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

An artificial grammar learning experiment is reported which investigated wh ether three types of information are learned during this kind of task: info rmation about the positions of single letters, about fragments of training strings, and about entire training strings. Results indicate that participa nts primarily learned information about string fragments and, to a lesser e xtent, 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, si milarity of test items to entire training items had a large effect, which w as at variance with the experimental results. The results of the SRN simula tions almost perfectly matched the experimental ones.