The Representation and Acquisition of Concepts
Psychology 747, Section 4097 - Fall 2003
Room 115 Psychology
Meeting Time: Tuesday-Thursday, 2:30-3:45
Instructor: Professor Robert Goldstone
Office: 338 Psychology
Office hours: Mon, Wed 2:00-3:30
Web site: http://cognitrn.psych.indiana.edu/rgoldsto/courses/concepts/index.htm
Without concepts, thought itself would be impossible. Concepts serve critical roles in organizing our thoughts, perception communication, prediction, and inference. This seminar will explore issues in concept learning and representation. Topics in philosophy, computer science, and developmental psychology will be covered, but the preponderance of the material will be from cognitive psychology. Among other topics, we will discuss prototype, exemplar, and “theory” theories of conceptual representation, the interconnectedness and modularity of concepts, computational models of concept acquisition, and how concepts are changed and created. For a full set of topics covered, consult the references below.
The three principle obligations of seminar participants will be to lead one of the fourteen class discussions, read the weekly assignment, and to actively participate in all class discussions. To facilitate the last two obligations, participants are required to either prepare a one-page written reaction to the weekly readings or to respond to the reaction of another student. Given the occasionally overwhelming pressures on students, participants are exempted from preparing reaction pages for two seminars of their choice. Thus, you must prepare a reaction or reaction-reaction for 11 of the weeks. These should be evenly divided into 6 reactions, and 5 reaction-reactions. Reactions will be coarsely graded (unacceptable, acceptable, and outstanding) and will receive brief comments by me.
Leading a Seminar
The purpose of the seminar leader is two-fold - to review the fundamental points of the readings, and to generate and direct active discussion. You should prepare about 25 minutes of instructional monologue. Overhead transparencies, powerpoint slides, and handouts are encouraged. You may assume that everybody has read the material, but you may want to explain aspects of the paper that other students could have difficulty understanding. Do not attempt to cover all of the material in detail. Rather, select a handful of points that seem to be of fundamental importance. Consider time to be a precious resource; do not waste it on digressions. Two ingredients of a successfully run seminar are that the leader focuses his or her comments on critical themes in the material, and opens up discussion so that the seminar participants are actively involved.
Late reaction pages will not be accepted (the point of the reaction page is to have participants think about their reaction before the seminar). You will submit your reaction pages using the web-based Annotate system developed by Indiana University's cognitive science program. This system is accessed at:
Annotate has been designed so that students can read each other's reactions, add their comments to the reaction, comment on other students' comments, etc. I will also make comments that can be read by all students, and assign grades that can be read by only the receiving student. Reaction pages will be coarsely graded (check minus = unacceptable, check = acceptable, and check plus = outstanding). The most common grade is "check," and do not be surprised if most of your reactions are not rated as "outstanding." I reserve this grade for truly noteworthy and insightful contributions.
The purpose of the weekly reaction page requirement is for seminar participants to develop particular perspectives on their readings. As E. M. Forester said, “How can I know what I think until I see what I say [write]?” The act of writing forces thoughts to be more precise and organized than they would otherwise be. The assignment is purposefully open-ended. Appropriate topics for reaction pages may be suggested, but most often, you will be left to select for yourself an interesting topic that relates to the readings in some way.
Once again, space should be considered a scarce resource. You should try to be refine your thoughts such that they can be concisely expressed on a single page. The most successful reaction pages focus on a single topic. Resist the temptation to write a few sentences each on four topics.
What are appropriate topics for reaction pages? You may develop an experiment that is inspired by one of the readings. Describe the experiment briefly, explain how it bears on relevant theories, and make predictions on the results. You may disagree with a particular claim. Explain why the claim is wrong, and why it is important that it is wrong. You may agree with a claim. Describe extensions to the claim, possible applications, formal models that capture the essence of the claim, or future directions for research. You may have nothing to say about a particular article. If so, explain why the article is not relevant to fundamental issues of concept learning or representation. Discuss the assumptions of the article, and why you find them inappropriate. Generally speaking, organizing your reaction page around a claim rather than a question stimulates more interest.
