How is complex sequential material acquired, processed, and represented when there is no intention to learn? Two experiments exploring a choice reaction time task are reported. Unknown to Ss, successive stimuli followed a sequence derived from a "noisy " finite-state grammar. After considerable practice (60,000 exposures) with Experiment 1, Ss acquired a complex body of procedural knowledge about the sequential structure of the material. Experiment 2 was an attempt to identify limits on Ss ability to encode the temporal context by using more distant contingencies that spanned irrelevant material. Taken together, the results indicate that Ss become increasingly sensitive to the temporal context set by previous elements of the sequence, up to 3 elements. Responses are also affected by priming effects from recent trials. A connectionist model that incorporates sensitivity to the sequential structure and to priming effects is shown to capture key aspects of both acquisition and processing and to account for the interaction between attention and sequence structure reported by Cohen, Ivry, and Keele (1990).

When exposed to a regular stimulus field, for instance, that generated by an artificial grammar, subjects unintentionally learn to respond efficiently to the underlying structure (Miller, 1958; Reber 1967). We explored the hypothesis that the learning process is chunking and that grammatical knowledge is implicitly encoded in a hierarchical network of chunks. We trained subjects on exemplar sentences while inducing them to form specific chunks. Their knowledge was then assessed through judgments ofgrammaticality. We found that subjects were less sensitive to violations that preserved their chunks than to violations that did not. We derived the theory of competitive chunking (CC) and found that it successfully reproduces, via computer simula-tions, both Miller's experimental results and our own. In CC, chunks are hierarchical structures strengthened with use by a bottom-up perception process. Strength-mediated competitions determine which chunks are created and which are used by the perception process. The world is regular, and people are efficient regularity detectors. Sometimes people are intentionally looking for structural regularities. Other times, however, people learn to respond to structured stimuli even though they do not suspect

Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study of implicit learning and the contrast between rules, similarity, and associative learning. Despite five decades of extensive research, however, a satisfactory theoretical consensus has not been forthcoming. Theoretical accounts of AGL are reviewed, together with relevant human experimental and neuroscience data. The author concludes that satisfactory understanding of AGL requires (a) an understanding of implicit knowledge as knowledge that is not consciously activated at the time of a cognitive operation; this could be because the corresponding representations are impoverished or they cannot be concurrently supported in working memory with other representations or operations, and (b) adopting a frequency-independent view of rule knowledge and contrasting rule knowledge with specific similarity and associative learning (co-occurrence) knowledge.

We present an unsupervised learning methodology derived from compact data encoding and demonstrate how to construct models of polysemy, priming, semantic disambiguation and learning using this theoretical basis. The model is capable of simulating human-like performance on artificial grammar learning.