Transfer and Complexity in Artificial Grammar Learning

Abstract

Implicit and explicit learning are sensitive to various degrees of complexity and abstractness, ranging from knowledge of first-order dependencies and specific surface structure to second-order dependencies and transfer. Three experiments addressed whether implicit learning is sensitive to this entire range of information or whether explicit knowledge becomes an important factor in cases of more complex learning. Experiment 1 used recognition and prediction to assess deliberate access to knowledge of letter patterns in an artificial grammar learning paradigm. Experiment 2 manipulated stimulus presentation and response in a sequence-based grammar learning paradigm. Learning can occur without awareness in cases of lesser complexity (such as learning first-order dependencies). However, more complex learning, such as that involved in learning second-order dependencies or in transfer to stimuli with the same underlying syntax but new surface features is linked to explicit knowledge. In contrast to Experiments 1 and 2 which assessed deliberate access to knowledge of the acquisition stimuli, Experiment 3 assessed deliberate access to knowledge of the transfer stimuli. Knowledge of initial trigrams in the transfer stimuli appears to play an important role in transfer. These findings are evaluated in terms of postulated implicit learning mechanisms.