Unconfounding similarity and rules in artificial grammar learning

Artificial grammar learning provides a principled experimental framework to investigate the roles of similarity and rule-induction mechanisms in category generalisation. Past attempts to disentangle these two mechanisms may be criticised for employing insensitive measures of similarity with little theoretical or empirical motivation, for failing to achieve independent measures of the effects of similarity and rule-induction components, and, with several notable exceptions, for confining stimuli to the domain of letter strings. The present work reports on two studies of artificial grammar learning using a standard grammar to arrange nested geometric shapes (Experiment 1) and angles between connected lines (Experiment 2). Grammaticality judgements for novel items are significantly above chance in both experiments. Similarity judgements for pairs of stimuli are used as the basis for modelling grammaticality judgements, using an exemplar-based model of categorisation. We test for independent contributions of similarity and rule-induction mechanisms by fitting nested regression models. Similarity is significant in accounting for grammaticality judgements in both experiments. Rule-induction has an additional, independent effect in Experiment 2, but not in Experiment 1. We discuss the implications of these results and their relationship to previous studies.