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The effect of category variability in perceptual categorization

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Stewart, Neil, 1974- and Chater, Nick. (2002) The effect of category variability in perceptual categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol.28 (No.5). pp. 893-907. ISSN 0278-7393

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Official URL: http://dx.doi.org/10.1037/0278-7393.28.5.893

Abstract

Exemplar and distributional accounts of categorization make differing predictions for the classification of a critical exemplar precisely halfway between the nearest exemplars of 2 categories differing in variability. Under standard conditions of sequential presentation, the critical exemplar was classified into the most similar, least variable category, consistent with an exemplar account. However, if the difference in variability is made more salient, then the same exemplar is classified into the more variable, most likely category, consistent with a distributional account. This suggests that participants may be strategic in their use of either strategy. However, when the relative variability of 2 categories was manipulated, participants showed changes in the classification of intermediate exemplars that neither approach could account for.

Item Type:	Journal Article
Subjects:	B Philosophy. Psychology. Religion > BF Psychology
Divisions:	Faculty of Science > Psychology
Library of Congress Subject Headings (LCSH):	Categorization (Psychology), Decision making -- Testing, Pattern perception
Journal or Publication Title:	Journal of Experimental Psychology: Learning, Memory, and Cognition
Publisher:	American Psychological Association
ISSN:	0278-7393
Date:	September 2002
Volume:	Vol.28
Number:	No.5
Page Range:	pp. 893-907
Identification Number:	10.1037/0278-7393.28.5.893
Status:	Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Economic and Social Research Council (Great Britain) (ESRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), European Commission (EC)
Grant number:	R000239351 (ESRC), 88/S09589 (BBSRC), RTN-HPRN-CT-1999-00065 (EC)
References:	1. Altham, P. M. (1979). Detecting relationships between categorical variables over time: A problem of deflating a chi-squared statistic. Applied Statistics, 28, 115-125. 2. Ashby, F. G. (1992). Multidimensional models of categorization. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition (pp. 449-483). Hillsdale, NJ: Erlbaum. 3. Ashby, F. G. & Alfonso-Reese, L. A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216-233. 4. Ashby, F. G. & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Animal Behavior Processes, 14, 33-53. 5. Ashby, F. G. & Maddox, W. T. (1992). Complex decision rules in categorization: Contrasting novice and experienced performance. Journal of Experimental Psychology: Human Perception & Performance, 18, 50-71. 6. Ashby, F. G. & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision-bound models of categorization. Journal of Mathematical Psychology, 37, 372-400. 7. Ashby, F. G. & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological Review, 93, 154-179. 8. Ashby, F. G., Maddox, W. T. & Lee, W. W. (1994). On the dangers of averaging across subjects when using multidimensional -scaling or the similarity-choice model. Psychological Science, 5, 144-151. 9. Carroll, J. D. & Wish, M. (1974). Models and methods for three-way multidimensional scaling. Contemporary developments in mathematical psychology, 2, 57-105. 10. Feldman, J. & Richards, W. (1998). Mapping the mental space of rectangles. Perception, 27, 1191-1202. 11. Fried, L. S. & Holyoak, K. J. (1984). Induction of category distributions: A framework for classification learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 10, 234-257. 12. Fukunaga, K. (1972). Introduction to statistical pattern recognition. New York: Academic Press. 13. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley. 14. Homa, D., Sterling, S. & Trepel, L. (1981). Limitations of exemplar-based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Learning, Memory, & Cognition, 7, 418-439. 15. Howell, D. C. (1997). Statistical methods for psychology (4th ed.). Belmont, CA: Duxbury Press. 16. Kalish, M. L. & Kruschke, J. K. (1997). Decision boundaries in one-dimensional categorization. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23, 1362-1377. 17. Krantz, D. H. & Tversky, A. (1975). Similarity of rectangles: An analysis of subjective dimensions. Journal of Mathematical Psychology, 12, 4-34. 18. Luce, R. D. (1959). Individual choice behavior. New York: Wiley. 19. Macmillan, N. A. & Ornstein, A. S. (1998). The mean-integrality representation of rectangles. Perception & Psychophysics, 60, 250-262. 20. Maddox, W. T. (1999). On the dangers of averaging across observers when comparing decision bound models and generalized context models of categorization. Perception & Psychophysics, 61, 354-374. 21. McLaren, I. P. (1997). Categorization and perceptual learning: An analogue of the face inversion effect. Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 50, 257-273. 22. Medin, D. L. & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238. 23. Medin, D. L. & Schwanenflugel, P. J. (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning & Memory, 7, 355-368. 24. Monahan, J. S. & Lockhead, G. R. (1977). Identification of integral stimuli. Journal of Experimental Psychology: General, 106, 94-110. 25. Moody, J. & Darken, C. (1989). Fast learning in networks of locally-tuned processing units. Neural Computation, 1, 281-294. 26. Morrison, D. F. (1990). Multivariate statistical methods. (3rd ed.). New York: McGraw-Hill. 27. Nilsson, N. J. (1965). Learning machines. New York: McGraw-Hill. 28. Noreen, D. L. (1981). Optimal decision rules for some common psychophysical paradigms. In S. Grossberg (Ed.), Mathematical psychology and psychophysiology (pp. 237-279). Providence, RI: American Mathematical Society. 29. Nosofsky, R. M. (1986). Attention, similarity and the identification -categorization relationship. Journal of Experimental Psychology: General, 115, 39-57. 30. Nosofsky, R. M. (1990). Relations between exemplar-similarity and likelihood models of classification. Journal of Mathematical Psychology, 34, 393-418. 31. Palmeri, T. J. & Nosofsky, R. M. (2001). Central tendencies, extreme points, and prototype enhancement effects in ill-defined perceptual categorization. Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 54, 197-235. 32. Posner, M. I. & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353-363. 33. Posner, M. I. & Keele, S. W. (1970). Retention of abstract ideas. Journal of Experimental Psychology, 88, 304-308. 34. Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 382-407. 35. Rips, L. J. (1989). Similarity, typicality, and categorization. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 21-59). New York: Cambridge University Press. 36. Rosch, E. (1973). On the internal structure of perceptual and semantic categories. In T. E. Moore (Ed.), Cognitive development and the acquisition of language (pp. 111-144). New York: Academic Press. 37. Rosch, E., Simpson, C. & Miller, R. S. (1976). Structural base of typicality effects. Journal of Experimental Psychology: Human Perception & Performance, 2, 491-502. 38. Rosseel, Y. (1996). Connectionist models of categorization: A statistical interpretation. Psychologica Belgica, 36, 93-112. 39. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536. 40. Shepard, R. N. (1980). Multidimensional scaling, tree-fitting, and clustering. Science, 210, 390-398. 41. Smith, E. E. & Sloman, S. A. (1994). Similarity- versus rule-based categorization. Memory & Cognition, 22, 377-386. 42. Smith, J. D. & Minda, J. P. (2000). Thirty categorization results in search of a model. Journal of Experimental Psychology: Learning, Memory, & Cognition, 26, 3-27. 43. Stewart, N. (2001). Perceptual categorization. Unpublished doctoral dissertation, University of Warwick, England. 44. Stewart, N., Brown, G. D. & Chater, N. (2002). Sequence effects in categorization of simple perceptual stimuli. Journal of Experimental Psychology: Learning, Memory, & Cognition, 28, 3-11. 45. Tavare, S. & Altham, P. M. (1983). Serial dependence of observations leading to contingency tables, and corrections to chi-squared statistics. Biometrika, 70, 139-144. 46. Townsend, J. T. & Landon, D. E. (1983). Mathematical models of recognition and confusion in psychology. Mathematical Social Sciences, 4, 25-71. 47. Wills, A. J. & McLaren, I. P. (1998). Perceptual learning and free classification. Quarterly Journal of Experimental Psychology: Comparative and Physiological Psychology, 51, 235-270.
URI:	http://wrap.warwick.ac.uk/id/eprint/618