challenging assumptions about concepts

Cognitive Development, 8, 169-180 (1993)

COMMENTARY

Challenging Assumptions About Concepts

Lawrence W. Barsalou University of Chicago

A science progresses by challenging its assumptions. As assumptions come and go, they represent current understandings, simplify analysis, and sometimes exist simply because believing otherwise would be inconceivable or present intractable problems. Within existing assumptions, theories develop and experiments are performed. If these accomplishments satiate the needs for knowledge, there is no reason to press further. Yet, if outstanding issues remain, as they usually do, current assumptions may block their resolution, and the search for

new assumptions often begins. The study of concepts across the cognitive sciences, from philosophy and linguistics to psychology and computer science, has rested on some fairly well-accepted assumptions for some time. Clearly, much progress has been made in this context, as the explosion of successful research on concepts over the last 20 years illustrates. Nevertheless, increasing numbers of researchers are challenging these assumptions, as do Jones and Smith (1993) in their provocative, insightful, and compelling lead article in this issue of Cogni- tive Development. In my commentary, I similarly question these assumptions, posing four questions that those of us who study concepts might ask ourselves from time to time: (a) Do coherent conceptual cores exist in long-term memory? (b) Do abstract propositions constitute conceptual cores? (c) Do concepts in long- term memory control behavior? (d) Is the primary purpose of developing and using concepts to taxonomize the environment?

The answers to these questions may in fact be that there are coherent conceptual cores in long-term memory, built from abstract propositions, that control behavior, as people taxonomize the environment. Yet for such answers to be convincing, researchers must transform these assumptions into hypotheses and marshall explicit evidence for them, rather than, as at present, embracing them implicitly, simply because of their familiarity, intuitive appeal, and convenience. As we shall see, these assumptions face strong challenges, both empirically and theoretically, and there is good reason to believe that an alternative state of affairs exists.

Work on this article was supported by funding from the Army Research Institute, MDA 903-90- K-0112.

Correspondence and requests for reprints should be sent to Lawrence W. Barsalou, Department of Psychology, 5848 S. University Ave., University of Chicago, Chicago, IL 60637.

Manuscript received September 30, 1992; revision accepted January 13, 1993 169

170 Lawrence W. Barsalou

ARE THERE COHERENT CONCEPTUAL CORES IN LONG-TERM MEMORY?

To place this first issue in perspective, consider the two extreme forms that the representation of a concept in long-term memory could take: At the incoherent extreme lie exemplar representations of categories. At the coherent extreme lie consistent and complete logical systems of categories. Exemplar representations of categories are potentially incoherent, because such systems, in their standard form, make no attempt to integrate exemplars or to develop generalizations across them. Instead, the representation of a concept is fragmented into independent exemplar memories acquired during different processing episodes with the category, with no generalizations across exemplars providing integration. As a result, these representations have no conceptual cores. Inconsistencies can occur, if different exemplars contain contradictory information about the category. In- completeness can occur, if existing exemplars do not provide an error-free algo- rithm for classifying all new exemplars.

In contrast, consistent and complete logical systems of categories do not exhibit inconsistencies, because deductive rules ensure that no piece of information in such a system contradicts any other piece of information. Nor do these systems exhibit incompleteness, because they provide error-free algorithms for classifying all new exemplars (e.g., necessary and sufficient conditions for category membership). Maintaining a consistent and complete system of category knowledge is effortfui, complicated, and sometimes intractable, as work on truth maintenance systems and the frame problem in artificial intelligence illustrates (Brown, 1987; McCarthy & Hayes, 1969).

