caa a22 4500 475788605 CHVBK 20180406123705.0 cr unu---uuuuu 170329e20000601xx s 000 0 eng 10.1007/s004260000038 doi (NATIONALLICENCE)springer-10.1007/s004260000038 Kinder Annette Department of Psychology, Philipps-Universität, Gutenbergstr. 18, D-35032 Marburg, Germany e-mail: kinder@mailer.uni-marburg.de, DE aut The knowledge acquired during artificial grammar learning: Testing the predictions of two connectionist models [Elektronische Daten] [Annette Kinder] 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. Springer-Verlag Berlin Heidelberg, 2000 https://doi.org/10.1007/s004260000038 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s004260000038 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Kinder Annette Department of Psychology, Philipps-Universität, Gutenbergstr. 18, D-35032 Marburg, Germany e-mail: kinder@mailer.uni-marburg.de, DE aut Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer