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

Phone: 855-4853

Email: rgoldsto@indiana.edu

Web site: http://cognitrn.psych.indiana.edu/rgoldsto/courses/concepts/index.htm

 

 

Course Description

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.

 

Reaction Pages

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:

http://www.indiana.edu/~annotate/

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.

 

 

Grading

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.

 

 

 

 

 

 

Weekly readings

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

Optional readings:

 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.

Kruschke, J. (in press).  Category learning. In K. Lamberts & R. L. Goldstone (Eds.)  Handbook of cognitive psychology.  London: Sage.

Laurence, S., & Margolis, E.  (2003).  Concepts.  In T. Warfield & S. Stich (Eds.) The Blackwell Guide to the Philosophy of Mind. Blackwell: London.

Komatsu, L. K. (1992).  Recent views of conceptual structure.  Psychological Bulletin, 112, 500-526.

Markman, A. B., & Gentner, D. (2001).  Thinking.  Annual Review of Psychology, 52, 223-247.

Medin, D. L., Lynch, E. B., & Solomon, K. O. (2000).  Are there kinds of concepts?  Annual Review of Psychology, 51, 121-147.

 

Week of 9/8: Exemplar Models of Categorization (Ji Son)

Estes, W. K. (1994).  Classification and Context.  New York: Oxford University Press.  Chapters 1 and 2

Nosofsky, R. M. (1984).  Choice, similarity, and the context theory of classification.  Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104-114.

Kruschke, J. K., (1992).  ALCOVE: An exemplar-based connectionist model of category learning.  Psychological Review, 99, 22-44.

Optional readings:

Allen, S. W., & Brooks, L. R. (1991).  Specializing the operation of an explicit rule.  Journal of Experimental Psychology: General, 120, 3-19.

Hintzman, D. L. (1986). Schema abstraction in a multiple-trace memory model. Psychological Review, 93 (4), 411–428.

Kruschke, J. K. (2001). Toward a Unified Model of Attention in Associative Learning.  Journal of Mathematical Psychology, 45, 812-863.

Lamberts, K. (1998). The time course of categorization. Journal of Experimental Psychology: Learning, Memory and Cognition, 24, 695–711.

Lamberts, K. (2000). Information-accumulation theory of speeded categorization.  Psychological Review, 107, 227-260.

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.

Love, B. C., Medin, D. L., & Gureckis, T. M. (in press).  SUSTAIN: A Network Model of Category Learning.  Psychological Review

Nosofsky, R. M., & Palmeri, T. J. (1997).  An exemplar-based random walk model of speeded classification.  Psychological Review, 104, 266-300.

Nosofsky, R. M. (1988).  Exemplar-based accounts of relations between classification, recognition, and typicality,  Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700-708.

Nosofsky, R. M. (1991).  Tests of an exemplar model for relating perceptual classification and recognition memory.  Journal of Experimental Psychology: Human Perception and Performance, 17, 3-27.

Nosofsky, R. M., & Palmeri, T. J. (1996). Learning to classify integral-dimension stimuli. Psychonomic Bulletin & Review, 3(2), 222–226.

Medin, D. L.,  & Schaffer, M. M. (1978).  A context theory of classification learning.  Psychological Review, 85, 207-238.

Juslin, P., & Persson, M. (2002). PROBabilities from EXemplars (PROBEX): A “lazy” algorithm for probabilistic inference from generic knowledge. Cognitive Science, 26, 563-607.

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).

Rumelhart, D. E., & Zipser, D. (1985).  Feature discovery by competitive learning.  Cognitive Science, 9, 75-112.

Optional readings:

Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General, 117, 227-247.

Gureckis, T. M., & Love, B. C. (2003).  Towards a unified account of supervised and unsupervised category learning.  Connection Science, 15, 1-24.

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.

 Tijsseling A.G, & Gluck M.A (2002), A connectionist approach to processing dimensional interaction, Connection Science, 14, 1-48.

 

Week 9/22: Rule-based Categories (Justin Kantner)

Sloman, S. A. (1996).  The empirical case for two systems of reasoning.  Psychological Bulletin, 119, 3-22.

Nosofsky, R. M., Palmeri, T. J., & McKinley, S. K. (1994).  Rule-plus-exception model of classification learning, Psychological Review, 101, 53-79.

Optional Reading:

Anderson, J. R., & Betz, J. (2001). A hybrid model of categorization. Psychonomic Bulletin and Review, 8, 629–647.

Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140.

Hahn, U., & Chater, N. (1998). Similarity and rules: distinct? exhaustive? empirically distinguishable? Cognition, 65(2-3), 197–230.

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)

Murphy, G. L., & Medin, D. L. (1985).  The role of theories in conceptual coherence.  Psychological Review, 92, 289-316.

Murphy, G. L. (2002).  The big book of concepts.  Cambridge, MA: MIT Press.  (Chapter 6, pp. 141-197).

