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Generality of the Abstraction Mechanisms in Artificial Grammar Learning

Abstract

Artificial grammar learning (AGL; Reber, 1989) has been a major experimental paradigm for the study of human induction processes. In this work we investigate the extent to which the learning mechanisms involved in AGL are general, an issue important to the ecological validity of AGL research. We have used three kinds of stimuli; Letter strings (the standard in AGL work), city sequences that corresponded to routes of an airline company, and shapes that were presented so that later shapes in a sequence contained all previous ones. We compared overall accuracy and patterns of error in these domains to find that performance was not different. The implications of this finding for existing theories of AGL and proposed relations to other cognitive mechanisms are discussed.

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