Grades will be based on the quality of reaction pages, seminar leading, and seminar participation. To get a good participation evaluation, it is not necessary to make many comments. Rare but thoughtful comments suffice. Here is the breakdown of the requirements for different grades:
A: Hands in acceptable or outstanding reactions for 11 out of 13 weeks. Good discussion leading and participation
B: hands in acceptable reactions for 9-10 out of 13 weeks. Good discussion leading and participation.
C: hands in acceptable reactions for 7-8 weeks.
Papers within a week are listed in the order you should read them. Readings in bold are required, the others are optional.
Week of 9/1: Introductions, overview of readings, class policies (Rob Goldstone)
No required reading, but see optional readings below
Goldstone, R. L., & Kersten, A. (2003). Concepts and Categorization. In A. F. Healy & R. W. Proctor (Eds.) Comprehensive handbook of psychology, Volume 4: Experimental psychology. (pp. 599-621). New Jersey: Wiley.
Komatsu, L. K. (1992). Recent views of conceptual structure. Psychological Bulletin, 112, 500-526.
Week of 9/8: Exemplar Models of Categorization (Ji Son)
Hintzman, D. L. (1986). Schema abstraction in a multiple-trace memory model. Psychological Review, 93 (4), 411–428.
Lamberts, K. (1998). The time course of categorization. Journal of Experimental Psychology: Learning, Memory and Cognition, 24, 695–711.
Lamberts, K. Brockdorff, N., & Heit, E. (2002). Perceptual processes in matching and recognition of complex pictures. Journal of Experimental Psychology: Human Perception and Performance, 28, 1176-1191.
Medin, D. L., & Schaffer, M. M. (1978). A context theory of classification learning. Psychological Review, 85, 207-238.
Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317-1323.
Week of 9/15: Neural Network Models of Concept Learning (Michael Roberts)
Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). In J. L. McClelland & D. E. Rumelhart (Eds.) Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2). Cambridge, MA: MIT Press. (pp. 7-57).
Kohonen, T. (2001). Self-Organizing Maps, Springer Series in Information Sciences, Vol. 30, Springer, Berlin, Heidelberg, New York, ISBN 3-540-67921-9.
Kruschke, J. K. (1993). Human category learning: Implications for back propagation models. Connection Science, 5, 3 36.
Week 9/22: Rule-based Categories (Justin Kantner)
Anderson, J. R., & Betz, J. (2001). A hybrid model of categorization. Psychonomic Bulletin and Review, 8, 629–647.
Medin, D. L., Wattenmaker, W. D., & Michalski, R. S. (1987). Constraints and preferences in inductive learning: An experimental study of human and machine performance. Cognitive Science, 11, 299-339.
Smith, E. E., & Sloman, S. A. (1994). Similarity- versus rule-based categorization. Memory and Cognition, 22, 377–386.
Week of 9/29: “Theory” Theory of Concepts (Coreen Farris & Aaron Loehrlein)
Gopnik, A., & Wellman, H. M. (1994). The "theory theory". In L. Hirschfeld & S. Gelman (Eds.), Mapping the mind: Domain specificity in culture and cognition. (pp. 257-293). New York: Cambridge University Press.
Heit, E. (1998). Influences of prior knowledge on selective weighting of category members. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 712-731.
Heit, E., & Bott, L. (2000). Knowledge selection in category learning. In D. L. Medin (Eds.), The Psychology of Learning and Motivation. (pp. 163-199). Academic Press.
Kim, N. S., & Ahn, W-K. (2002). Clinical Psychologists’ Theory-Based Representations of Mental Disorders Predict Their Diagnostic Reasoning and Memory. Journal of Experimental Psychology: General, 131, 451-476.
Week of 10/6: Perceptually Grounded Concepts (Janet Aisbett)
Boroditsky, L, & Ramscar, M. (2002). The roles of body and mind in abstract thought. Psychological Science, 13, 185-190.
Rodney A. Brooks (1991). Intelligence without representation, Artificial Intelligence, Volume 47, Issues 1-3, January 1991, Pages 139-159.
