Attention, Perception, & Psychophysics

, Volume 79, Issue 6, pp 1777–1794 | Cite as

The impact of category structure and training methodology on learning and generalizing within-category representations

  • Shawn W. Ell
  • David B. Smith
  • Gabriela Peralta
  • Sébastien Hélie


When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.


Knowledge representation Training methodology Generalization Category learning 



This work was supported by the National Science Foundation under Grants #1349677-BCS and #1349737-BCS to S.H. and S.W.E., respectively. The authors would like to thank Alex Groat, Mikael Heikkinen, Steve Hutchinson, Erin Perry, Nicole Rubin, and Lauren Szymula for their help with data collection.


  1. Anderson, J. R., & Fincham, J. M. (1996). Categorization and sensitivity to correlation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 259–277.PubMedGoogle Scholar
  2. Ashby, F. G. (1992a). Multidimensional models of categorization. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition (pp. 449–483). Hillsdale, NJ: Erlbaum.Google Scholar
  3. Ashby, F. G. (1992b). Multivariate probability distributions. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition (pp. 1–34). Hillsdale, NJ: Erlbaum.Google Scholar
  4. 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.CrossRefPubMedGoogle Scholar
  5. Ashby, F. G., & Crossley, M. J. (2011). A Computational model of how cholinergic interneurons protect striatal-dependent learning. Journal of Cognitive Neuroscience, 23, 1549–1566. doi: 10.1162/jocn.2010.21523 CrossRefPubMedGoogle Scholar
  6. Ashby, F. G., & Ell, S. W. (2001). The neurobiology of human category learning. Trends in Cognitive Science, 5, 204–210.CrossRefGoogle Scholar
  7. Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 33–53.PubMedGoogle Scholar
  8. Ashby, F. G., & Lee, W. W. (1993). Perceptual variability as a fundamental axiom of perceptual science. In S. C. Masin (Ed.), Foundations of percpetual theory (pp. 369–399). Amsterdam: Elsevier.CrossRefGoogle Scholar
  9. Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178. doi: 10.1146/annurev.psych.56.091103.070217 CrossRefPubMedGoogle Scholar
  10. Ashby, F. G., Queller, S., & Berretty, P. M. (1999). On the dominance of unidimensional rules in unsupervised categorization. Perception & Psychophysics, 61, 1178–1199.CrossRefGoogle Scholar
  11. Ashby, F. G., & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological Review, 93, 154–179.CrossRefPubMedGoogle Scholar
  12. Ashby, F. G., & Waldron, E. M. (1999). The nature of implicit categorization. Psychonomic Bulletin & Review, 6, 363–378.CrossRefGoogle Scholar
  13. Ashby, F. G., Waldron, E. M., Lee, W. W., & Berkman, A. (2001). Suboptimality in human categorization and identification. Journal of Experimental Psychology: General, 130, 77–96.CrossRefGoogle Scholar
  14. Brainard, D. H. (1997). Psychophysics software for use with MATLAB. Spatial Vision, 10, 433–436.CrossRefPubMedGoogle Scholar
  15. Carvalho, P. F., & Goldstone, R. L. (2014). Putting category learning in order: Category structure and temporal arrangement affect the benefit of interleaved over blocked study. Memory & Cognition, 42, 481–495. doi: 10.3758/s13421-013-0371-0 CrossRefGoogle Scholar
  16. Carvalho, P. F., & Goldstone, R. L. (2015). The benefits of interleaved and blocked study: Different tasks benefit from different schedules of study. Psychonomic Bulletin & Review, 22, 281–288. doi: 10.3758/s13423-014-0676-4 CrossRefGoogle Scholar
  17. Casale, M. B., & Ashby, F. G. (2008). A role for the perceptual representation memory system in category learning. Perception & Psychophysics, 70, 983–999.CrossRefGoogle Scholar
  18. Casale, M. B., Roeder, J. L., & Ashby, F. G. (2012). Analogical transfer in perceptual categorization. Memory & Cognition, 40, 434–449.CrossRefGoogle Scholar
  19. Chin-Parker, S., & Ross, B. H. (2002). The effect of category learning on sensitivity to within-category correlations. Memory & Cognition, 30, 353–362.CrossRefGoogle Scholar
