comparing the ''common in"-relation with the mutually distinct properties allow us to identify (or to produce) coordinated concepts on different levels (like R O S E und L I L I E ( R O S E and L I L Y ) or U E N N E und H A H N ( H E N and R O O S T E R ) in figure 1.) High degrees of commonality point towards synonyms. And emphasizing specific properties in comparison with their expressions allows the detection (or production) of antonyms, comparatives or new classes of concepts defined by some kind of common properties (e.g. the sel(s) of all combustible objects). Event concepts, b y their respective (case) relations, allow the identification of truth values in given expressions: to DR1.XK demands A N I M A T E as a property for all agents and a subset of fluids for O B J E C T S ; S T O N E or B U I L D I N G would be violations in this context. Semantic cores allow forward-inferences by concatenations via F I N A L I T Y : to B U Y in order to G I V E with a change from G O O D S into G I F T (fig. 3).

ILLHESS /

Fig. 4.

E v e n t c o n c a t e n a t i o n b y C O N D and/or C A U S . There is evidence t h a t during t e x t comprehension

some kind of by-activations of concept properties t a k e s place. This is due to t h e semantic relations of t h e core and plays a p a r t in free recall e x p e r i m e n t s

118

Z. Psychol. 196 (1988) 2

Finally, conceptual event-sequences allow backward-inferences like conditions for a sequence, causality-relations or violations of space and/or time dependencies 2 (fig. 4). Having outlined stationary as well as procedurally "implemented" parts in the human memory we are going to use this means for constructing a first approach model of human sentence (and text) comprehension. It is supported by experiments (Beyer, 1986; van der Meer, 1985) and to some degree by eye-movement, investigations (Groner and Fraisse, 1982). The basic idea is: human language understanding is a process which moves to and fro between parts of the text input and the conceptually structured human knowledge base, and it is guided as a strategy, composed of a meta-level which uses cognitive modules and is driven by a motive: the goal for which reason understanding is necessary. This last statement means that the depth of meaning elaboration during the understanding process depends on the current information requirement in order to make a decision or have a judgement or give an answer. Outline of an approximative model of human text (sentence) comprehension The model to be outlined is only partly supported by data about text processing, and partly constructed by hypotheses about what we know of the organization of conceptual memory. The model is being tested by means of an AT-computer. Human parsing, there is no doubt, differs from what ATN procedures perform: there is no syntactic taxonomy to begin with but direct access from words to concepts (this is provable by priming experiments). The first step (now the general procedure of the model) is to look for the first event-concept, mostly a verb, sometimes an auxiliary. An internal (and latent) activation of the allowable semantic relations follows (a thunderstorm has no F I N but e.g. COND and CAUS). This activation is rendered in the following sequence (Schmieschek, 1987): the (active) A G E N T , the R E C I P I E N T , O B J E C T , I N S T R U M E N T and eventually the F I N A L I T Y (which points to possible goals or motives of the agent). These relations open the respective file and point to the most general properties. They are assigned to the respective superordinate concept as a lexical entry whose property set is included in all subordinated concepts (s. fig. 1). This indicates the set of all semantically acceptable concepts and is also called the selection constraint in producing semantically correct statements around a given core (if the event-core is COOKING then the agent properties demand an adult A G E N T ; the respective restriction for to E A T is human, the object for COOKING is a marked subset of food (including meat or vegetables etc. but excluding e.g. biscuits)). The LOCATION demands here a subset of rooms (excluding bathroom or toilet); the F I N A L I T Y is goal orientation like tasting, satiation or eating as another new event concept including as objects whatever cookable things might be stored in an individual memory. This latent preactivation of concept sets by relations is the Other kinds of cognitive capabilities like the derivation of metaphors or analogical reasoning can be partially explained, from the given point of view (Klix, 1984, 1987). Meta-level rules like deductive or inductive reasoning, the role of complete vs. incomplete information; differences of presuppositions for reasoning due to physical laws or social rules are omitted in this connection. 2

