title: Associative sequence learning in humans.
abstract: In a series of experiments using the serial reaction time paradigm, the authors compared the predictions of a powerful associative model of sequence learning (the simple recurrent network; J. L. Elman, 1990) with human performance on the problem devised by A. Maskara and W. Noetzel (1993). Even though the predictions made by the simple recurrent network for variants of this problem are often counterintuitive, they matched human performance closely, suggesting that performance was associatively based rather than rule based. Simple associative chaining models of sequence learning, however, have difficulty in accommodating these results. The authors' conclusion is that, under the conditions of the experiments, human sequence learning is associatively driven, as long as this is understood to mean that a sufficiently powerful means of extracting the statistical regularities in the sequences is in play.
authors.affiliation: Department of Computing, Goldsmiths College, University of London, UK.
authors.forename: Rainer
authors.initials: R
authors.lastname: Spiegel
authors.forename: I P L
authors.initials: IP
authors.lastname: McLaren
mesh_terms: D000293:Adolescent; D000328:Adult; D001245:Association Learning; D005544:Forecasting; D006801:Humans; D008960:Models, Psychological; D011340:Problem Solving; D011582:Psychological Theory; D011597:Psychomotor Performance; D011930:Reaction Time; D012691:Serial Learning