Goldstone, R. L. (2003). Learning to perceive while perceiving to learn. In R. Kimchi, M. Behrmann, & C. Olson (Eds.) Perceptual organization in vision: Behavioral and neural perspectives. (pp. 233-278). New Jersey: Lawrence Erlbaum Associates.
Week of 10/13: Conceptual Webs (Greg Gibbon)
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato's problem: The Latent Semantic Analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211-240.
Lenat, D. B., & Feigenbaum, E. A. (1991). On the thresholds of knowledge, Artificial Intelligence, 47, 185-250.
Sowa, J. F. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks Cole Publishing Co.
Week of 10/20: Conceptual Development (Adam Sheya & Rima Hanania)
Baillargeon, R. (1993). The Object Concept Revisited: New Directions in the Investigation of Infants' Physical Knowledge. In C. Granrund, ed.,Visual Perception and Cognition in Infancy. Hillsdale, NJ: Lawrence Erlbaum Associates.
Barsalou, L. W. (1987). The instability of graded structure: Implications for the nature of concepts. In U. Neisser (Ed.) Concepts and conceptual development (pp. 101-140). New York: Cambridge University Press.
Gelman, S. A., Coley, J. D., & Gottfried, G. M. (1994). Essentialist beliefs in children: The acquisition of concepts and theories. In L. A. Hirschfeld & S. A. Gelman (Eds.), Mapping the mind. (pp. 341-367). Cambridge, England: Cambridge University Press.
Gelman, S. A., & Markman, E. M. (1986). Categories and induction in young children. Cognition, 23, 183–209.
Gopnik, A., & Meltzoff, A. (1997). Words, Thoughts, and Theories. Cambridge, MA: MIT Press.
Imai, M., Gentner, D., & Uchida, N. (1994). Children's theories of word meaning: The role of shape similarity in early acquisition. Cognitive Development, 9, 45-75.
Jones, S.S., Smith, L.B., Landau, B. (1991). Object properties and knowledge in early lexical learning. Child Development, 62, 499-516.
Keil, F.C. (1989). Concepts, Kinds and Development. Cambridge, MA: Bradford Books/MIT 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.
Landau, B., Smith, L.B., & Jones, S.S. (1988). The importance of shape in early lexical learning. Cognitive Development, 3, 229-321.
Landau, B., Smith, L.B., & Jones, S.S. (1992). Syntactic context and the shape bias in children's and adult's lexical learning. Journal of Memory and Language, 31, 807-825.
Mandler, J.B., Bauer, P.J., & McDonough, L. (1991). Separating the sheep from the goats: differentiating global categories. Cognitive Psychology, 23, 263–298.
Markman, E.M. (1989). Categorization and naming in children: Problems of induction. Cambridge, MA: MIT Press.
Soja, N.N., Carey, S., & Spelke, E.S. (1991). Ontological categories guide young children's inductions of word meaning: Object terms and substance terms. Cognition, 38, 179-211
Wellman, H. M., & Gelman, S. A. (1992). Cognitive development: Foundational theories of core domains. Annual Review of Psychology, 43, 337-375.
Xu, F., & Carey, S. (1996). Infants' Metaphysics: The Case of Numerical Identity. Cognitive Psychology, 30, 111-53.
Week 10/27: Categorization, Perceptual Learning, and Expertise (Chuck Lindsey)
Tanaka, J., & Taylor, M. (1991). Object categories and expertise : is the basic level in the eye of the beholder? Cognitive Psychology, 23, 457-482.
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
Diamond, R., & Carey, S. (1986). Why faces are and are not of Experimental Psychology: General, 115, 107-117.
Gauthier, I. Tarr, M.J., Anderson A.W., Skudlarski, P., & Gore, J. C. (1999). Activation of the middle fusiform "face area" increases with expertise in recognizing novel objects. Nature Neuroscience, 2, 568-573
Needham, A., & Ormsbee, S. M. The development of object segregation in the first year of life. In R. Kimchi, M. Behrmann, & C. Olson (Eds.) Perceptual organization in vision: Behavioral and neural perspectives. (pp. 205-232). New Jersey: Lawrence Erlbaum Associates.
