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.
This is a preview of subscription content, log in to check access.
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.
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:10.1037/0278-73126.96.36.199PubMedGoogle Scholar
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/BF03193362CrossRefGoogle Scholar
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.2010.21538PubMedCrossRefGoogle 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, 418–439. doi:10.1037/0278-73188.8.131.528Google Scholar
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:10.1037/0278-73184.108.40.2060PubMedGoogle Scholar
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/BF03195849CrossRefGoogle Scholar
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/BF03196726CrossRefGoogle Scholar
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
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
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/BF03193463CrossRefGoogle Scholar
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/B978-0-12-385527-5.00006-1Google Scholar
Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104, 266–300.PubMedCrossRefGoogle Scholar
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
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
Shanks, D. R., & St. John, M. F. (1994). Characteristics of dissociable human learning systems. Behavioral and Brain Sciences, 17, 367–447.CrossRefGoogle Scholar
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/h0093825CrossRefGoogle Scholar
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/BF03193507CrossRefGoogle Scholar
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
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.1.4.344CrossRefGoogle Scholar