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11 - Computational Models of Categorization

from Part III - Computational Modeling of Basic Cognitive Functionalities

Published online by Cambridge University Press:  21 April 2023

By
Kenneth J. Kurtz
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

This chapter provides an overview of approaches to formal modeling in the domain of categorization. The core psychological processes addressed by models are: generating a classification decision in response to a stimulus and constructing category representations based on supervised experience. A taxonomy is provided that organizes the formal models in terms of their use of a fixed, combined, or constructed approach to predicting categories under either a cue-based or item-based framework. The chapter gives in-depth coverage of a leading approach (exemplar models) as well as an emerging alternative: a constructed cue-based model (DIVA) that differs from competing accounts by learning to reconstruct the input features via sets of category-specific weights and using the degree of reconstructive success (i.e., goodness-of-fit to the category) to determine the likelihood of membership.

Keywords

categorizationhuman classification learningcomputational modelingsimilarityrulesabstractionconnectionist models
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 373 - 399
Publisher: Cambridge University Press
Print publication year: 2023

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References

Aha, D. W., & Goldstone, R. L. (1992). Concept learning and flexible weighting. In Proceedings of the fourteenth annual conference of the Cognitive Science Society (vol. 534, p. 539).Google Scholar
Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98(3), 409–429.CrossRefGoogle Scholar
Ashby, F. G., & Alfonso-Reese, L. A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39(2), 216–233.Google Scholar
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, 105(3), 442–481.Google Scholar
Ashby, F. G., & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37(3), 372–400.Google Scholar
Ashby, F. G., & Maddox, W.T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178.CrossRefGoogle ScholarPubMed
Ashby, F. G., & Rosedahl, L. (2017). A neural interpretation of exemplar theory. Psychological Review, 124(4), 472–482.CrossRefGoogle ScholarPubMed
Austerweil, J. L., & Griffiths, T. L. (2013). A nonparametric Bayesian framework for constructing flexible feature representations. Psychological Review, 120(4), 817–851.Google Scholar
Austerweil, J. L., Liew, S. X., Conaway, N., & Kurtz, K. J. (under review). Creating something different: similarity, contrast, and representativeness in categorization.Google Scholar
Barsalou, L. W. (1983). Ad hoc categories. Memory & Cognition, 11(3), 211–227.CrossRefGoogle ScholarPubMed
Battleday, R. M., Peterson, J. C., & Griffiths, T. L. (2020). Capturing human categorization of natural images by combining deep networks and cognitive models. Nature Communications, 11(1), 1–14.Google Scholar
Brooks, L.R. (1978). Nonanalytic concept formation and memory for instances. In Rosch, E. & Lloyd, B., (Eds.), Cognition and Categorization, (pp. 169–211). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Conaway, N., & Kurtz, K. J. (2017a). Similar to the category, but not the exemplars: a study of generalization. Psychonomic Bulletin & Review, 24(4), 1312–1323.Google Scholar
Conaway, N., & Kurtz, K. J. (2017b). Solving nonlinearly separable classifications in a single-layer neural network. Neural Computation, 29(3), 861–866.Google Scholar
Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140.Google Scholar
Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407(6804), 630–633.CrossRefGoogle ScholarPubMed
Fried, L. S., & Holyoak, K. J. (1984). Induction of category distributions: a framework for classification learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(2), 234–257.Google Scholar
Gentner, D., & Kurtz, K. J. (2005). Learning and using relational categories. In Ahn, W. L., Goldstone, R. L., Love, B. C., Markman, A. B., & Wolff, P. W. (Eds.). Categorization Inside and Outside the Lab. Washington, DC: American Psychological Association.Google Scholar
Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: an adaptive network model. Journal of Experimental Psychology: General, 117(3), 227–247.Google Scholar
Goldstone, R. L. (1994). The role of similarity in categorization: providing a groundwork. Cognition, 52, 125–157.Google Scholar
Goldstone, R. L., Kersten, A., & Carvalho, P. F. (2018). Categorization and concepts. In J. Wixted (Ed.), Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience (vol. 3, pp. 1–43). New York, NY: Wiley.Google Scholar
