Skip to main content
Log in

Exemplar-based accounts of “multiple-system” phenomena in perceptual categorization

  • Published:
Psychonomic Bulletin & Review Aims and scope Submit manuscript

Abstract

We demonstrate that a wide variety of recently reported “rule-described” and “prototype-described” phenomena in perceptual classification, which have led to the development of a number of multiplesystem models, can be given an alternative interpretation in terms of a single-system exemplar-similarity model. The phenomena include various rule- and prototype-described patterns of generalization, dissociations between categorization and similarity judgments, and dissociations between categorization and old-new recognition. The alternative exemplar-based interpretation relies on the idea that similarity is not an invariant relation but a context-dependent one. Similarity relations among exemplars change systematically because of selective attention to dimensions and because of changes in the level of sensitivity relating judged similarity to distance in psychological space. Adaptive learning principles may help explain the systematic influence of the selective attention process and of modulation in sensitivity settings on judged similarity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98, 409–429.

    Article  Google Scholar 

  • Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Erlbaum.

    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, 442–481.

    Article  PubMed  Google Scholar 

  • Ashby, F. G., & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37, 372–400.

    Article  Google Scholar 

  • Ashby, F. G., & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological Review, 93, 154–179.

    Article  PubMed  Google Scholar 

  • Barsalou, L. W. (1985). Ideals, central tendency, and frequency of instantiation as determinants of graded structure in categories. Journal of Experimental Psychology: Learning, Memory, & Cognition, 11, 629–654.

    Google Scholar 

  • Brooks, L. R. (1978). Nonanalytic concept formation and memory for instances. In E. Rosch & B. B. Lloyd (Eds.), Cognition and categorization (pp. 169–211). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Busemeyer, J. R., Dewey, G. I., & Medin, D. L. (1984). Evaluation of exemplar-based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Learning, Memory, & Cognition, 10, 638–648.

    Google Scholar 

  • Carey, S. (1985). Conceptual change in childhood. Cambridge, MA: MIT Press.

    Google Scholar 

  • Carroll, J.D., & Wish, M. (1974). Models and methods for three-way multidimensional scaling. In D. H. Krantz, R. C. Atkinson, R. D. Luce, & P. Suppes (Eds.), Contemporary developments in mathematical psychology (Vol. 2, pp. 57–105). San Francisco: W. H. Freeman.

    Google Scholar 

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

    Article  Google Scholar 

  • Estes, W. K. (1986). Array models for category learning. Cognitive Psychology, 18, 500–549.

    Article  PubMed  Google Scholar 

  • Estes, W. K. (1994). Classification and cognition. New York: Oxford University Press.

    Book  Google Scholar 

  • Garner, W. R. (1974). The processing of information and structure. New York: Wiley.

    Google Scholar 

  • Gelman, S. A., & Markman, E. M. (1986). Categories and induction in young children. Cognition, 23, 183–209.

    Article  PubMed  Google Scholar 

  • Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.

    Article  Google Scholar 

  • Getty, D. J., Swets, I. A., Swets, J. B., & Green, D. M. (1979). On the prediction of confusion matrices from similarity judgments. Perception & Psychophysics, 26, 1–19.

    Article  Google Scholar 

  • Gibson, J. J., & Gibson, E. J. (1955). Perceptual learning: Differentiation or enrichment? Psychological Review, 62, 32–41.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Goldstone, R. L. (1994b ). Similarity, interactive interaction, and mapping. Journal of Experimental Psychology: Learning, Memory, & Cognition, 20, 3–28.

    Google Scholar 

  • Grunwald, P. (2000). Model selection based on minimum description length. Journal of Mathematical Psychology, 44, 133–152.

    Article  PubMed  Google Scholar 

  • Heit, E. (1994). Models of the effects of prior knowledge on category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 20, 1264–1282.

    Google Scholar 

  • Heit, E. (1997). Knowledge and concept learning. In K. Lamberts & D. Shanks (Eds.), Knowledge, concepts, and categories (pp. 7–41). Cambridge, MA: MIT Press.

    Google Scholar 

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

    Article  Google Scholar 

  • Hintzman, D. L. (1988). Judgments of frequency and recognition memory in a multiple-trace memory model. Psychological Review, 95, 528–551.

    Article  Google Scholar 

  • Homa, D. (1984). On the nature of categories. Psychology of Learning & Motivation, 18, 49–94.

    Article  Google Scholar 

  • Keil, F. C. (1989). Concepts, kinds, and cognitive development. Cambridge, MA: MIT Press.

