On what it means to automatize a rule

Abstract

The results of two experiments are reported that included a combined total of approximately 633,000 categorization trials. The experiments investigated the nature of what is automatized after lengthy practice with a rule-guided behavior. The results of both experiments suggest that an abstract rule, if interpreted as a verbal-based strategy, was not automatized during training, but rather the automatization linked a set of stimuli with similar values on one visual dimension to a common motor response. The experiments were designed to test and refine a recent neurocomputational model of how rule-guided behaviors become automatic (Kovacs, Hélie, Tran, & Ashby, 2021). The model assumes that rule-guided behaviors are initially controlled by a distributed neural network centered on rule units in prefrontal cortex, and that in addition to initiating behavior, this network also trains a faster and more direct network that includes projections from visual cortex directly to the rule-sensitive neurons in premotor cortex. The present results support this model and suggest that the projections from visual cortex to prefrontal and premotor cortex are restricted to visual representations of the relevant stimulus dimension only.