Between these two extremes lie many other possibilities, and probably the true state of affairs. One possibility is the core-plus-identification view, which can be construed as a concatenation of the two extremes just reviewed (e.g., E.E. Smith & Medin, 1981). On the one hand, cores contain consistent and complete criteria for category membership that are effortful to apply. On the other hand, identification procedures contain heuristics for recognizing category members that are implemented easily, but that may be inconsistent and incomplete. Alter- natively, there may be no sharp distinction between core and peripheral properties, with properties simply varying continuously from highly diagnostic or important to weakly diagnostic or unimportant (e.g., Hampton, 1979, 1987; McClelland & Rumelhart, 1985). A weak form of coherence might arise in these theories when central properties are intercorrelated with one another across exemplars, thereby becoming active across contexts as a set, as more peripheral properties come and go.

Thus far, there appears to be no compelling evidence for or against any of these views. Each position has its adherents, yet direct evidence is sparse. A number of critical issues must be addressed empirically and theoretically before we can answer this question more definitively. First, we must determine whether

Challenging Assumptions About Concepts 171

natural concepts really have conceptual cores, or whether the appearance of cores arises from other sources of knowledge. As Jones and Smith suggest, people may simply have a metatheory about categories in general, which contains the belief that any category should have a core. In the induction task, people may use this metatheory about categories to infer that the category under consideration has a core, even though they have no knowledge about its content. This inference could in turn cause a category name to control induction, because people believe that two entities having the same name also share the same core and should therefore be sorted together. If this metatheoretical belief about the general existence of cores controls induction, it does not follow that people know conceptual cores for specific categories.

Alternatively, a metatheory about linguistic expertise could control induction (Putnam, 1973, 1975). Perhaps subjects in induction experiments perceive the experimenter as a linguistic expert and believe that the experimenter's use of category names reflects his or her expertise. When the experimenter refers to a perceptually dissimilar exemplar by the name of the critical category, subjects may infer from their metatheory about linguistic expertise that this exemplar is the one most likely to possess the induced property.

Still another possibility is that people know conceptual cores only for very general categories, such as ontological kinds (Keil, 1979). For example, people might know cores for animal, artifact, location, and so forth. In induction tasks involving more specific concepts, such as skunk and chair, people may infer properties from their more general superordinates via inheritance. Because these core properties reside outside of the specific concepts under consideration, claiming that these concepts have cores is inaccurate. Even though concepts may inherit core information, only a few concepts actually have explicitly represented cores, and most do not.

As these examples illustrate, people may not know conceptual cores for specific categories, yet they may act as if they do because of other knowledge they possess. Before we conclude that specific concepts have cores, we need direct evidence for such cores and evidence against the possibility that they arise from other sources of knowledge.

If we discover that conceptual cores exist in specific concepts, a second set of issues arises concerning their acquisition and maintenance. How do people acquire cores? Discovering definitional properties for a concept can require a tremendous amount of computational effort, perhaps more than people are capable of performing (Barsalou & Hale, 1992). Furthermore, how do people maintain cores? Avoiding inconsistency in the face of a changing world raises tremendous computational problems; and it would perhaps be surprising, given everything we know about the limitations on human rationality, to find that people can avoid inconsistency all that well. Maintaining consistency could require examining everything one knows about a category on encountering each new exemplar, and it would be remarkable if people were this thorough in


maintaining their knowledge. As further cause for skepticism, we have observed specific individuals in our laboratory producing contradictory information when defining the same concept on two different occasions (Barsalou, Sewell, & Spindler, 1992).

It may be that people try to establish some generality and consistency in their concepts of categories, but only succeed partially. For example, people may establish coherent generalizations across subsets of exemplars but not across all exemplars (Medin & Ross, 1989), or they may establish coherent generalizations that apply only in specific contexts. If one assumes that just a small subset of knowledge is ever activated on a given occasion for a category, then coherence may only be established within these activated subsets, or between several subsets processed sequentially. As a result, there may be many "patches" of local coherency within the greater knowledge of a category, with no overarching generalizations establishing consistency across all of it. Perhaps the only place that such broad generalizations occur successfully are in formal approaches to knowledge, as in science, although inconsistencies often occur there as well.