Özgen, E., & Davies, I. R. L. (2002). Acquisition of categorical color perception: A perceptual learning approach to the linguistic relatively hypothesis. Journal of Experimental Psychology: General, 131, 477-493.
Tanaka, J.W., & Taylor, M. (1991). Object categories and expertise: Is the basic level in the eye of the beholder? Cognitive Psychology, 23, 457-482.
Week 11/3: Natural Kinds and artifacts (Cynthia Drake)
Bloom, P. (1996). Intention, history, and artifact concepts. Cognition, 60, 1-29
Quine, W. V. (1977). Natural Kinds. in S. Schwartz (Ed.) Naming, Necessity, and Natural Kinds. Ithaca: Cornell University Press. (pp 155-175).
Malt, B.C. (1994). Water is not H2O. Cognitive Psychology, 27, 41-70.
Malt, B. C., & Johnson, E. J. (1992). Do artifact concepts have cores? Journal of Memory and Language, 31, 195–217.
Medin, D. L., & Ortony, A. (1989). Psychological essentialism. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning. (pp. 179-196). Cambridge, MA: Cambridge University Press.
Week 11/10: Cultural Perspectives on Concepts (Aaron Loehrlein)
Boyd, R. & Richerson, P. J. (1985). Culture and the Evolutionary Process. Chicago: University of Chicago Press.
Choi, I., Nisbett, R.E., & Smith, E.E. (1997). Culture, category salience, and inductive reasoning. Cognition, 65, 15-32.
Coley, J.D., Medin, D.L., & Atran, S. (1997). Does rank have its privilege? Inductive inferences within folkbiological taxonomies. Cognition, 64, 73-112.
Week of 11/17: Concepts and Word Meaning (Amy Scott)
Gelman, S. A., & Heyman, G. D. (1999). Carrot-eaters and creature believers: The effects of lexicalization on children’s inferences about social categories. Psychological Science, 10, 489–493.
Week 11/24: Thanksgiving
Week 12/1: Language and Conceptualization (David Landy & Shakila Shayan)
Bowerman, M., & Choi, S. (2003). Space under construction: Language-specific spatial categorization in first language acquisition. In D. Gentner & S. Goldin-Meadow (Eds.) Language in mind. Cambridge, MA: MIT Press. (pp. 387-427)
Yoshida, H., & Smith, L. B. (2003). Shifting ontological boundaries: How Japanese and English speaking children generalize names for animals and artifacts [target article + commentaries]. Developmental Science, 6, 1-34.
Gentner, D., & Boroditsky, L. (2000). Individuation, relativity, and early word learning. In M. Bowerman & S. Levinson (Eds.), Language acquisition and conceptual development (pp. 215–256). Cambridge: Cambridge University Press.
Imai, M., & Gentner, D. (1997). A cross-linguistic study of early word meaning: universal ontology and linguistic influence. Cognition, 62, 169–200.
Levinson, S. C. (1997). Language and cognition: The cognitive consequences of spatial description in Guugu Yimithirr. Journal of Linguistic Anthropology, 7, 98-131.
Levinson, S. C. (in press). Space in language and cognition: Explorations in cognitive diversity. Cambridge, MA: Cambridge University Press.
Lucy, J. (1992). Grammatical categories and cognition: A case study of the linguistic relativity hypothesis. Cambridge: Cambridge University Press.
Malt, B. C. (1995). Category coherence in cross-cultural perspective. Cognitive Psychology, 29, 85-148.
Malt, B. C., Sloman, S. A., Gennari, S., Shi, M., & Wang, Y. (1999). Knowing versus naming: Similarity of the linguistic categorization of artifacts. Journal of Memory and Language, 40, 230–262.
Medin, D. L., Lynch, E. B., Coley, J. D., & Atran, S. (1997). Categorization and reasoning among tree experts: Do all roads lead to Rome? Cognitive Psychology, 32, 49–96.