  20. Chin-Parker, S., & Ross, B. H. (2004). Diagnosticity and prototypicality in category learning: A comparison of inference learning and classification learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 30, 216–226. doi: 10.1037/0278-7393.30.1.216 Google Scholar
  21. Ell, S. W., & Ashby, F. G. (2006). The effects of category overlap on information-integration and rule-based category learning. Perception and Psychophysics, 68, 1013–1026.CrossRefPubMedGoogle Scholar
  22. Ell, S. W., & Ashby, F. G. (2012). The impact of category separation on unsupervised categorization. Attention, Perception, & Psychophysics, 74, 466–475.CrossRefGoogle Scholar
  23. Ell, S. W., Ashby, F. G., & Hutchinson, S. (2012). Unsupervised category learning with integral-dimension stimuli. Quarterly Journal of Experimental Psychology, 65, 1537–1562.CrossRefGoogle Scholar
  24. Ell, S. W., Ing, A. D., & Maddox, W. T. (2009). Criterial noise effects on rule-based category learning: The impact of delayed feedback. Attention, Perception, & Psychophysics, 71, 1263–1275.CrossRefGoogle Scholar
  25. Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140.CrossRefGoogle Scholar
  26. Goldstone, R. L. (1996). Isolated and interrelated concepts. Memory & Cognition, 24, 608–628.CrossRefGoogle Scholar
  27. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley.Google Scholar
  28. Hammer, R., Diesendruck, G., Weinshall, D., & Hochstein, S. (2009). The development of category learning strategies: what makes the difference? Cognition, 112, 105–119. doi: 10.1016/j.cognition.2009.03.012 CrossRefPubMedGoogle Scholar
  29. Hélie, S., Shamloo, F., & Ell, S. W. (2017). The effect of training methodology on kowledge representation in perceptual categorization. Manuscript submitted for publication.Google Scholar
  30. Hoffman, A. B., & Rehder, B. (2010). The costs of supervised classification: The effect of learning task on conceptual flexibility. Journal of Experimental Psychology: General, 139, 319–340. doi: 10.1037/a0019042 CrossRefGoogle Scholar
  31. Kleiner, M., Brainard, D., Pelli, D., Ingling, A., Murray, R., & Broussard, C. (2007). What’s new in Psychtoolbox-3? Perception, 36 (ECVP Abstract Supplement).Google Scholar
  32. Levering, K. R., & Kurtz, K. J. (2015). Observation versus classification in supervised category learning. Memory & Cognition, 43, 266–282. doi: 10.3758/s13421-014-0458-2 CrossRefGoogle Scholar
  33. Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53, 49–70.CrossRefGoogle Scholar
  34. Maddox, W. T., & Ashby, F. G. (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioral Processes, 66, 309–332.CrossRefGoogle Scholar
  35. Maddox, W. T., & Bogdanov, S. V. (2000). On the relation between decision rules and perceptual representation in multidimensional perceptual categorization. Perception & Psychophysics, 62(5), 984–997.CrossRefGoogle Scholar
  36. Maddox, W. T., Bohil, C. J., & Ing, A. D. (2004). Evidence for a procedural learning-based system in category learning. Psychonomic Bulletin & Review, 11, 945–952.CrossRefGoogle Scholar
  37. Maddox, W. T., Filoteo, J. V., Hejl, K. D., & Ing, A. D. (2004). Category number impacts rule-based but not information-integration category learning: Further evidence for dissociable category learning systems. Journal of Experimental Psychology: Learning, Memory, & Cognition, 30, 227–235.Google Scholar
  38. Maddox, W. T., Filoteo, J. V., Lauritzen, J. S., Connally, E., & Hejl, K. D. (2005). Discontinuous categories affect information-integration but not rule-based category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 31, 654–669. doi: 10.1037/0278-7393.31.4.654 Google Scholar
  39. Markman, A. B., & Ross, B. (2003). Category use and category learning. Psychological Bulletin, 129, 529–613.CrossRefGoogle Scholar
  40. Medin, D. L., Wattenmaker, W. D., & Hampson, S. E. (1987). Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology, 19, 242–279.CrossRefPubMedGoogle Scholar
  41. Milton, F., & Wills, A. J. (2004). The influence of stimulus properties on category construction. Journal of Experimental Psychology: Learning, Memory, & Cognition, 30, 407–415. doi: 10.1037/0278-7393.30.2.407 Google Scholar
