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Memory & Cognition

, Volume 42, Issue 3, pp 508–524 | Cite as

Procedural memory effects in categorization: Evidence for multiple systems or task complexity?

  • Safa R. Zaki
  • Dave F. Kleinschmidt
Article

Abstract

According to an influential multiple-systems model of category learning, an implicit procedural system governs the learning of information-integration category structures, whereas a rule-based system governs the learning of explicit rule-based categories. Support for this idea has come in part from demonstrations that motor interference, in the form of inconsistent mapping between response location and category labels, results in observed deficits, but only for learning information-integration category structures. In this article, we argue that this response location manipulation results in a potentially more cognitively complex task in which the feedback is difficult to interpret. In one experiment, we attempted to attenuate the cognitive complexity by providing more information in the feedback, and demonstrated that this eliminates the observed performance deficit for information-integration category structures. In a second experiment, we demonstrated similar interference of the inconsistent mapping manipulation in a rule-based category structure. We claim that task complexity, and not separate systems, might be the source of the original dissociation between performance on rule-based and information-integration tasks.

Keywords

Categorization 

Notes

Author note

We thank Greg Ashby, Ben Newell, and two anonymous reviewers for helpful comments on previous versions of this article. We also thank Si Young Mah for her help running Experiment 2.

References

  1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, AC-19, 716–723. doi: 10.1109/TAC.1974.110070 CrossRefGoogle Scholar
  2. Alfonso-Reese, L. A., Ashby, F. G., & Brainard, D. H. (2002). What makes a categorization task difficult? Perception & Psychophysics, 64, 570–583. doi: 10.3758/BF03194727 CrossRefGoogle Scholar
  3. 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. doi: 10.1037/0033-295X.105.3.442 PubMedCrossRefGoogle Scholar
  4. Ashby, F. G., Ell, S. W., & Waldron, E. M. (2003). Procedural learning in perceptual categorization. Memory & Cognition, 31, 1114–1125. doi: 10.3758/BF03196132 CrossRefGoogle Scholar
  5. 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. doi: 10.1037/0278-7393.14.1.33 PubMedGoogle Scholar
  6. 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 PubMedCrossRefGoogle Scholar
  7. Ashby, F. G., & O’Brien, J. B. (2005). Category learning and multiple memory systems. Trends in Cognitive Sciences, 9, 83–89. doi: 10.1016/j.tics.2004.12.003 PubMedCrossRefGoogle Scholar
  8. Ell, S. W., & Ashby, F. G. (2006). The effects of category overlap on information-integration and rule-based category learning. Perception & Psychophysics, 68, 1013–1026. doi: 10.3758/BF03193362 CrossRefGoogle Scholar
  9. Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107–140. doi: 10.1037/0096-3445.127.2.107 CrossRefGoogle Scholar
  10. Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457–472. doi: 10.1214/ss/1177011136 CrossRefGoogle Scholar
  11. Gureckis, T. M., James, T. W., & Nosofsky, R. M. (2011). Re-evaluating dissociations between implicit and explicit category learning: An event-related fMRI study. Journal of Cognitive Neuroscience, 23, 1697–1709. doi: 10.1162/jocn.2010.21538 PubMedCrossRefGoogle Scholar
  12. 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, 418–439. doi: 10.1037/0278-7393.7.6.418 Google Scholar
  13. Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142, 573–603. doi: 10.1037/a0029146 CrossRefGoogle Scholar
  14. Maddox, W. T., & Ashby, F. G. (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 66, 309–332. doi: 10.1016/j.beproc.2004.03.011 PubMedCrossRefGoogle Scholar
  15. Maddox, W. T., Ashby, F. G., & Bohil, C. J. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 650–662. doi: 10.1037/0278-7393.29.4.650 PubMedGoogle Scholar
  16. Maddox, W. T., Ashby, F. G., Ing, A. D., & Pickering, A. D. (2004a). Disrupting feedback processing interferes with rule-based but not information-integration category learning. Memory & Cognition, 32, 582–591. doi: 10.3758/BF03195849 CrossRefGoogle Scholar
  17. Maddox, W. T., Bohil, C. J., & Ing, A. D. (2004b). Evidence for a procedural-learning-based system in perceptual category learning. Psychonomic Bulletin & Review, 11, 945–952. doi: 10.3758/BF03196726 CrossRefGoogle Scholar