Slobin, D. I. (2003). Language and thought online: Cognitive consequences of linguistic relativity. In D. Gentner & S. Goldin-Meadow (Eds.) Language in mind. Cambridge, MA: MIT Press. (pp. 157-191)
Whorf, B.L. (1956). The relation of habitual thought and behavior to language. In J.B. Carroll (Ed.), Language, thought, and reality: Essays by B.L. Whorf (pp. 35-270). Cambridge, MA: MIT Press
Wolff, P., Medin, D. L., & Pankratz, C. (1999). Evolution and devolution of folkbiological knowledge. Cognition, 73, 177-204
Week of 12/8: Representation-building During Concept Learning (Damien Sullivan)
Chalmers, D., French, R., & Hofstadter, D. (1995). High-level perception, representation, and analogy: A critique of artificial-intelligence methodology. In D. Hofstadter (Ed.) Fluid concepts and creative analogies. New York: Basic Books.
Goldstone, R. L., Steyvers, M., Spencer-Smith, J., & Kersten, A. (2000). Interactions between perceptual and conceptual learning. In E. Diettrich & A. B. Markman (Eds.) Cognitive dynamics: Conceptual change in humans and machines. (pp. 191-228). Mahwah, NJ: Lawrence Erlbaum Associates.
Schank, R. C., Collins, G. C., & Hunter, L. E. (1986). Transcending inductive category formation in learning. Behavioral and Brain Sciences, 9, 639-686.
Additional topics not covered
Bayesian models of Categorization
Ross, B. H., & Murphy, G. L. (1996). Category-based predictions: Influence of uncertainty and feature associations. Journal of Expeirmental Psychology: Learning, Memory, & Cognition, 22, 736-753.
Similarity and Categorization
Bassok, M., & Medin, D. L. (1997). Birds of a feather flock together: Similarity judgments with semantically rich stimuli. Journal of Memory & Language, 36, 311-336.
Edelman, S. (1999). Representation and recognition in vision. Cambridge, MA: MIT Press.
Gardenfors, P. (2000). Conceptual spaces: The geometry of thought. Cambridge, MA: MIT Press.
Gentner, D., & Rattermann, M. J. (1991). Language and the career of similarity. In S. A. Gelman & J. P. Byrnes, (Eds.), Perspectives on language and thought interrelations in development (pp. 225-277). Cambridge, England: Cambridge University Press.
Hahn, U. (2003). Similarity. In L. Nadel (Ed.) Encyclopedia of Cognitive Science. London: Macmillan.
Rips, L. J. (1989). Similarity, typicality, and categorization. In S. Vosniadu & A. Ortony (Eds.), Similarity, analogy, and thought. (pp. 21-59). Cambridge: Cambridge University Press.
Rips, L. J., & Collins, A. (1993). Categories and resemblance. Journal of Experimental Psychology: General, 122, 468-486.
Shepard, R. N. (1962a) The analysis of proximities: Multidimensional scaling with an unknown distance function. Part I. Psychometrika, 27, 125-140.
Shepard, R. N. (1962b) The analysis of proximities: Multidimensional scaling with an unknown distance function. Part II. Psychometrika, 27, 219-246.
Shepard, R. N., & Arabie, P. (1979). Additive clustering: Representation of similarities as combinations of discrete overlapping properties. Psychological Review, 86, 87-123.
Smith, J. D., & Kemler, D. G. (1984). Overall similarity in adults' classification: The child in all of us. Journal of Experimental Psychology: General, 113, 137-159.
Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327-352.
Tversky, A., & Gati, I. (1982). Similarity, separability, and the triangle inequality. Psychological Review, 89, 123-154
Decision Bound Models of Categorization
Ashby, F. G. (1992). Multidimensional models of perception and cognition. Hillsdale, NJ: Erlbaum.
Ashby, F. G., & Gott, R. (1988). Decision rules in perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 33-53.
Ashby, F. G., & Maddox, W. T. (1993). Relations among prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 38, 423-466.
Ashby, F. G., & Maddox, W. T. (1998). Stimulus categorization. In M. H. Birnbaum (Ed) Measurement, judgment, and decision making: Handbook of perception and cognition. (pp. 251-301). San Diego, CA: Academic Press.
