Connectionist Accounts of the Inverse Base-rate Effect in Categorization

Abstract

Two experiments replicated the ‘inverse base-rate effect’ reported in categorization studies by Medin & Edelson. In Experiment I subjects were presented with the case histories of hypothetical medical patients and had to diagnose which illness they thought each patient was suffering from on the basis of the symptoms they had. On some trials (AB-→1), patients had two symptoms, A and B, and the correct diagnosis was disease 1. On other trials (AC-→2)patients had symptoms A and C and the correct diagnosis was disease 2. Feedback was provided on each trial about the correct diagnosis. Symptom A was common to both diseases, but subjects saw more AB-→1 than AC-→2 trials. On subsequent test trials subjects were more likely to choose disease 1 than disease 2 as their diagnosis for patients who had just symptom A, in accordance with the base-rates of the two diseases. However, on test trials where patients had both symptoms B and C, which had never previously occurred together, subjects were more likely to choose disease 2, contrary not only to the underlying base-rates, but also to the predictions of a well-established connectionist model of categorization. A variety of alternative connectionist models are considered. In Experiment 2 it was found that a necessary condition for the inverse base-rate effect is that symptom A is more strongly associated with disease 1 than disease 2, which is consistent with an associative learning account which appeals to the notion of competition between symptoms. A new connectionist learning model, using a learning algorithm based on Wagner's theory of associative learning, is shown to be able to reproduce the main results.