COVIS

Abstract

The COVIS model of category learning assumes separate rule-based and procedural-learning categorization systems that compete for access to response production. The rule-based system selects and tests simple verbalizable hypotheses about category membership. The procedural-learning system gradually associates categorization responses with regions of perceptual space via reinforcement learning. Description and motivation of COVIS Despite the obvious importance of categorization to survival, and the varied nature of category-learning problems facing every animal, research on category learning has been narrowly focused (e.g., Markman & Ross, 2003). For example, the majority of category-learning studies have focused on situations in which two categories are relevant, the motor response is fixed, the nature and timing of feedback is constant (or ignored), and the only task facing the participant is the relevant categorization problem. One reason for this narrow focus is that until recently, the goal of most categorization research has been to test predictions from purely cognitive models that assume a single category-learning system. In typical applications, the predictions of two competing single-system models were pitted against each other and simple goodness-of-fit was used to select a winner (Maddox & Ashby, 1993; McKinley & Nosofsky, 1995; Smith & Minda, 1998). During the past decade, however, two developments have begun to alter this landscape. First, there are now many results suggesting that human categorization is mediated by multiple category-learning systems (Ashby & O'Brien, 2005; Ashby et al ., 1998; Erickson & Kruschke, 1998; Love, Medin, & Gureckis, 2004; Reber et al ., 2003).

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