Finally, a third set of issues concerns the use of cores. Are cores used every time a concept is processed, or just sometimes? Perhaps cores are used only when the costs for making a categorization error are higher than the costs of applying the core. When the costs of a categorization error are less than the costs of applying the core, people may often use more heuristic methods that are easier to apply. For example, while wandering across a university campus, one categor- izes students and faculty based on appearance prototypes, simply because the cost of checking university identification cards is higher than the cost of making an error.

If most defining information associated with a category is only used occasion- ally, why should we distinguish it with the term "core'?" Why are the expert and least defeasible criteria for determining membership thought to constitute the most important information that the average person knows about a concept? Why isn't the concept's function more central (Nelson, 1974)'? Because functions are probably of significantly greater importance than definitions in daily life, why don't they constitute conceptual cores (e.g., the function of water versus its molecular structure; cf. Malt, 1992)'?

DO ABSTRACT PROPOSITIONS CONSTITUTE CONCEPTUAL CORES?

As Jones and Smith note, theorists often assume that abstract propositions, rather than perceptual memories, constitute the cores of concepts. The primary founda- tion of the propositional view in cognitive science stems from the discovery that people forget surface structure in language and pictures quickly, retaining only their gist (e.g., Mandler & Ritchey, 1977; Sachs, 1974). To represent gist, theorists turned to propositional representations, because multiple surface repre-

Challenging Assumptions Abou! Concepts 173

sentations (e.g., the active and passive forms of a sentence) can reflect the same underlying propositions (e.g., Anderson & Bower, 1973; Kintsch, 1974). Anoth- er reason for adopting propositions is to represent abstract concepts. Because propositions are abstract amodal representations that bear no perceptual resemblance to their referents, their predicate-calculus-like structures can represent the content of any concept, not just perceptual ones.

Nevertheless, the propositional view of concepts faces serious problems (Bar- salou, 1993). The first is that no one has ever provided direct evidence that anything like predicate-calculus expressions lie at the heart of cognitive representations. Instead, we have only adopted these representations because they provide a convenient formal language for expressing what theorists believe about the content of concepts. A second problem is that we have no accounts of how propositional representations arise in the cognitive system, either innately or through experience. We haven't the faintest idea of how biological mechanisms could produce abstract propositions, nor of how information processing mechanisms might transduce propositions from perceptual experience.

A related difficulty is symbol grounding, as illustrated by Searle's (1980) Chinese room problem (also see Harnad, 1987, 1990; Huttenlocher, 1976; Hut- tenlocher & Higgins, 1978). Because propositional representations bear no perceptual similarity to their referents, the relations between these representations and their referents are arbitrary. If no account is given of how propositional representations are related to their referents, the result is a system that has no meaning or true understanding of its world.

Because we have no direct evidence for propositional representations, because we can not tell a coherent story about their origins in the cognitive system, and because we have no account of how propositions relate to the environment, we should be at least somewhat skeptical of them, and we might even consider looking for alternatives. Elsewhere, I have outlined an alternative view in which all conceptual representations arise from perception (Barsalou, 1993). This theory makes several departures from what might typically be viewed as the perceptual approach to knowledge. First, the perceptual representations that underlie knowledge are not holistic exemplars or analog "snapshots" of perception. In- stead, they are small components of perceptions extracted analytically from larger experiences via selective attention (e.g., the shape, texture, or color of an object). Second, such components are extracted not only from perceptions of the environment, but also from introspection of subjectively-experienced mental events. As a resialt, perceptual components develop for cognitive operations (e.g., retrieval, comparison), affective states (e.g., anger, anxiety), and for as- pects of anything else that can be conceived of mentally but not necessarily experienced perceptually. Mandler (1992) proposes a similar view.

