Adaptive learning of Gaussian categories leads to decision bounds and response surfaces incompatible with optimal decision making

Two experiments in category learning are used to examine two types of categorization models. In both a two and four choice experiment, subjects are shown to fail to learn to optimally classify two dimensional stimuli. The general recognition theory (GRT) of Ashby & Maddox (1990) predicts quadratic decision bounds. The first experiment disconfirms this. The extended GRT predicts that learners adopt a bound of complexity equivalent to the optimal one. The second experiment disconfirms this as well. Both experiments support the idea that general resources of adaptive systems can provide explanations of observed sub-optimal behavior.