  18. Maddox, W. T., Glass, B. D., O’Brien, J. B., Filoteo, J. V., & Ashby, F. G. (2010). Category label and response location shifts in category learning. Psychological Research, 74, 219–236.PubMedCentralPubMedCrossRefGoogle Scholar
  19. Maddox, W. T., Ing, A. D., & Lauritzen, J. S. (2006). Stimulus modality interacts with category structure in perceptual category learning. Perception & Psychophysics, 68, 1176–1190.CrossRefGoogle Scholar
  20. Maddox, W. T., Lauritzen, J. S., & Ing, A. D. (2007). Cognitive complexity effects in perceptual classification are dissociable. Memory & Cognition, 35, 885–894. doi: 10.3758/BF03193463 CrossRefGoogle Scholar
  21. Newell, B. R., & Dunn, J. C. (2008). Dimensions in data: Testing psychological models using state-trace analysis. Trends in Cognitive Sciences, 12, 285–290. doi: 10.1016/j.tics.2008.04.009 PubMedCrossRefGoogle Scholar
  22. Newell, B. R., Dunn, J. C., & Kalish, M. (2010). The dimensionality of perceptual category learning: A state-trace analysis. Memory & Cognition, 38, 563–581. doi: 10.3758/MC.38.5.563 CrossRefGoogle Scholar
  23. Newell, B. R., Dunn, J. C., & Kalish, M. (2011). Systems of category learning: Fact or fantasy? In B. H. Ross (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 54, pp. 167–215). Orlando, FL: Academic Press. doi: 10.1016/B978-0-12-385527-5.00006-1 Google Scholar
  24. Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104, 266–300.PubMedCrossRefGoogle Scholar
  25. Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 53–79. doi: 10.1037/0033-295x.101.1.53 PubMedCrossRefGoogle Scholar
  26. Nosofsky, R. M., Stanton, R. D., & Zaki, S. R. (2005). Procedural interference in perceptual classification: Implicit learning or cognitive complexity? Memory & Cognition, 33, 1256–1271.CrossRefGoogle Scholar
  27. Nosofsky, R. M., & Zaki, S. R. (1999). Math modeling, neuropsychology, and category learning: Response to B. Knowlton (1999). Trends in Cognitive Sciences, 3, 125–126. doi: 10.1016/S1364-6613(99)01291-7 PubMedCrossRefGoogle Scholar
  28. Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Vienna, Austria: Author. Retrieved from http://mcmc-jags.sourceforge.net
  29. Shanks, D. R., & St. John, M. F. (1994). Characteristics of dissociable human learning systems. Behavioral and Brain Sciences, 17, 367–447.CrossRefGoogle Scholar
  30. Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75, 1–42. doi: 10.1037/h0093825 CrossRefGoogle Scholar
  31. Spiering, B. J., & Ashby, F. G. (2008). Response processes in information-integration category learning. Neurobiology of Learning and Memory, 90, 330–338. doi: 10.1016/j.nlm.2008.04.015 PubMedCentralPubMedCrossRefGoogle Scholar
  32. Stanton, R. D., & Nosofsky, R. M. (2007). Feedback interference and dissociations of classification: Evidence against the multiple-learning-systems hypothesis. Memory & Cognition, 35, 1747–1758. doi: 10.3758/BF03193507 CrossRefGoogle Scholar
  33. Van Orden, G. C., Pennington, B. F., & Stone, G. O. (1990). Word identification in reading and the promise of subsymbolic psycholinguistics. Psychological Review, 97, 488–522. doi: 10.1037/0033-295x.97.4.488 PubMedCrossRefGoogle Scholar
  34. Waldron, E. M., & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: Evidence for multiple category learning systems. Psychonomic Bulletin & Review, 8, 168–176.CrossRefGoogle Scholar
  35. Willingham, D. B. (1998). A neuropsychological theory of motor skill learning. Psychological Review, 105, 558–584. doi: 10.1037/0033-295x.105.3.558 PubMedCrossRefGoogle Scholar
  36. Willingham, D. B. (1999). The neural basis of motor-skill learning. Current Directions in Psychological Science, 8, 178–182. doi: 10.1111/1467-8721.00042 CrossRefGoogle Scholar
  37. Zaki, S. R. (2004). Is categorization performance really intact in amnesia? A meta-analysis. Psychonomic Bulletin & Review, 11, 1048–1054. doi: 10.3758/BF03196735 CrossRefGoogle Scholar
  38. Zaki, S. R., & Nosofsky, R. M. (2001). A single-system interpretation of dissociations between recognition and categorization in a task involving object-like stimuli. Cognitive, Affective, & Behavioral Neuroscience, 1, 344–359. doi: 10.3758/CABN.1.4.344 CrossRefGoogle Scholar
  39. Zeithamova, D., & Maddox, W. T. (2006). Dual-task interference in perceptual category learning. Memory & Cognition, 34, 387–398. doi: 10.3758/BF03193416 CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2013

Authors and Affiliations

  1. 1.Williams CollegeWilliamstownUSA
  2. 2.Department of Psychology, Bronfman Science CenterWilliams CollegeWilliamstownUSA
  3. 3.Department of Brain and Cognitive SciencesUniversity of RochesterRochesterUSA

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