ABSTRACT
This study develops a computational model based on the Holland et al.'s (1986) induction theory to simulate the tacit knowledge of artificial grammars acquired from experience with exemplars of the grammar (e.g., Reber, 1969, 1976). The initial application of this model tests the proposition that the rules acquired about an artificial grammar consist of sets of partially valid rules that compete against one another to control response selection. Choices are made and the strength of rules is adjusted based on current levels of strength, specificity, and support among rules having their conditions matched on a particular trial. Verbal instructions generated by two human subjects who developed expertise in discriminating valid from invalid strings through extensive practice on a multiple choice string discrimination task served as inputs into the simulation model. Results show that these sets of rules verbalized by subjects can be represented as sets of condition-action rules. Further, these rules can compete against each other to select valid choices on the string discrimination task as described in the Holland et al. model, resulting in a level of performance very similar to that of human yoked subjects who attempted to use the rules provided by the original subjects. Finally, when the rules are automatically tuned by an optimization algorithm using feedback about correctness of choices, performance of the simulation approaches the level of the original subject. It is concluded that a considerable portion of implicit knowledge that is not verbalized to yoked partners consists of the relative strengths of competing rules.