Optional reading:

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)

Harnad, Stevan (1990) The Symbol Grounding Problem. Physica D 42:335-346.

Barsalou, L. (1999).  Perceptual symbol systems.  Behavioral and Brain Sciences, 22, 577-660. [target article and commentaries]

Cangelosi, A., Greco, A. & Harnad, S. (2002) Symbol Grounding and the Symbolic Theft Hypothesis. In: Cangelosi, A. & Parisi, D. (Eds.) Simulating the Evolution of Language. London, Springer.

Optional readings:

Aisbett, J., & Gibbon, G. (2001).  A general formulation of conceptual spaces as a meso level representation.  Artificial Intelligence, 133, 189-232.

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.

Goldstone, R. L., & Rogosky, B. J. (2002). Using relations within conceptual systems to translate across conceptual systems.  Cognition, 84, 295-320.

Optional reading:

Goldstone, R. L. (1996). Isolated and Interrelated Concepts. Memory & Cognition, 24, 608-628

Lenat, D. B., & Feigenbaum, E. A. (1991).  On the thresholds of knowledge, Artificial Intelligence, 47, 185-250.

The CYC tutorial

Conceptual Graphs (see also John Sowa’s web page)

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)

Required readings:

Keil, F. C. (1990).  Constraints on constraints: Surveying the epigenetic landscape.  Cognitive Science, 14, 135-163.

Carey, S. (1990).  Knowledge acquisition: Enrichment or conceptual change?  In E. Margolis & S. Laurence (Eds.) Concepts.  Cambridge, MA: MIT Press (pp. 459-487).

 

Optional Readings:

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.

Gelman, S, & Wellman, H. M.  Insides and essences: Early understandings of the non-obvious.

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)

Required readings:

Goldstone, R. L, Lippa, Y., & Shiffrin, R. M. (2001). Altering object representations through category learning.  Cognition, 78, 27-43.

Tanaka, J., & Taylor, M. (1991). Object categories and expertise : is the basic level in the eye of the beholder? Cognitive Psychology, 23, 457-482.

Optimal readings:

Brooks, L. R., Norman, G. R., & Allen, S. W. (1991).  Role of specific similarity in a medical diagnostic task.   Journal of Experimental Psychology: General, 120, 278-287.

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., Skudlarski, P., Gore, J. C., & Anderson, A. W. (2000).  Expertise for cars and birds recruits brain areas involved in face recognition.  Nature Neuroscience, 3, 191-197.

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

Goldstone, R. L. (2000).  Unitization during category learning.  Journal of Experimental Psychology: Human Perception and Performance, 26, 86-112

Goldstone, R. L. (1994). Influences of categorization on perceptual discrimination. Journal of Experimental Psychology: General, 123, 178-200.

Goldstone, R. L., & Steyvers, M. (2001).  The sensitization and differentiation of dimensions during category learning.  Journal of Experimental Psychology: General, 130, 116-139.

Livingston, K., Andrews J. & Harnad, S. (1998) Categorical Perception Effects Induced by Category Learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 24(3) 732-753

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)

Required Readings:

Ahn, W. (1998). Why are different features central for natural kinds and artifacts? The role of causal status in determining feature centrality. Cognition, 69, 135-178.

Markman, E. M. (1989).  Categorization and Naming in Children: Problems of Induction.  Boston: MIT Press.  Chapter 5.

Optional Readings:

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)

Medin, D. L., & Atran, S. (in press).  The native mind: Biological categorization and reasoning in development and across cultures.  Psychological Review.

Alexrod, R. (1997).  The dissemination of culture: A model with local convergence and global polarization.  The Journal of Conflict Resolution, 41, 203-226.

Boyd, R., & Richerson, P. J. (2002).  Group beneficial norms can spread rapidly in a structured population.  Journal of Theoretical Biology, 215, 287-296.

Optional readings:

Boyd, R. & Richerson, P. J. (1985). Culture and the Evolutionary Process. Chicago: University of Chicago Press.

Boyd, R. & Richerson, P. J.  Memes: Universal acid or a better mouse trap. For presentation of Conference on Memes held at Cambridge University.

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.

Malt, B.C. (1995). Category coherence in cross-cultural perspective. Cognitive Psychology, 29, 85-148.

 

Week of 11/17: Concepts and Word Meaning (Amy Scott)

Murphy, G. L. (2002).  The big book of concepts.  Cambridge, MA: MIT Press.  Chapter 11, (pp. 385-441).

Gentner, D. (2003).  Why we’re so smart.  In D. Gentner & S. Goldin-Meadow (Eds.)  Language in mind.  Cambridge, MA: MIT Press. (pp. 195-235)

Optional readings:

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.

Yoshida, H., & Smith, L. B. (2003).  Correlation, concepts, and cross-linguistic differences.  [response].  Developmental Science, 6, 30-34.

Optional readings:

Boroditsky, L. (2000).  Metaphoric structuring: Understanding time through spatial metaphors.  Cognition, 75, 1-28.

