Proceedings of the Annual Meeting of the Cognitive Science Society

This study extends the work of Druhan et al. (1989) and Mathews et al. (1989b) by applying their computational model of implicit learning to the task of learning artificial grammars (AG) without feedback. The ability of two induction algorithms, the forgetting algorithm which learns by inducing new rules from presented exemplars and the genetic algorithm which heuristically explores the space of possible rules, to induce the grammar rules through experience with exemplars of the grammar is evaluated and compared with data collected from human subjects performing the same A G task. The computational model, based on Holland et al.'s (1986) induction theory represents knowledge about the grammar as a set of partially valid condition-action rules that compete for control of response selection. The induction algorithms induce new rules that enter into competition with existing rules. The strengths of rules are modified by internally generated feedback. Strength accrues to those rules that best represent the structure present in the presented exemplars. W e hypothesized that the forgetting algorithm would successfully learn to discriminate valid &t)m invalid exemplars when the set of exemplars was high in family resemblance. W e also proposed that the genetic algorithm would perform better than chaiKe but not as well as the forgetting algorithm. Results supported those hypotheses. Interestingly, the Mathews et al. (1989a) subjects performed no better than chance on the same A G learning task. W e concluded that this discrepancy between the simulation results and the human data is caused by interference from unconstrained hypotheses generation of our human subjects. Support for this conclusion is two-fold: (1) subjects are able to learn the A G when the task is designed so that hypothesis geno^tion is inhibited, and (2) informal inspection of verbal protocols bom human subjects indicates they are generating and maintaining hypotheses of little or no validity.