A significant part of everyday learning occurs incidentally — a process typically described as implicit learning. A central issue in this and germane domains such as language acquisition is the extent to which performance depends on the acquisition and deployment of abstract rules. In an attempt to address this question, we show that the apparent use of such rules in a simple categorisation task of artificial grammar strings, as reported by Shanks, Johnstone, and Staggs (1997), can be simulated by means of a simple recurrent network, and may thus turn out not be incompatible with the acquisition of statistical regularities rooted in the processing of exemplars of the presented material.
|Title of host publication||Proceedings of the 22nd Annual Meeting of the Cognitive Science Society|
|Editors||Lila R Gleitman, Aravind K Joshi|
|Place of Publication||New Jersey|
|Publisher||Lawrence Erlbaum Associates|
|Number of pages||6|
|Publication status||Published - 2000|
Timmermans, B., & Cleeremans, A. (2000). Rules versus statistics in biconditional grammar learning: A simulation based on Shanks et al. (1997). In L. R. Gleitman, & A. K. Joshi (Eds.), Proceedings of the 22nd Annual Meeting of the Cognitive Science Society (pp. 947-952). Lawrence Erlbaum Associates. http://srsc.ulb.ac.be/axcWWW/papers/pdf/00-Cogsci.pdf