As components are extracted from perception and introspection, they function as perceptual symbols, capable of standing for their referents. Note that there is no symbol grounding problem here because the analog nature of these symbols


guides the establishment of their referents via perceptual similarity. Importantly. these perceptual symbols constitute a compositional symbol system. As cognitive linguists have noted for some time, symbols for the components of perception can be combined productively to form complex representations in very much the same way that more traditional propositional representations combine (e.g., Jackendoff, 1987; Langacker, 1987; Talmy, ! 988). For example, perceptual symbols for chair and table can instantiate regions in a perceptual symbol for above to form a complex representation for the chair" above the table. Because perceptual symbols can be bound to regions of other perceptual symbols, argumentation and recursion--two fundamental properties of frames and schemata--emerge naturally (Barsalou, 1993; also see Barsalou, 1992a; Barsalou & Hale, 1992).

Perceptual symbols provide natural accounts of gist and abstract concepts. Once a complex configuration of perceptual symbols has been constructed, a wide variety of linguistic utterances provide paraphrases of it (e.g., "the chair above the table" and "the table below the chair" describe the same perceptual configuration). For abstract concepts, the selectivc extraction of components. coupled with the extraction of components from introspection (especially cognitive operations), provides a rich expressive language for representing abstract notions such as truth, the, and so forth (Barsalou, 1993). In general, it always seems possible to find a configuration of components in perception and/or introspection that an abstract term picks out as its meaning, such that abstract, amodal, and arbitrary propositions are unnecessary. In addition, much expressive power arises from analogy to concrete perceptual domains, as illustrated by Talmy's (1988) grounding of cause and other abstract contingency concepts in the haptic experience of force dynamics (also see Lakoff & Johnson, 1980).

In summary, a compositional system of perceptual symbols offers natural solutions to the problems of traditional propositional systems. From our understanding of perception, we can build coherent structural accounts of perceptual symbols, how they arise in the cognitive system, and how they relate to the environment. In addition, these perceptually-based systems maintain the advan- tages of traditional propositional systems, because they are compositional, and can represent gist and abstract concepts.

If this account is true, it no longer makes sense to debate the relative merits of perceptual and propositional representations, given that all conceptual content is perceptual and no propositional content exists. Instead, the argument reverts to one that has been around a long time: Whether the core content of a concept is the appearance of its exemplars, or whether it is the larger event contexts in which these exemplars typically reside, such as those that indicate function (Nelson, 1974, 1986) and origin (Gelman & Markman, 1986; Keil, 1989). For example, the core content of dog could be perceptual symbols that represent a dog's appearance, it could be perceptual symbols that represent a dog's behavior and function as a pet, or it could be perceptual symbols that represent's a dog's origins, either reproductive or genetic. Note that such a formulation predicts


something along the lines of a characteristic-to-defining shift (Keil & Batterman, 1984), assuming that appearance constitutes characteristic information, whereas function and origin constitute more defining information. Because appearance is the most immediate and obvious perceptual information available for a category, it should be acquired first, with attention to the surrounding contexts that inform function and origins being a more subtle and complicated discrimination.

DO CONCEPTS IN LONG-TERM MEMORY CONTROL BEHAVIOR?

On the traditional view of concepts, propositional cores stored in memory control the processing of categories. However, increasing evidence suggests that the conceptual structures controlling behavior are temporary concepts constructed dynamically in working memory. Jones and Smith review compelling evidence for this view, and further evidence can be found elsewhere (Barsalou, 1987, 1989; Barsalou et al., 1992; Cech, Shoben, & Love, 1990; Glucksberg & Key- sar, 1990; Kahneman & Miller, 1986; Medin, Goldstone, & Gentner, 1993).

However, one could still argue that long-term memory contains core concepts which are overridden in specific contexts by dynamically constructed concepts in working memory. Although these dynamic concepts control behavior, people nevertheless possess core concepts in long-term memory. Yet, if conceptual cores never affect behavior, why are they important'? Why should we honor them with the term core? Why should we reserve the term concept for them, rather than for the temporary representations that actually control behavior? Because temporary representations do all of the work, they should partake in at least some of the glory. For this reason, I have referred to the temporary representations of categories in working memory as "concepts," given that concepts are supposed to be structures that control behavior, and these temporary representations appear to do just that (Barsalou, 1987, 1989, 1993).

