The knowledge acquired during artificial grammar learning: Testing the predictions of two connectionist models

Abstract

An artificial grammar learning experiment is reported which investigated whether three types of information are learned during this kind of task: information about the positions of single letters, about fragments of training strings, and about entire training strings. Results indicate that participants primarily learned information about string fragments and, to a lesser extent, information about positions of letters. Two connectionist models, an autoassociator and a simple recurrent network (SRN), were tested on their ability to account for these results. In the autoassociator simulations, similarity of test items to entire training items had a large effect, which was at variance with the experimental results. The results of the SRN simulations almost perfectly matched the experimental ones.