Rule-based and exemplar-based classification in artificial grammar learning

In this study, we examined the induction of syntactic rules, given the presentation of letter strings generated from a finite-state grammar. Our primary interest was whether application of abstracted syntax or analogy to remembered exemplars could serve as a basis for judgments of grammaticality of novel stimuli. The grammatical status of test items and their objective similarity to studied exemplars were manipulated independently to investigate whether rule-based or instance-based information was a more important determinant of classification performance. When group data were examined, the results indicated that both factors were equally important in influencing grammaticality judgments about novel letter strings. There were, however, large individual differences in the magnitude of grammatical status effects, with a subgroup of subjects clearly using a classification strategy other than analogy to remembered exemplars. The results offer qualified support for the hypothesis (Reber & Allen, 1978) that rule-based information can be implicitly abstracted given limited experience with richly structured stimulus domains, and these results are inconsistent with a strong version of the instance-based model of categorization.