The Representation of Structure in Sequence Prediction Tasks

Is knowledge acquired implicitly abstract or based on memory for exemplars? This question is at the heart of a current, but long-standing, controversy in the field of implicit learning (see Reber, 1989, for a review). For some authors, implicit knowledge is best characterized as rulelike. For others, however, knowledge acquired implicitly is little more than knowledge about memorized exemplars, or at best, knowledge about elementary features of the material, such as the frequency of particular events. In this paper, I argue that the debate may be ill-posed, and that the two positions are not necessarily incompatible. Using simulation studies, I show that abstract knowledge about the stimulus material may emerge through the operation of elementary, associationist learning mechanisms of the kind that operate in connectionist networks. I focus on a sequence learning task first proposed by Kushner, Cleeremans & Reber (1991), during which subjects are exposed to random fixed-length sequence..