Boroditsky, L. (2001).  Does language shape thought?  Mandarin and English speakers’ conceptions of time.  Cognitive Psychology, 43, 1-22.

Gennari, S. P, Sloman, S. A., Malt, B. C., & Fitch, T. (2002).  Motion events in language and cognition.  Cognition, 49-79.

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.

Proffitt, J. B., Coley, J. D., & Medin, D. L. (2000).  Expertise and category-based induction.  Journal of Experimental Psychology: Learning, Memory, & Cognition, 26, 811-828.

Roberson, D., Davies, I., & Davidoff, J. (2000).  Color categories are not universal: Replications and new evidence from a stone-age culture.  Journal of Experimental Psychology: General, 129, 369-398.

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)

Wisniewski, E. J., & Medin, D. L. (1994).  On the interaction of theory and data in concept learning.  Cognitive Science, 18, 221-281.

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.

Hofstadter, D., & Mithcell, M. (1995).  The Copycat project: A model of mental fluidity and analogy-making.  In D. Hofstadter (Ed.)  Fluid concepts and creative analogies.  New York: Basic Books.

Optional readings:

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.

Schyns, P. G., Goldstone, R. L, & Thibaut, J. (1998).  Development of features in object concepts.  Behavioral and Brain Sciences, 21, 1-54

 

Additional topics not covered

Bayesian models of Categorization

Anderson, J. R. (1991).  The adaptive nature of human categorization.  Psychological Review, 98, 419-429.

Tenenbaum, J. B., & Griffiths, T. L. (2001).  Generalization, similarity, and Bayesian inference.  Behavioral and Brain Sciences, 24, 629-640.

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.

Goldstone, R. L. (1994a).  Similarity, interactive activation, and mapping.  Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 3-28.

Goldstone, R. L. (1994b).   The role of similarity in categorization: Providing a groundwork.  Cognition, 52, 125-157.

Hahn, U. (2003).  Similarity.  In L. Nadel (Ed.) Encyclopedia of Cognitive Science.  London: Macmillan.

Hahn, U., Chater, N., & Richardson, L. B. (2003).  Similarity as transformation.  Cognition, 87, 1-32.

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

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. (in press).  Categorization and causal reasoning.  Cognitive Science.

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.

Sloman, S. A., Loves, B. C., Ahn, W. (1998).  Feature centrality and conceptual coherence.  Cognitive Science, 222, 189-228.

 

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.

Regehr, G. & Brooks, L. R. (1993).  Perceptual manifestations of an analytic structure: The priority of holistic individuation.  Journal of Experimental Psychology: General, 122, 92-114.

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

Boroditsky, L. (2000).  Metaphoric structuring: Understanding time through spatial metaphors.  Cognition, 75, 1-28.

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).

Rehder, B., & Ross, B. H. (2001).  Abstract coherent categories.  Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1261-1275.

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).

 

Conceptual Change

Carey, S. (1985).  Conceptual change in childhood. Cambridge, MA: Bradford Books.

Carey, S.  Knowledge acquisition: Enrichment or conceptual change?

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.

Millikan, R. (1998). A Common Structure for Concepts of Individuals, Stuffs, and Real Kinds: More Mama, More Milk, and More Mouse. Behavioral and Brain Sciences, 21, 55-65.

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.

 

Conceptual Combination

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

Posner, M. I., & Keele, S. W. (1968).  On the genesis of abstract ideas.  Journal of Experimental Psychology, 77, 28-38.

Posner, M. I., & Keele, S. W. (1970).  Retention of abstract ideas.  Journal of Experimental Psychology, 83, 304-308.

Rosch, E., & Mervis, C. B. (1975).  Family resemblances: Studies in the internal structure of categories.  Cognitive Psychology, 7, 573-605.

Armstrong, S.E., Gleitman, L.R., & Gleitman, H. (1983). What some concepts might not be. Cognition, 13, 263-308.

Blair, M., & Homa, D. (2001).  Expanding the search for a linear separability constraint on category learning.  Memory & Cognition, 29, 1153-1164.

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.

Markman, A. B., & Ross. B. H. (2003).  Category use and category learning.  Psychological Bulletin, 129, 592-613.

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

Rosch, E., Mervis, C. B., Gray, W., Johnson, D., & Boyes-Braem, P. (1976).  Basic objects in natural categories.  Cognitive Psychology, 7, 573-605.

Murphy, G. L. , & Smith, E. E. (1982). Basic-level superiority in picture categorization. Journal of Verbal Learning and Verbal Behavior, 21, 1-20.

Murphy, G. L., & Wisniewksi, E.J. (1989).  Categorizing objects in isolation and in scenes: What a superordinate is good for, Journal of Experimental Psychology: Learning, Memory, & Cognition, 15, 572-586.

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.

Jolicoeur, P., Gluck, M. A., & Kosslyn, S. M. (1984).  Pictures and names: Making the connection.  Cognitive Psychology, 16, 243-275.