Abstract
We partially replicate and extend Shepard, Hovland, and Jenkins's (1961) classic study of task difficulty for learning six fundamental types of rule-based categorization problems. Our main results mirrored those of Shepard et al., with the ordering of task difficulty being the same as in the original study. A much richer data set was collected, however, which enabled the generation of block-by-block learning curves suitable for quantitative fitting. Four current computational models of classification learning were fitted to the learning data: ALCOVE (Kruschke, 1992), the rational model (Anderson, 1991), the configural-cue model (Gluck & Bower, 1988b), and an extended version of the configural-cue model with dimensionalized, adaptive learning rate mechanisms. Although all of the models captured important qualitative aspects of the learning data, ALCOVE provided the best overall quantitative fit. The results suggest the need to incorporate some form of selective attention to dimensions in category-learning models based on stimulus generalization and cue conditioning.
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This work was supported by Grant PHS R01 MH48494-01 from the National Institute of Mental Health to R.M.N., an ONR Young Investigator Award to M A G., and Grant N00014-88-J-0112 from the ONR Biological Intelligence Initiative to M AG.
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Nosofsky, R.M., Gluck, M.A., Palmeri, T.J. et al. Comparing modes of rule-based classification learning: A replication and extension of Shepard, Hovland, and Jenkins (1961). Mem Cogn 22, 352–369 (1994). https://doi.org/10.3758/BF03200862
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DOI: https://doi.org/10.3758/BF03200862