... 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, 3991), the configural-cue model (cluck & Bower, 1988b), and an extended version of the conf`igural-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 same form of selective attention todimensions in category-learning models based on stimulus generalization and cue conditioning.