ABSTRACT
This chapter examines computational models of implicit learning. One of the important results from the literature on people learning artificial grammars is that they can transfer their knowledge to a different letter set embodying the same grammar. The appealing intuition motivating the work of Servan-Schreiber and Anderson is that perception and memory are both more-or-less automatic processes of chunking. The Competitive Chunking model was given the same data, and it was assumed that it would recall a string only if nchunks equalled one for that string. Connectionism attempts to model human performance according to patterns of activation across a number of simple computational elements, or units, connected by weights. The auto associator may have been trained on grammatical strings in the learning phase. A number of promising approaches exist for modelling the learning of finite-state grammars: classifier systems, Competitive Chunking, exemplar models, and connectionist models.