Ashby, F. G., & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological Review, 93, 154-179.
Categorization and Causality
Ahn, W. K. (1999). Effect of causal structure on category construction. Memory & Cognition, 27, 1008–1023.
Rehder, B., & Hastie, R. (2001). Causal knowledge and categories: The effects of causal beliefs on categorization, induction, and similarity. Journal of Experimental Psychology: General, 130, 323–360.
The Neuroscience of Categorization
Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 10, 442-481.
Ashby, F. G., & Waldron, E. M. (2000). The neuropsychological bases of category learning. Current Directions in Psychological Science, 9, 10-14.
Ishai, A., Ungerleider, L.G., Martin, A., Schouten, J.L., & Haxby, J.V. (1999). Distributed representation of objects in the human ventral visual pathway. Proceedings of the National Academy of Science, 96, 9379-9384
Knowlton, B. J., & Squire, L. R. (1994). The information acquired during artificial grammar learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 79-91.
Knowlton, B. J., & Squire, L. R. (1993). The learning of categories: Parallel brain systems for item memory and category knowledge. Science, 262, 1747-1749.
Knowlton, B. J., Squire, L. R., & Gluck, M. (1994). Probabilistic classification learning in amnesia. Learning and Memory, 1, 106-120.
Kolodny, J. A. (1994). Memory processes in classification learning: An investigation of amnesic performance in categorization of dot patterns and artistic styles. Psychological Science, 5, 164-169.
Nosofsky, R. M., & Zaki, S. R. (1998). Dissociations between categorization and recognition in amnesic and normal individuals: An exemplar-based interpretation. Psychological Science, 9, 247-255.
Smith, E. E., Patalano, A. L., & Jonides, J. (1998). Alternative strategies of categorization. Cognition, 65(2-3), 167–196.
Warrington, E.K., & Shallice, T. (1984). Category specific semantic impairments. Brain, 107, 829-854.
Nonanalytic Concept Formation
Brooks, L. R. (1987). Decentralized control of categorization: The role of prior processing episodes. In U. Neisser (Ed.), Concepts and conceptual development: The ecological and intellectual factors in categorization. (pp. 141-174). Cambridge: Cambridge University Press.
Kemler Nelson, D. G. (1989). The nature and occurrence of holistic processing. In: B. E. Shepp & S. Ballesteros (Eds.) Object Perception: Structure & Process. (pp. 387-419). Hillsdale, NJ: Erlbaum.
Shanks, D. R., Darby, R. J., & Charles, D. (1998). Resistance to interference in human associative learning: Evidence of configural processing. Journal of Experimental Psychology: Animal Behavior Processes, 24(2), 136–150.
Shanks, D. R., & StJohn, M. F. (1994). Characteristics of dissociable human learning-systems. Behavioral and Brain Sciences, 17(3), 367–395.
Smith, L. B. (1992). A model of perceptual classification in children and adults. Psychological Review, 96, 125-144.
Abstract Concepts and Metaphors
Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 155-170.
Gibbs, R. W. (1992). Categorization and metaphor understanding. Psychological Review, 99, 572-577.
Glucksberg, S., & Keysar, B. (1990). Understanding metaphorical comparisons: Beyond similarity. Psychological Review, 97, 3-18.
Lakoff, G. (1993). The contemporary theory of metaphor. in A. Ortony (ed.) Metaphor and thought. Cambridge: Cambridge University Press. (pp. 202-251).
Rumelhart, D. E. (1993). Some problems with the notion of literal meaning. in A. Ortony (ed.) Metaphor and thought. Cambridge: Cambridge University Press. (pp. 71-82).
Carey, S. (1985). Conceptual change in childhood. Cambridge, MA: Bradford Books.
Gholson, B., & Barker, P. (1985). Kuhn, Lakatos, and Laudan. American Psychologist, 40, 755-769.
Thagard, P. (1992). Conceptual Revolutions. Princeton University Press: Princeton, NJ.
Vosniadu, S., & Brewer, W. F. (1994). Mental models of the day/night cycle. Cognitive Science, 18, 123-183.