Klix, K n o w l e d g e in sentence comprehension

119

b a c k g r o u n d against which the words of the i n p u t string are a t t a c h e d to t h e i r semantic role in t h e sentence, or w h e t h e r t h e y violate t h e selection constraint. In t h e case of C O O K I N G concepts like Doctor. Teacher, Mother. A u n t . P s y c h i a t r i s t . Murderer are accepted (as adults) but B a b y or Horse or a n y animal or plant etc. are rejected as possible agents. B A B Y is accepted as the recipient, b u t rejected as t h e object while specified subsets of animals or plants are accepted by this relational restriction condition. In s h o r t : t h e unfolded semantic core of an event concept allows us to i d e n t i f y (in principle) t h e possible semantic suitability of words of a given s t a t e m e n t and it defines the range in which a word m a y be sought for an i n t e n d e d r e m a r k . Since all input words are m a p p e d onto t h e constraint grid, it is also possible to recognize restrictions which depend on more t h a n one most general set of properties: a doctor is allowed to T E A C I I IN T H E H O S P I T A L (LOC). he is allowed to T E A C H S A I L I N G (as O B J ) , he is allowed to T E A C H T H E R A P Y I N T H E H O S P I T A L b u t not to t e a c h sailing t h e r e (the core 'sailing', a l t h o u g h semantically compatible with teach, does not include hospital as possible L O C A T I O N , b u t a beach or some subset of stretch of water). At t h e e n d of this first step all t h e event-relevant concepts are identified in their role, defined b y the e v e n t - r e l e v a n t semantic relation. A full propositional description would be derivable. The n e x t step in this (human) model procedure is a refinement of this rough meaning s t r u c t u r e : adjectives are identified as emphasized properlies of object concepts a n d a d v e r b s of e v e n t specification concerning space, t i m e or modality. There are also restrictions concerning possible a n d impossible a t t r i b u t e s . B u t a general decision rule has not y e t been e l a b o r a t e d . This refinement procedure ends t h e first phase of this model procedure. The n e x t steps are optional. Which of t h e m are to be carried out depends on the 'comprehension motiv a t i o n ' of t h e reader. The b a c k g r o u n d of this optional device is t h e simulation of a m e t a level: which s t e p and d e p t h of comprehension is needed in order to realize t h e given recognition r e q u i r e m e n t : to be roughly informed, for answering a question or for translating a sentence. W e indicate here some possible p a t h s . A : If t h e sentence is subdivided b y a c o m m a or " A N D " t h e n a referential analysis s t a r t s (after h a v i n g identified t h e s t r u c t u r e a r o u n d t h e first event concept). The procedure is forked, two p a t h s are m e n t i o n e d h e r e : (1) A possible f o r w a r d inference is mostly guided b y t h e finality-relation: " u m . . . z u " ("in order t o " ) allows us to relate a subject-free sentence to t h e first e v e n t c o n c e p t : " E r schlachtete das Tier, u m das Fleisch zu v e r k a u f e n " ("he slaughtered t h e animal in order to sell t h e m e a t " ) : to slaughter points via F I N to " f o r selling" or " f o r e a t i n g " a n d their subconcepts. (2) A possible b a c k w a r d inference m e d i a t e d b y conjunctions like " b e v o r " ("before"), " d a n a c h " ( " a f t e r t h a t " ) , " w ä h r e n d " ("while"), " o b g l e i c h " ( " a l t h o u g h " ) , " w e i l " ("because"), "so d a ß " ("so t h a t " ) etc. Time dependencies between events are recognizable as well as t h e identification of conditions of effects (the procedure i n t e r a c t s in f u r t h e r steps with t h e referential a t t a c h m e n t of a group of pronouns). B : If t h e i n f o r m a t i o n needed d e m a n d s a t i m e analysis (e.g. for answering W H E N questions), t h e n a subroutine is s t a r t e d , which asks for g r a m m a t i c a l t i m e codes. It allows us to