One might still argue that these coherent concepts in long-term memory really do control behavior when it counts, such as when the costs for making a categorization error are high. However, I suspect that even under these conditions we will not see stability. We once asked subjects to define concepts carefully and observed considerable variability in the properties that a subject generated on two occasions (Barsalou et al., 1992). One could argue that producing explicit definitions is not the same as performing actual categorizations, yet McCloskey and Glucksberg (1978) did just that and found substantial variability in how a given individual classified the same exemplar on different occasions. If conceptual cores do not surface during classification to produce stability, then it is hard to imagine in what other task they might. If people possess and use such cores, why would they contradict themselves by admitting an entity into a category on one occasion and rejecting it on another? Such behavior is hardly the sort that inspires faith in conceptual cores. Until we have evidence that stable conceptual cores in


long-term memory control behavior in some task, we have little reason to believe in their existence.

IS THE PRIMARY PURPOSE O F DEVELOPING AND USING CONCEPTS TO TAXONOMIZE THE ENVIRONMENT?

Because taxonomies have a venerable history in science, it is not surprising that cognitive scientists typically model the human categorizer as an intuitive taxono- mist. On this view, people, especially during their early development, try to determine the classes of entities that exist in the world and to discover their core natures. As children become adults, they adhere to their taxonomic principles, developing deeper theories of the essences that bind categories together. Because categorization researchers tend to view people as intuitive taxonomists, it is not surprising that experiments desi~maed to study categorization processes typically reflect this assumption. Experiments often assume that people are attempting to determine the types of things in a particular domain, even for artificial stimuli, and that their ultimate goal is to form a taxonomy that captures the essences of the kinds present.

It would indeed be surprising if people did not pursue the taxonomic enterprise to some extent. Nevertheless, I suspect that this is a relatively minor aspect of the categorization enterprise as a whole. One particularly critical departure from taxonomization is the use of categories in language (Murphy, 1991). Rarely does the use of a categorical term during communication serve to specify the taxonomic essence of the category. Instead, the use of the term is, first, to pick out a referent, and second, perhaps to convey some description of the referent relevant to the current context. For example, if I say to a companion in a museum, "Look at the lilies," I am not referring to the taxonomic essence of the naturally occurring lily. Instead, I am drawing the listener's attention to a picture of some lilies. In any particular context, a categorical term can pick out virtually any kind of referent that bears some resemblance to anything known about the category. Such usage can be quite creative and tenuous, as when a waitress says, "The ham sandwich at the comer table wants another root beer," where the referent of "ham sandwich" bears no relation to the taxonomic essence of ham sandwich (Nunberg, 1979). In general, any piece of information stored with a category in memory can assist in establishing its referent (Barsalou, 1992b).

Moreover, salient information associated with a category term may further serve to describe a referent. In metaphor, properties of the vehicle often are projected onto the topic, as in "My job is a jail" (Glucksberg & Keysar, 1990) or "Billboards are warts on the environment" (Ortony, 1979). By focusing on the taxonomic essences of categories, we miss these other important uses of categorical information, namely, their roles in establishing reference and describing referents. If it is more important for categorical knowledge to serve these functions than to taxonomize the environment, one might imagine a very different sort of categorization system.


Elsewhere, I have argued that another central function of"taxonomic" categories is to build and maintain world models (Barsalou, 1991). A world model is not a taxonomic system of the kinds of things in the world, but instead contains a person's beliefs about locations in the world and the objects and activities that reside in them. At the core of a world model is a hierarchical system of spatial frames that represents nested locations, such as continents, countries, cities, houses, rooms, and so forth (Minsky, 1977). Within this spatial system, reside representations of particular objects and activities believed to exist currently, such as cars, pets, furniture, work activities, play activities, and so forth.

