The knowledge acquired during artificial grammar learning: testing the predictions of two connectionist models.
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.
Kinder
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Department of Psychology, Philipps-Universität, Marburg, Germany. kinder@mailer.uni-marburg.de
D000328:Adult; D001185:Artificial Intelligence; D003071:Cognition; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D016571:Neural Networks, Computer