Cue Selection and Category Learning: A Systematic Comparison of Three Theories1

We evaluate three approaches to tasks involving categorisation with probabilistic cues (the Bayesian approach, the associationist approach, and the hypothesis testing approach) by comparing the behaviour of three classes of cognitive model with that of human participants on a simulated medical diagnosis task. The task yields dependent measures relating to both categorisation accuracy and cue selection. A systematic exploration of the effects of processing biases within the models reveals that all three approaches are able to account for the effects in the human data, provided that appropriate performance factors and processing biases are incorporated. The discussion focuses on the methodology used to evaluate the approaches and on the role of performance factors and processing biases within the various models.