There are good reasons for believing that people use basic-level categories to represent objects and events in their world models (Barsalou, 1991). One is that basic categories capture important structural properties of objects and events, thereby supporting an infinite number of functional inferences that could arise later when attempting to achieve unanticipated goals. For example, representing an entity in a world model as a chair enables computing an infinite number of functions later, such as something that could support a bucket o f water, something that could be stood on to change a light bulb, and so forth. Because basic categories capture likely configurations of physical parts (Biederman, 1987; -Tversky & Hemenway, 1985), using them to represent objects in a world model enables a tremendous amount of functional inference, much of which may be unanticipated at the time of storing the object in the world model. If one of the primary functions of basic categories is to build world models that support functional inference, then people may be much more concerned with extracting structural information about these categories than they are about establishing their taxonomic essences.

People's categorizations of entities in a world model do not end with the basic level. Although these categorizations may be primary in the sense that they constitute the initial categorization of entities in a context-independent manner, they may be followed by secondary categorizations that are highly context- dependent (Barsalou, 1991). After categorizing an entity initially as a chair, people may subsequently categorize it as firewood, something to support a bucket o f water, or something that could be stood on to change a light bulb, depend- ing on their current goal. None of these ad hoc categories captures the taxonomic essence of their referents or their physical structure. Instead, the primary function of these categories is to capture information that bears on goal achievement, t

As these examples illustrate, categories play a wide variety of roles in the

lAd hoc and goal-derived categories are not synonymous with the dynamic construction of temporary concepts in working memory. Ad hoc and goal-de.rived categories represent one specific type of category, defined, first, as collections of entities that instantiate a frame attribute, and second, as secondary categorizations of entities (Barsalou, 1991). The dynamic construction of tempera.,3, concepts in working memory is an orthogonal phenomenon, because it occurs for a~y type of category, including basic-level categories, ad hoc categories, and so forth. Just as the repre.scotatioas of basic-level categories may exhibit instability across contexts, so may the representations of ad hoc categories, and any other type of category


cognitive system, many of which have little to do with taxonomies of the environment. These roles suggest that people's primary orientation toward categories may be to develop instruments that serve more immediate tasks. If so, focusing all of our scientific muster on people's ability to taxonomize may only provide a weak attack on understanding human categorization.

CONCLUSION

In challenging the basic assumptions that underlie the study of concepts, some fairly challenging assumptions have been suggested as their replacements. Rath- er than containing consistent and complete concepts, long-term memory contains tremendous amounts of diverse category knowledge which may only be orga- nized locally. Rather than containing abstract propositions at their core, concepts are inherently perceptual, with the important differences revolving around ap- pearances versus event contexts. Rather than concepts in long-term memory controlling performance, temporary concepts constructed dynamically in working memory are responsible. Rather than serving primarily to taxonomize the environment, categories serve more immediate cognitive purposes, such as effec- tive communication, building world models to support unanticipated goals, and cross-categorizing entities to achieve goals optimally.

Because these alternative assumptions are sufficiently controversial, they, too, should not be adopted as implicit assumptions but should be cast as hypotheses. Testing these hypotheses empirically and capturing them theoretically in computational models will be challenging pursuits. Yet doing so is likely to increase our understanding of concepts, eventually leading to the abandonment of these assumptions for still better ones.

REFERENCES

Anderson, J.R., & Bower, G.H. (1973). Human associative memor)" Washington, DC: Winston. Barsalou. L.W. (1987). The instability of graded structure in concepts. In U. Neisser (Ed.), Concepts

and conceptual development." Ecological arid intellectual factors in categorization (pp. 101- 140). New York: Cambridge University Press.