Philosophical Approaches to Concepts
Fodor, J. A. (1975). The language of thought. New York: Thomas Y. Crowell.
Fodor, J. A. (1998). Concepts: Where Cognitive Science Went Wrong. New York: Oxford University Press.
Fodor, J. A., & Lepore, E. (1992). Holism: A Shopper's Guide. Cambridge, MA: Basil Blackwell.
Putnam, H. (1970). Is semantics possible? In H. Kiefer and M. Munitz (Eds.), Language, belief, and knowledge. Minneapolis: University of Minnesota Press.
Putnam, H. (1973). Meaning and reference. The Journal of Philosophy, 70, 699-711
Quine, W. (1951/1980). Two dogmas of empiricism. In From a logical point of view: Nine logico-philosophical essays (pp. 20-46). Cambridge, MA: Harvard University Press.
Wittgenstein, L. (1953). Philosophical investigations. New York: Macmillan.
Decompositional and Atomic Accounts of Conceptual Representation
Fodor, J., Garrett, M., Walker, E., & Parkes, C. M. (1980). Against definitions. Cognition, 8, 263-367.
Johnson-Laird, P. N. (1983). Mental models. Cambridge, MA: Harvard University Press. Chapter 10.
McNamara, T. P., & Miller, D. L. (1989). Attributes of theories of meaning. Psychological Bulletin, 106, 355-376.
Margolis, E. (1998). How to Acquire a Concept. Mind & Language, 13, no. 3, 347-369.
Medin, D.L., & Shoben, E.J. (1988). Context and structure in conceptual combination. Cognitive Psychology, 20, 158-190.
Murphy, G. L. (1988). Comprehending complex concepts. Cognitive Science, 12, 529-562.
Wisniewski, E. J. (1997). When concepts combine. Psychonomic Bulletin and Review, 4, 167-183.
Wisniewski, E. J. (1998). Property instantiation in conceptual combination. Memory and Cognition, 26, 1330-1347.
Prototype Theories of Concepts
Armstrong, S.E., Gleitman, L.R., & Gleitman, H. (1983). What some concepts might not be. Cognition, 13, 263-308.
Lakoff, G. (1987). Cognitive models and prototype theory. In U. Neisser (Ed.) Concepts and conceptual development. Cambridge University Press: Cambridge. (pp. 63-100).
Minda, J. P., & Smith, J. D. (2001). Prototypes in category learning: The effects of category size, category structure, and stimulus complexity. Journal of Experimental Psychology: Learning Memory and Cognition, 27(3), 775–799.
Smith, J. D., & Minda, J. P. (2000). Thirty categorization results in search of a model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 3–27.
Smith, J. D., & Minda, J. P. (2002). Distinguishing prototype-based and exemplar-based processes in dot-pattern category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 28, 800-811.
Induction, Inference and Category use
Heit, E. (2000). Properties of inductive reasoning. Psychonomic Bulletin & Review, 7(4), 569–592.
Heit, E., & Rubinstein, J. (1994). Similarity and property effects in inductive reasoning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 20, 411-422.
Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., & Shafir, E. (1990). Category-based induction. Psychological Review, 97, 185–200.
Ross, B. H. (1997). The use of categories affects classification. Journal of Memory and Language, 37, 240–267.
Ross, B. H. (1999). Post-classification category use: The effects of learning to use categories after learning to classify. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 743–757.
Sloman, S. (1993). Feature-based induction. Cognitive Psychology, 25, 231-280.
Sloman, S.A. (1998). Categorical inference is not a tree: The myth of inheritance hierarchies. Cognitive Psychology, 35, 1-33.
Yamauchi, T., & Markman, A. B. (2000a). Inference using categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 776–795.
The (In)stability of Concepts
Barsalou, L. W. (1987). The instability of graded structure: Implications for the nature of concepts. In U. Neisser (Ed.), Concepts and Conceptual Development, (pp. 101-140). New York: Cambridge University Press.
Barsalou, L. W. (1982). Context-independent and context-dependent information in concepts. Memory and Cognition, 10, 82-93
The Hierarchical Organization of Concepts
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