Goldstone, R. L., Schyns, P. G., & Medin, D. L. (1997). Learning to bridge between perception and cognition. The Psychology of Learning and Motivation, 36, 1–14.Google Scholar
Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule‐based concept learning. Cognitive Science, 32(1), 108–154.Google Scholar
Gureckis, T. M., & Love, B. C. (2003). Towards a unified account of supervised and unsupervised category learning. Journal of Experimental & Theoretical Artificial Intelligence, 15(1), 1–24.CrossRefGoogle Scholar
Gureckis, T. M., & Markant, D. B. (2012). Self-directed learning: a cognitive and computational perspective. Perspectives on Psychological Science, 7(5), 464–481.Google Scholar
Hampton, J. A. (1981). An investigation of the nature of abstract concepts. Memory & Cognition, 9(2), 149–156.Google Scholar
Homa, D., Sterling, S., & Trepel, L. (1981). Limitations of exemplar-based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Human Learning and Memory, 7(6), 418–439.Google Scholar
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3(1), 79–87.Google Scholar
Jäkel, F., Schölkopf, B., & Wichmann, F. A. (2008). Generalization and similarity in exemplar models of categorization: insights from machine learning. Psychonomic Bulletin & Review, 15(2), 256–271.Google Scholar
Jäkel, F., Schölkopf, B., & Wichmann, F. A. (2009). Does cognitive science need kernels?. Trends in Cognitive Sciences, 13(9), 381–388.Google Scholar
Jones, M., & Love, B. C. (2011). Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34(4), 169–188.Google Scholar
Katz, J. J., & Fodor, J. A. (1963). The structure of a semantic theory. Language, 39(2), 170–210.Google Scholar
Kemp, C. (2012). Exploring the conceptual universe. Psychological Review, 119(4), 685–722.Google Scholar
Knapp, A. G., & Anderson, J. A. (1984). Theory of categorization based on distributed memory storage. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(4), 616–637.Google Scholar
Kruschke, J. K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99(1), 22–44.Google Scholar
Kruschke, J. K. (1993). Human category learning: implications for backpropagation models. Connection Science, 5(1), 3–36.CrossRefGoogle Scholar
Kruschke, J. K. (2006). Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review, 113(4), 677–699.CrossRefGoogle ScholarPubMed
Kruschke, J. K. (2008). Models of categorization. In Sun, R. (Ed.), The Cambridge Handbook of Computational Psychology, (pp. 267–301). Cambridge: Cambridge University Press.Google Scholar
Kruschke, J. K., & Johansen, M. K. (1999). A model of probabilistic category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(5), 1083–1119.Google Scholar
Kurtz, K. J. (2007). The divergent autoencoder (DIVA) model of category learning. Psychonomic Bulletin & Review, 14(4), 560–576.Google Scholar
Kurtz, K. J. (2015). Human category learning: toward a broader explanatory account. In B. H. Ross (Ed.), Psychology of Learning and Motivation, (vol. 63, pp. 77–114). New York, NY: Academic Press.Google Scholar
Kurtz, K. J., & Conaway, N. (under review). Exemplar models can’t see the forest for the trees: a critical test and model comparison.Google Scholar
Kurtz, K. J., Levering, K. R., Stanton, R. D., Romero, J., & Morris, S. N. (2013). Human learning of elemental category structures: revising the classic result of Shepard, Hovland, and Jenkins (1961). Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(2), 552–572.Google Scholar
Kurtz, K. J., Mason, M., & Wetzel, M. (2020). Investigating discriminative constraints to the divergent autoencoder (DIVA) model of human category learning. Poster presented at the 2020 Annual Meeting of the Psychonomic Society.Google Scholar
Kurtz, K. J., & Silliman, D. C. (2019). Warning: the exemplars in your category representation may not be the ones experienced during learning. In Goel, A., Seifert, C., & Freska, C. (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 56–57). Cognitive Science Society.Google Scholar
Kurtz, K. J, & Wetzel, M. (2021). On the generalization of simple alternating category structures. Cognitive Science, 45(4), e12972.Google Scholar
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338.Google Scholar
Lamberts, K. (1998). The time course of categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24(3), 695–711.Google Scholar
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.Google Scholar
Lee, M. D., & Navarro, D. J. (2002). Extending the ALCOVE model of category learning to featural stimulus domains. Psychonomic Bulletin & Review, 9(1), 43–58.Google Scholar