Barsalou, L.W. (1989). lntra-coneept similarity and its implications for inter-concept similarity. In S. Vosniadou & A. Ortony (Eds.), Similarir., and analogical reasoning (pp. 76-121 ). New York: Cambridge University Press.

Barsalou, L.W. (1991). Deriving categories to achieve goals. In GH. Bower (Ed.), The psychology of learning and motivation: Advances #1 research and theory (Vol. 27, 1-64). New York: Academic.

Barsalou, L.W. (1992a). Frames, concepts, and conceptual fields. In E. Kittay & A. Lehrer (Eds.), Frames, fields, and contrasts: New essays in lexical and semantic organization (pp. 21-74). Hillsdale, NJ: Erlbaum.

Barsalou, L.W. (1992b). Cognitive psychology: An overview for cognitive scientists. Hillsdale, N J: Erlbaum.

Barsalou, L.W. (1993). Flexibility. structure, and linguistic vagary in concepts: Manifestations of a


compositional system of perceptual symbols. In A.C. Collins, S.E. Gathercore, M.A. Con- way, & P.E.M. Morris (Eds.), Theories of memories (pp. 29-101). London: Erlbaum.

Barsalou, L.W., & Hale. C.R. (1992). Components of conceptual representation: From feature lists to recursive frames. In I. Van Mechelen, J. Hampton, R. Michalski, & P. Theuns (Eds.), Categories and concepts: Theoretical views and indt~ctive data analysis (pp. 97-144). Lon- don: Academic.

Barsalou, L.W., Sewell. D.R., & Spindler, J.L. (1992). Stability attdflexibility hi human concepts. In preparation.

Biederman, I. (1987). Recognition by components: A theory of human image understanding. Psycho- logical Review 94. 115- 147.

Brown, F.M. (Ed.). (1987). The frame problem in artificial intelligence. Los Altos, CA: Morgan Kaufmann.

Cech, C.G., Shoben, E.J., & Love, M. (1990). Multiple congruity effects in judgments of magni- tude. Journal of E.t'perimental Psychology: Learning, Memoo; and Cognition, 16, 1142- 1152.

Gel man, S.A.. & Markman, E.M. (1986). Categories and induction in young children. Cognition, 23, 183-209.

Glucksberg, S.. & Keysar, B. (1990). Understanding metaphorical comparisons: Beyond similarity. Psychological Reviews; 97, 3-18.

Hampton, J.A. (1979). Polymorphous concepts in semantic memory. Journal of Verbal Learning and Verbal Behavior, 18, 441-461.

Hampton, J.A. (1987). Inheritance of attributes in natural concept conjunctions. Memory & Cogni- tion, 15, 55-71.

Hamad, S. (1987). Category induction and representation. In S. Harnad (Ed.), Categoricalpercep- tion: The groundwork of cognition (pp. 535-565). New York: Cambridge University Press.

Harnad, S. (1990). The symbol grounding problem. Physica D, 42, 335-346. Huttenlocher, J. (1976). Language and intelligence. In L.B. Resnick (Ed.), The nature of intelligence

(pp. 261-281). Hillsdale, NJ: Erlbaum. Huttenlocher. J.. & Higgins, E.T. (1978). Issues in the study of symbolic development. In W.

Andrew Collins (Ed.). Minnesota Symposia on child psychology (Vol. II, pp. 98-140). Hillsdale, N J: Erlbaum.

Jackendoff, R. (1987). On beyond zebra: The relation of linguistic and visual information. Cognition, 26. 89-114.

Jones, S.S., & Smith, L.B. (1993). The place of perception in children's concepts. Cognitive Development, 8, 113-139.

Kahneman, D., & Miller, D.T. (1986). Norm theory: Comparing reality to its alternatives. Psycho- logical Reviews, 93, 136-153.

Keil, F.C. (1979). Semantic and conceptual development: An ontological perspective. Cambridge, MA: Harvard University Press.

