WHAT IS LEARNED FROM ARTIFICIAL GRAMMARS - TRANSFER TESTS OF SIMPLE ASSOCIATION

Ss were trained on letter pairs or letter strings in an artificial grammar learning paradigm to determine the extent to which implicit learning is driven by simple associative knowledge. Learning on strings resulted in sensitivity to violations of grammaticality and in transfer to a changed letter set. Learning on letter pairs resulted in less sensitivity and no transfer. Discrepancies in performance were later reduced, but not eliminated, by equating the task demands of the conditions during learning. A direct test of associative knowledge showed that training on letter pairs resulted in knowledge of legal bigrams, but this knowledge was only weakly related to violation sensitivity. The experiments demonstrate that knowledge of isolated associations is sufficient to support some learning, but this knowledge cannot explain the more abstract knowledge that results from learning on complete exemplars.