  42. Minda, J. P., & Ross, B. H. (2004). Learning categories by making predictions: An investigation of indirect category learning. Memory & Cognition, 32, 1355–1368.CrossRefGoogle Scholar
  43. Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 53–79.CrossRefPubMedGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437–442.CrossRefPubMedGoogle Scholar
  46. Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.CrossRefPubMedGoogle Scholar
  47. Pothos, E. M., & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.CrossRefGoogle Scholar
  48. Pothos, E. M., & Chater, N. (2005). Unsupervised categorization and category learning. Quarterly Journal of Experimental Psychology: A, 58, 733–752.CrossRefGoogle Scholar
  49. Pothos, E. M., & Close, J. (2008). One or two dimensions in spontaneous classification: A simplicity approach. Cognition, 107, 581–602. doi: 10.1016/j.cognition.2007.11.007 CrossRefPubMedGoogle Scholar
  50. Reber, P. J., Stark, C. E. L., & Squire, L. R. (1998). Cortical areas supporting category learning identified using functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 95, 747–750.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Roediger, H. L., Marsh, E. J., & Lee, S. C. (2002). Kinds of memory. In H. Pashler & D. L. Medin (Eds.), Stevens’ handbook of experimental psychology (Memory and cognitive processes 3rd ed., Vol. 2, pp. 1–41). New York: John Wiley & Sons.Google Scholar
  52. Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of natural categories. Cognitive Psychology, 7, 573–605.CrossRefGoogle Scholar
  53. Schmidt, R. A., & Bjork, R. A. (1992). New conceptualizations of practice: Common principles in three paradigms suggest new concepts for training. Psychological Science, 3, 207–217.CrossRefGoogle Scholar
  54. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.CrossRefGoogle Scholar
  55. Smith, J. D., Beran, M. J., Crossley, M. J., Boomer, J., & Ashby, F. G. (2010). Implicit and explicit category learning by macaques (Macaca mulatta) and humans (Homo sapiens). Journal of Experimental Psychology: Animal Behavior Processes, 36, 54–65. doi: 10.1037/a0015892 PubMedPubMedCentralGoogle Scholar
  56. 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
  57. Smith, J. D., & Minda, J. P. (2001). Journey to the center of the category: The dissociation in amnesia between categorization and recognition. Journal of Experimental Psychology: Learning, Memory, & Cognition, 27, 984–1002.Google Scholar
  58. 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.Google Scholar
  59. Thomas, R. D. (1998). Learning correlations in categorization tasks using large, ill-defined categories. Journal of Experimental Psychology: Learning, Memory, & Cognition, 24, 119–143.Google Scholar
  60. Wickens, T. D. (1982). Models for behavior: Stochastic processes in psychology. San Francisco, CA: W. H. Freeman.Google Scholar
  61. Yamauchi, T., & Markman, A. B. (1998). Category learning by inference and classification. Journal of Memory and Language, 39, 124–148.CrossRefGoogle Scholar
  62. Zeithamova, D., Maddox, W. T., & Schnyer, D. M. (2008). Dissociable prototype learning systems: Evidence from brain imaging and behavior. Journal of Neuroscience, 28, 13194–13201. doi: 10.1523/JNEUROSCI.2915-08.2008 CrossRefPubMedPubMedCentralGoogle Scholar
  63. Zotov, V., Jones, M. N., & Mewhort, D. J. (2011). Contrast and assimilation in categorization and exemplar production. Attention, Perception, & Psychophysics, 73, 621–639. doi: 10.3758/s13414-010-0036-z CrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2017

Authors and Affiliations

  • Shawn W. Ell
    • 1
  • David B. Smith
    • 2
  • Gabriela Peralta
    • 2
  • Sébastien Hélie
    • 3
  1. 1.Department of Psychology, Graduate School of Biomedical Sciences and EngineeringUniversity of MaineOronoUSA
  2. 2.Department of PsychologyUniversity of MaineOronoUSA
  3. 3.Department of Psychological SciencesPurdue UniversityWest LafayetteUSA

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