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find (1) whether the time is past, present or future (by morphological verb-forms and/or " W I R D " ( " I S " ) , " H A T " ( " H A S " ) , " W A R " ( " W A S " ) stem) and (2), if required, which of the linguistically defined pasts or futures are expressed (e.g. the " W A R " ( " W A S " ) or " H A T T " ( " H A D " ) and " G E " as past perfect). Suitable cues are used for other tense relations but it may be that there are non-identifiable corcs. C: A third (not yet complete) procedure tries to assign more distant or accumulated adjectives to the respective noun concept. Anaphora belongs to this area, but the problem is more difficult than is usually appreciated. The idea is to solve it by testing the propertycompatibility between an adjective as attribute property and the noun in question. D : Until now, grammatical rules have been used only so far as they are necessary to arrive at semantic differentiation and non-ambiguity. (Den Yater pflegte die Mutter (the mother looks after the father): the agent is identified by the case identification (nominative vs. accusative).) In " D i e Mutter pflegte die T a n t e " ("the mother looks after the a u n t " — " t h e aunt looks after the mother") this rule fails and another one is applied: If the case markings are the same the first noun refers to the agent (in German). At present a step-by-step elaboration of grammatical descriptions is being outlined: word classification (a multi-classification problem!), a morphological analysis and — finally — the composition of a phrase structure tree. The point is again: which requirement demands which kind of depth in grammatical analysis? We cannot assume that sentence comprehension is primarily mediated by grammatical analysis of sentences. For the same reason we do not believe that the recognition of a circle is mediated by the activation of its formula, not-withstanding that the formula would also be advantageous or even necessary in order to realize specific tasks. After having outlined our general approach concerning different classes of concepts and inferences and after having sketched a model idea on their interaction during sentence processing (which was simplified here) it seems necessary to emphasize that all these statements are more or less convincing. I am in general convinced that it is impossible to detect connections or regularities in human memory by intuition or self-observation (although this may have some heuristic value at the beginning). The crucial point is to derive alternative hypotheses concerning different possibilities and to check them by an appropriate experimental design.

On experimental evidence Figure 5 shows the hypothetical background. Mental activity (of which meaning comprehension is a pert) is stimulated by two sources, i.^. sensory input on the one hand and memory entries on the other. Concerning the internal information source we distinguish between stationary entries and procedural modules. The latter interact with both kinds of d a t a : they allow manipulation of sensory input as well as conceptual units and comparison of both. The functional unit where this happens is called the operative compartment. Its activity makes recognition possible, it enables subjects to produce solutions for given problems, e.g. answering questions or to transform sentences into conceptual meaning configurations.

Klix, Knowledge in sentence comprehension

121

Fig. 5. Operational compartment, assumed as a functional unit in which interaction takes place between sensory input, activated memory units, and the motivational background. I t includes short term storage, provides identification of inputs and allow s both sensory input and conceptual (i.e. memory) units to be manipulated and the two to be compared

Concerning the experiments we have performed I have chosen the least complex and the most transparent findings. 1. Concerning our alternatives: explicitly stored vs. inferentially derived relations between concepts: it is intuitively undecidable whether the relation: sub-superordinated concepts (or vice versa) is explicitly stored in memory or due to property comparisons; either is possible. I would like to emphasize one thing: it cannot be decided b y intuition whether semantic relations in event concepts are explicitly stored or not and whether they trigger eventrelated concept properties or not. It is also undecidable whether semantic relations exist at the word level (i.e. among lexical entries) or whether word connections need the conceptual background in order to be detected as a meaningful compound. T h e alternative is outlined in figure 6. E x p e r i m e n t 1 is carried out as follows: S ' s are offered a series of word pairs, step b y step on a computer screen, and in a randomized

( c j )

^ C J

Fig. 6. Alternative of concept relation detection: activation vs. comparison

122

Z. Psychol. 196 (1988) 2

order. They have to decide whether a given relation [e.g. sub-super-concept) holds between a given pair or not. The dislractors (i.e. where the subjects have to answer NO) are varied in their degree of similarity with the target words. This is done b y the amount of properties they have in common (sub-super vs. coordinated concepts). If there exists an explicitly stored relation then the detection time should not depend on the degree of similatlity with the distraclors and vice versa: if a comparative procedure takes place with regard to common and specific properties, then there must be a dependence. As figure 7 shows after Preuss: there is a significant dependence.

t/ms

1200

SUB/SUP

1000-

sus/sup ( i / :

-1—

eoo

n.

CO(2)

coin

(1/2

Fig. 7. Delay in relation detection depends on the similarities between target ( S U B / S U P and distraetor (CO)

On the oilier h a n d : if event-related concepts include explicitly stored semantic relations which link concepts then there should not be such a dependence on the distraetor items. Figure 8 after Kiichler confirms this prediction. 2. Second experiment : concerning activation time between memory units. Supposing scheme 6 is right then it is predictable that the activation time for detecting meaningful relations between concepts must be shorter if there is an immediate or autonomous activaRc I ais I on — Recoon :