Levering, K. R., Conaway, N., & Kurtz, K. J. (2020). Revisiting the linear separability constraint: new implications for theories of human category learning. Memory & Cognition, 48, 335–347.Google Scholar
Levering, K. R., & Kurtz, K. J. (2015). Observation versus classification in supervised category learning. Memory & Cognition, 43(2), 266–282.Google Scholar
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111(2), 309–332.Google Scholar
Luce, R. D. (1963). Detection and recognition. In Luce, R. D., Bush, R. R., & Galanter, E. (Eds.), Handbook of Mathematical Psychology, (pp. 103–189). New York, NY: Wiley.Google Scholar
Markman, A. B., & Ross, B. H. (2003). Category use and category learning. Psychological Bulletin, 129, 592–613.Google Scholar
McClelland, J. L., & Rumelhart, D. E. (1986). A distributed model of memory. In Rumelhart, D. L., & McClelland, J. L., (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Vol II. Applications (pp. 170–215). Cambridge MA: MIT Press.Google Scholar
Medin, D. L. (1989). Concepts and conceptual structure. American Psychologist, 44(12), 1469–1481.Google Scholar
Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.Google Scholar
Medin, D. L., & Schwanenflugel, P. J. (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7(5), 355–368.Google Scholar
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.Google Scholar
Minda, J. P., & Smith, J. D. (2002). Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(2), 275–292.Google ScholarPubMed
Murphy, G. L. (2002). The Big Book of Concepts. Cambridge, MA: MIT press.Google Scholar
Murphy, G. L. (2003). Ecological validity and the study of concepts. In Ross, B., (Ed.), The Psychology of Learning and Motivation (vol. 43, pp. 1–41). San Diego, CA: Elsevier Academic Press.Google Scholar
Murphy, G. L. (2005). The study of concepts inside and outside the laboratory: Medin versus Medin. In Ahn, W. L., Goldstone, R. L., Love, B. C., Markman, A. B., & Wolff, P. W., (Eds.), Categorization Inside and Outside the Laboratory, (pp. 179–195). Washington, DC: American Psychological Association.Google Scholar
Murphy, G. L. (2016). Is there an exemplar theory of concepts?. Psychonomic Bulletin & Review, 23(4), 1035–1042.Google Scholar
Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289–316.Google Scholar
Murphy, G. L., & Ross, B. H. (1994). Predictions from uncertain categorizations. Cognitive Psychology, 27, 148–193.Google Scholar
Navarro, D. J. (2005). Analyzing the RULEX model of category learning. Journal of Mathematical Psychology, 49(4), 259–275.Google Scholar
Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(1), 104–114.Google Scholar
Nosofsky, R. M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General, 115(1), 39–57.Google Scholar
Nosofsky, R. M. (1992). Similarity scaling and cognitive process models. Annual Review of Psychology, 43(1), 25–53.Google Scholar
Nosofsky, R. M., Gluck, M., Palmeri, T. J., McKinley, S. C., & Glauthier, P. (1994). Comparing models of rule-based classification learning: a replication and extension of Shepard, Hovland, and Jenkins (1961). Memory & Cognition, 22, 352–369.CrossRefGoogle ScholarPubMed
Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104(2), 266–300.Google Scholar
Nosofsky, R. M., & Palmeri, T. J. (1998). A rule-plus-exception model for classifying objects in continuous-dimension spaces. Psychonomic Bulletin & Review, 5(3), 345–369.Google Scholar
Nosofsky, R. M., Palmeri, T. J., & McKinley, S. K. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 55–79.CrossRefGoogle ScholarPubMed
Nosofsky, R. M., Sanders, C. A., Gerdom, A., Douglas, B. J., & McDaniel, M. A. (2017). On learning natural-science categories that violate the family-resemblance principle. Psychological Science, 28(1), 104–114.Google Scholar
Palmeri, T. J., Love, B. C., & Turner, B. M. (2017). Model-based cognitive neuroscience. Journal of Mathematical Psychology, 76(Part B), 59–64.Google Scholar
Pape, A. D., Kurtz, K. J., & Sayama, H. (2015). Complexity measures and concept learning. Journal of Mathematical Psychology, 64, 66–75.CrossRefGoogle Scholar
Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109(3), 472–491.CrossRefGoogle Scholar
Poggio, T., & Girosi, F. (1990). Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247(4945), 978–982.Google Scholar
Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.Google Scholar
Pothos, E. M., Perlman, A., Bailey, T. M., et al. (2011). Measuring category intuitiveness in unconstrained categorization tasks. Cognition, 121(1), 83–100.Google Scholar
Pothos, E. M., & Wills, A. J. (2011). Formal Approaches in Categorization. Cambridge: Cambridge University Press.Google Scholar
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.CrossRefGoogle Scholar
Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3(3), 382–407.Google Scholar
Rehder, B., & Hoffman, A. B. (2005). Eyetracking and selective attention in category learning. Cognitive Psychology, 51(1), 1–41.Google Scholar
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H., & Prokasy, W. F. (Eds.), Classical Conditioning II: Current Research and Theory (pp. 64–99). New York, NY: Appleton-Century-Crofts.Google Scholar
Roads, B. D., & Love, B. C. (2020). Enriching ImageNet with human similarity judgments and psychological embeddings. arXiv preprint arXiv:2011.11015Google Scholar
Rosch, E., & Mervis, C. B. (1975). Family resemblances: studies in the internal structure of categories. Cognitive Psychology, 7(4), 573–605.Google Scholar
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408.Google Scholar
Ross, B. H., Taylor, E. G., Middleton, E. L., & Nokes, T. J. (2008). Concept and category learning in humans. In Roediger, H. L., III (Ed.), Cognitive Psychology of Memory (pp. 535–557). Oxford: Elsevier.Google Scholar
Rosseel, Y. (2002). Mixture models of categorization. Journal of Mathematical Psychology, 46(2), 178–210.Google Scholar
Rumelhart, D. E. (1980). Schemata: the building blocks. In R. J. Spiro, B. C. Bruce, & W. F. Brewer (Eds.), Theoretical Issues in Reading Comprehension. London: Routledge.Google Scholar
Rumelhart, D. E. (1989). Toward a microstructural account of human reasoning. In Vosniadou, S. and Ortony, A. (Eds.), Similarity and Analogical Reasoning. New York, NY: Cambridge University Press.Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In Rumelhart, D. E. & McClelland, J. L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Vol 1. Foundations (pp. 318–362). Cambridge, MA: Bradford Books/MIT Press.Google Scholar
Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: alternative algorithms for category learning. Psychological Review, 117(4), 1144–1167.Google Scholar
Sanders, C. A., & Nosofsky, R. M. (2020). Training deep networks to construct a psychological feature space for a natural-object category domain. Computational Brain & Behavior, 2020, 1–23.Google Scholar
Shanks, D. R. (1991). Categorization by a connectionist network. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17(3), 433–443.Google Scholar
Schwenk, H. (1998). The diabolo classifier. Neural Computation, 10(8), 2175–2200.Google Scholar
Schyns, P. G., Goldstone, R. L. & Thibaut, J. (1998). The development of features in object concepts. Behavioral and Brain Sciences, 21, 1–54.Google Scholar
Shepard, R. N. (1957). Stimulus and response generalization: a stochastic model relating generalization to distance in psychological space. Psychometrika, 22, 325–345.Google Scholar
Shepard, R. N. (1962). The analysis of proximities: multidimensional scaling with an unknown distance function. I. Psychometrika, 27(2), 125–140.Google Scholar
Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237(4820), 1317–1323.CrossRefGoogle Scholar
Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75(13), 1–42.Google Scholar
Smith, E. E., & Medin, D. L. (1981). Categories and Concepts. Cambridge, MA: Harvard University Press.Google Scholar
Solomon, K. O., Medin, D. L. & Lynch, E. (1999). Concepts do more than categorize. Trends in Cognitive Sciences, 3, 99–104.Google Scholar
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: statistics, structure, and abstraction. Science, 331(6022), 1279–1285.Google Scholar
Vanpaemel, W., & Lee, M. D. (2012). The Bayesian evaluation of categorization models: comment on Wills and Pothos (2012). Psychological Bulletin, 138(6), 1253–1258.Google Scholar
Vanpaemel, W., & Storms, G. (2008). In search of abstraction: the varying abstraction model of categorization. Psychonomic Bulletin & Review, 15(4), 732–749.Google Scholar
Vigo, R. (2009). Categorical invariance and structural complexity in human concept learning. Journal of Mathematical Psychology, 53(4), 203–221.Google Scholar
Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits (No. TR-1553-1). Stanford, CA: Stanford Electronics Labs.CrossRefGoogle Scholar
Wills, A. J. & Pothos, E. M. (2012). On the adequacy of current empirical evaluations of formal models of categorization. Psychological Bulletin, 138, 102–125.Google Scholar
Yang, L., & Lewandowsky, S. (2004). Knowledge partitioning in categorization: constraints on exemplar models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 1045–1064.Google Scholar
Zeithamova, D., Mack, M. L., Braunlich, K., et al. (2019). Brain mechanisms of concept learning. Journal of Neuroscience, 39(42), 8259–8266.Google Scholar

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