Testing the simple recurrent network model in explicit artificial grammar learning task

Abstract

The simple recurrent network (SRN) proposed by Cleeremans & McClelland (1991) is one of the most successful models for implicit learning of complex sequences. In this study, we examined whether the SRN can also explain subjects' performance in explicit learning of an artificial grammar. In two experiments, subjects learned syntactic rules in the order that the SRN model predicted. This suggests that the underlying mechanisms of implicit and explicit sequence learning are closely related to each other in behaving like the SRN.