Modelling category learning using a dual-system approach: a simulation of Shepard, Hovland and Jenkins (1961) by COVIS

This paper examines the ability of a dual-system, formal model of categorization COVIS (Ashby, Paul & Maddox, 2011) to predict the learning performance of participants on the six category structures described in Shepard, Hovland and Jenkin’s (1961) seminal study. COVIS assumes that category learning is mediated by two dissociable neural systems that compete to control responding. The verbal system explicitly tests verbalizable rules, whereas the implicit system gradually associates each stimulus with the appropriate response. Although COVIS is highly influential, there are no published evaluations of the formal model against classic category learning data (COVIS is most typically applied heuristically to the design of new experiments). In the current paper, we begin to address this gap in the literature. Specifically, we demonstrate that COVIS is able to accommodate the ordinal pattern found by Shepard et al., provided that adjustments consistent with the model’s theoretical framework are made