Abstract
The present study investigated whether the later learning of a category could affect the representation of other categories learned previously. Participants initially learned two or three categories, where each stimulus was composed of features that were distinctive to a category, shared with one or both of the other categories, or were idiosyncratic. When two categories were initially learned, a subsequent learning phase involved the learning of a third category that either shared distinctive features with categories learned previously, thereby discounting those features as diagnostic or was composed of features unrelated to the original categories. A common transfer test contained old, new, and prototype stimuli for classification, as well as critical items that revealed whether discounting of previously diagnostic features had occurred. The results revealed that stimuli assigned to a particular category in the two-category condition were assigned to the third category learned subsequently when the later learning discounted previously diagnostic features. These results suggest that later learning of a category can indirectly modify the representation of categories learned previously.
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Notes
Curly brackets are used to specify the features defining a stimulus. When the stimulus appeared in either learning or transfer, the order of the features was randomized on each occurrence for each participant.
The characterization of a stimulus as composed of diagnostic, common, and idiosyncratic features mirrors the descriptive theoretical model of Homa and Chambliss (1975), where each stimulus was assumed to contain idiosyncratic features that made each stimulus unique, common features that were found in multiple examples of potentially other categories, and discriminative features that were both common to a category and unique to that category. Variable manipulations of category size and number of categories to be learned were assumed to isolate common vs. distinctive features, respectively. In the present study, a similar description is used except that the term ‘diagnostic’ is preferred to ‘distinctive’ since ‘diagnostic’ better captures the critical status of a feature or features that survive, as unique and common to a category, following the later learning of a related category.
We would like to thank a reviewer for pointing out the similarity between our paradigm and the problem of ‘catastrophic interference’ that often arises when sequential, rather than concurrent, learning is modeled by distributed connectionist networks.
We found no apparent bias to assign transfer patterns to the C category for those conditions when category C was learned subsequently. In this analysis, error assignments for new patterns that unambiguously belonged to category A or B were erroneously assigned to the C category 51.3% of the time and erroneously to the other remaining category 48.7% of the time.
References
French, R. M. (1999). Catastrophic forgetting in neural networks. Trends in Cognitive Sciences, 3, 128–135.
Goldstone, R. L., & Steyvers, M. (2001). The sensitization and differentiation of dimensions during category learning. Journal of Experimental Psychology: General, 130, 116–139.
Homa, D. (1984). On the nature of categories. In G. H. Bower (Ed.), The psychology of learning and motivation, vol. 18. New York: Academic Press.
Homa, D., & Chambliss, D. (1975). The relative contributions of common and distinctive information on the abstraction from ill-defined categories. Journal of Experimental Psychology: Human Learning & Memory, 104(4), 351–359.
Homa, D., Proulx, M. J., & Blair, M. (2008). The modulating influence of category size on the classification of exception patterns. Quarterly Journal of Experimental Psychology, 61, 425–443.
McCloskey, M., & Cohen, N. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. In G. H. Bower (Ed.), The psychology of learning and motivation, vol 24. New York: Academic Press.
Nosofsky, R. M., & Bergert, F. B. (2007). Limitations of exemplar models of multi-attribute probabilistic inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 999–1019.
Nosofsky, R. M., & Zaki, S. R. (2002). Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 924–940.
Ratcliff, R. (1990). Connectionist model of recognition memory: Constraints imposed by learning and forgetting functions. Psychological Review, 97, 285–308.
Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.
Smith, J. D., & Minda, J. P. (1998). Prototypes in the midst: The early epochs of category learning. Journal of Experimental Psychology: Learning, Memory & Cognition, 24, 1411–1436.
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.
Spiering, B. J., & Ashby, F. G. (2008). Initial training with difficult items facilitates information integration, but not rule-based category learning. Psychological Science, 19, 1169–1177.
Tanaka, J. W., Curran, T., & Sheinberg, D. L. (2005). The training and transfer of real-world perceptual expertise. Psychological Science, 16, 145–151.
Zaki, S. R., Nosofsky, R. M., Stanton, R. D., & Cohen, A. L. (2003). Prototype and exemplar accounts of category learning and attentional allocation: A reassessment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1160–1173.
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Homa, D., Rogers, D. & Lancaster, M.E. The indirect modification of categorical knowledge. Psychon Bull Rev 22, 219–227 (2015). https://doi.org/10.3758/s13423-014-0662-x
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DOI: https://doi.org/10.3758/s13423-014-0662-x