Keil, F.C.. & Batterman, N. (1984). A characteristic to defining shift in the development of word meaning. Journal of Verbal Learning and Verbal Behavior. 23. 221-236.

Keil, F.C. (1989). Concepts, kinds, and cognitive development. Cambridge, MA: MIT Press. Kintsch, W. (1974). The representation of meaning in memor3: Hillsdale, NJ: Erlbaum. Lakoff, G., & Johnson, M. (1980). Metaphors we live b3: Chicago: University of Chicago Press. Langacker, R.W. (1987). Foundations of cognitive grammar: Vol. 1. Theoretical prerequisites.

Stanford, CA: Stanford University Press. Malt, B.C. (1992). Water is not H,O. Manuscript submitted for publication. Mandler, J.M. (1992). How to build a baby: I1. Conceptual primitives. Journal of Psychological

Review, 99, 587-604. Mandler, J.M., & Ritchey, G.H. (1977). Long-term memory for pictures. Journal of Experimental

Psychology; Human Learning and Memo~., 3, 386-396. McCarthy, J., & Hayes, P. (1969). Some philosophical problems from the standpoint of artificial


intelligence. In B. Meltzer & D. Michie (Eds.), Machine learniug 4. Edinburgh: Edinburgh University Press.

McClelland, .I.L., & Rumelha~, D.E. (1985). Distributed memory and the representation of general and specific information. Journal of Experimental Psychology: General, 114, 159-188.

McCloskey, M., & Glucksbcrg, S. (1978). Natural categories: Well-defined or fuzzy set? Memory & Cognition, 6. 462-472.

Merlin, D.L., Goldstone, R.L., & Gentner, D. (1993). Respects for similarity. Psychological Review 100, 254-278.

Medin, D.L., & Ross, B.H. (1989). The specific character of abstract thought: Categorization, problem solving, and induction. In R.J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 5, pp. 189-223). Hillsdale, NJ: Erlbaum.

Minsky, M.L. (1977). A framework for representing knowledge. In P.H. Winston (Ed.), The psychology of computer vision (pp. 211-277). New York: McGraw-Hill.

Murphy, G.M. (1991). Meaning and concepts. In P. Schwanenflugel (Ed.), The psychology of word meaning (pp. 11-35). Hillsdale, N.h Erlbaum.

Murphy, G.M. (in press), A rational theory of concepts. In G.V. Nakamura, R.M. Taraban, & D.L. Medin, (Eds.), The Psychology of Learning and Motivation: Categorization by humans and machines (Vol. 29). San Diego, CA: Academic.

Nunberg, G. (1979). The non-uniqueness of semantic solutions: pulyscmy. Linguiatics and Philoso- phy, 3, 143-184.

Nelson, K. 0974). Concept, word, and sentence: Interrelations in acquisition and development. Psychological Review, 81,267-285.

Nelson, K. (1986). Event knowtedge : Structure and function in development. Hillsdalc, NJ: Eribaum. Ortnny, A. (1979). Beyond literal similarity. Psychological Review. 86, 161-180. Putnam, H. (1973). Meaning and reference. Journal of Philosaphy, 70, 699-711. Putnam, H. 0975). The meaning of "meaning." In H. Putnam (Ed.), Mind, language, and reality:

Philosophical papers (Vol. 2, pp. 215-271). New York: Cambridge University Press. Sachs, J.D.S. (1974). Memory in reading and listening to discourse. Memory & Cognition, 2, 95-

100. Searle, I.R. (1980). Minds, brains, and programs. Behavioural and Brain Sciences, 3, 417-424. Smith, E.E~, & Medin, D.L. (1981). Categories and concepts. Cambridge, MA: Harvard University

Press. Talmy, L. (1988). Force dynamics in language and cognition. Cognitive Science, 12, 49-100. Tversky, B., & Hemenway, K. (1985). Objects, parts, and categories. Journal of Experimental

Psychology: General, 113, 169-193.

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