    Google Scholar 

  • Knowlton, B. J., Mangels, J. A., & Squire, L. R. (1996). A neostriata! habit learning system in humans. Science, 273, 1399–1402.

    Article  PubMed  Google Scholar 

  • Knowlton, B. I., & Squire, L. R. (1993). The learning of categories: Parallel brain systems for item memory and category knowledge. Science, 262, 1747–1749.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Lamberts, K. (1994). Flexible tuning of similarity in exemplar-based categorization. Journal of Experimental Psychology: Learning, Memory, & Cognition, 20, 1003–1021.

    Google Scholar 

  • Levine, M. (1975). A cognitive theory of learning: Research on hypothesis testing. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Lewandowsky, S. (1995). Base-rate neglect in ALCOVE: A critical reevaluation. Psychological Review, 102, 185–191.

    Article  Google Scholar 

  • Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492–527.

    Article  Google Scholar 

  • Lovett, M. (1998). Choice. In I. R. Anderson & C. Lebiere (Eds.), The atomic components of thought (pp. 255–296). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Luce, R. D. (1963). Detection and recognition. In R. D. Luce, R. R. Bush, & E. Galanter (Eds.), Handbook of mathematical psychology (pp. 103–189). New York: Wiley.

    Google Scholar 

  • Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of classification. Perception & Psychophysics, 53, 49–70.

    Article  Google Scholar 

  • McKinley, S. C., & Nosofsky, R. M. (1995). Investigations of exemplar and decision-bound models in large-size, ill-defined category structures. Journal of Experimental Psychology: Human Perception & Performance, 21, 128–148.

    Google Scholar 

  • McKinley, S. C., & Nosofsky, R. M. (1996). Selective attention.and the formation of linear decision boundaries. Journal of Experimental Psychology: Human Perception & Performance, 22, 294–317.

    Google Scholar 

  • Medin, D. L., Goldstone, R. L., & Gentner, D. (1990). Similarity involving attributes and relations: Judgments of similarity and differences are not inverses. Psychological Science, I, 64–69.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 5, 207–238.

    Article  Google Scholar 

  • Medin, D. L., & Smith, E. E. (1981). Strategies and classification learning. Journal of Experimental Psychology: Human Learning & Memory, 7, 241–253.

    Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Myung, I. J. (2000). The importance of complexity in model selection. Journal of Mathematical Psychology, 44, 190–204.

    Article  PubMed  Google Scholar 

  • Myung, I. J., & Pitt, M. A. (1997). Applying Occam's razor in modeling cognition: A Bayesian approach. Psychonomic Bulletin & Review, 4, 79–95.

    Article  Google Scholar 

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

    Google Scholar 

  • Nosofsky, R. M. (1985). Overall similarity and the identification of separable-dimension stimuli: A choice model analysis. Perception & Psychophysics, 38, 415–432.

    Article  Google Scholar 

  • Nosofsky, R. M. (1986). Attention, similarity, and the identification — categorization relationship. Journal of Experimental Psychology: General, ll5, 39–57.

    Article  Google Scholar 

  • Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, & Cognition, 13, 87–109.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Nosofsky, R. M. (1991b). Typicality in logically defined categories: Exemplar-similarity versus rule instantiation. Memory & Cognition, 19, 131–150.

    Article  Google Scholar 

  • Nosofsky, R. M. (1992). Similarity scaling and cognitive process models. Annual Review of Psychology, 43, 25–53.

    Article  Google Scholar 

  • Nosofsky, R. M. (1998a). Optimal performance and exemplar models of classification. In M. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 218–247). Oxford: Oxford University Press.

    Google Scholar 

  • Nosofsky, R. M. (1998b). Selective attention and the formation of linear decision boundaries: Reply to Maddox and Ashby (1998). Journal of Experimental Psychology: Human Perception & Peiformance, 24, 322–339.

    Google Scholar 

  • Nosofsky, R. M., Clark, S. E., & Shin, H. J. (1989). Rules and exemplars in categorization, identification, and recognition. Journal of Experimental Psychology: Learning, Memory, & Cognition, 15, 282–304.

    Google Scholar 

  • Nosofsky, R. M., Gluck, M. A., 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.

    Article  Google Scholar 

  • Nosofsky, R. M., Kruschke, J. K., & McKinley, S.C. (1992). Combining exemplar-based category representations and connectionist learning rules. Journal of Experimental Psychology: Learning, Memory, & Cognition, 18, 211–233.

    Google Scholar 

  • Nosofsky, R. M., & Palmeri, T. J. (1996). Learning to classify integraldimension stimuli. Psychonomic Bulletin & Review, 3, 222–226.

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Nosofsky, R. M., Palmeri, T. J., & McKinley, S.C. (1994). Rule-plusexception model of classification learning. Psychological Review, 101, 53–79.

    Article  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Palmeri, T. J. (1997). Exemplar similarity and the development of automaticity. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23, 324–354.

    Google Scholar 

  • Palmeri, T. J., & Nosofsky, R. M. (1995). Recognition memory for exceptions to the category rule. Journal of Experimental Psychology: Learning, Memory, & Cognition, 21, 548–568.

    Google Scholar 

  • Pavel, M., Gluck, M. A., & Henkle, V. (1988). Generalization by humans and multi-layer networks. In Proceedings of the Tenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Posner, M.I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 11, 353–363.

    Article  Google Scholar 

  • Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 382–407.

    Article  Google Scholar 

  • Rips, L. J. (1989). Similarity, typicality, and categorization. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 21–59). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Rips, L. J., & Collins, A. (1993). Categories and resemblance. Journal of Experimental Psychology: General, 122, 468–486.

    Article  Google Scholar 

  • Ross, B. H. (1987). This is like that: The use of earlier problems and the separation of similarity effects. Journal of Experimental Psychology: Learning, Memory, & Cognition, 13, 629–639.

    Google Scholar 

  • Shepard, R.N. (1958). Stimulus and response generalization: Tests of a model relating generalization to distance in psychological space. Journal of Experimental Psychology, 55, 509–523.

    Article  PubMed  Google Scholar 

  • Shepard, R.N. (1964). Attention and the metric structure of the stimulus space. Journal of Mathematical Psychology, 1, 54–87.

    Article  Google Scholar 

  • Shepard, R. N. (1986). Discrimination and generalization in identification and classification: Comment on Nosofsky. Journal of Experimental Psychology: General, 115, 58–61.

    Article  Google Scholar 

  • Shepard, R.N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.

    Article  PubMed  Google Scholar 

  • Shepard, R.N., & Chang, J. J. (1963). Stimulus generalization in the learning of classifications. Journal of Experimental Psychology, 65, 94–102.

    Article  PubMed  Google Scholar 

  • Shepard, R.N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs, 15 (13, Whole No. 517).

  • Shin, H. J., & Nosofsky, R. M. (1992). Similarity scaling studies of dot pattern classification and recognition. Journal of Experimental Psychology: General, 121, 278–304.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Smith, E. E., & Sloman, S. A. (1994). Similarity- versus rule-based categorization. Memory & Cognition, 22, 377–386.

    Article  Google Scholar 

  • Smith, J.D., & Minda, J.P. (1998). Prototypes in the mist: The early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 24, 1411–1436.

    Google Scholar 

  • Smith, J.D., Murray, M. I., & Minda, J.P. (1997). Straight talk about linear separability. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23, 659–680.

    Google Scholar 

  • Smith, L. B. (1989). A model of perceptual classification in children and adults. Psychological Review, 96, 125–144.

    Article  PubMed  Google Scholar 

  • Squire, L. R., & Knowlton, B. J. (1995). Learning about categories in the absence of memory. Proceedings of the National Academy of Sciences, 92, 12470–12474.

    Article  Google Scholar 

  • Trabasso, T., & Bower, G. H. (1968). Attention in learning: Theory and research. New York: Wiley.

    Google Scholar 

  • Vandierendonck, A. (1995). A parallel rule activation and rule synthesis model for generalization in category learning. Psychonomic Bulletin & Review, 2, 442–459.

    Article  Google Scholar 

  • Wickens, T. D. (1982). Models for behavior: Stochastic processes in psychology. San Francisco: W. H. Freeman.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Robert M. Nosofsky.

Additional information

This work was supported by Grant ROI MH48494-09 from the National Institute of Mental Health. We thank F. Gregory Ashby, John Wixted, and two anonymous reviewers for their helpful criticisms of earlier versions of this article. We are also indebted to Michael Erickson for providing us with the original program code used to conduct Experiment I from Erickson and Kruschke ( 1998), and to J. David Smith for providing us with the individual subject data from J. D. Smith, Murray, and Minda (1997).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nosofsky, R.M., Johansen, M.K. Exemplar-based accounts of “multiple-system” phenomena in perceptual categorization. Psychon Bull Rev 7, 375–402 (2000). https://doi.org/10.1007/BF03543066

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03543066

Navigation