Abstract
Constant interaction with a dynamic environment—from riding a bicycle to segmenting speech—makes sensitivity to the sequential structure of the world a fundamental dimension of information processing. Accounts of sequence learning vary widely, with some authors arguing that parsing and segmentation processes are central, and others proposing that sequence learning involves mere memorization. In this paper, we argue that sequence knowledge is essentially statistical in nature, and that sequence learning involves simple associative prediction mechanisms. We focus on a choice reaction situation introduced by Lee (1997), in which participants were exposed to material that follows a single abstract rule, namely that stimuli are selected randomly, but never appear more than once in a legal sequence. Perhaps surprisingly, people can learn this rule very well. Or can they? We offer a conceptual replication of the original finding, but a very different interpretation of the results, as well as simulation work that makes it clear how highly abstract dimensions of the stimulus material can in fact be learned based on elementary associative mechanisms. We conclude that, when relevant, memory is optimized to facilitate responding to events that have not occurred recently, and that sequence learning in general always involves sensitivity to repetition distance.
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Notes
All networks involved dual connection weights as described in Cleeremans and McClelland (1991). Slow and fast weights were associated with learning rates of .04 and .45 respectively. Momentum was .9, and the fast weights decayed at a rate of .4. For each simulation study described in the text, twelve networks initialized with different random weights selected in the −.5–+.5 range were each trained in a total of 30,240 trials (720 sequences × 6 elements × 7 epochs), and their responses averaged.
An error in the stimulus generation program resulted in one instance of a stimulus associated with a lag of 21.
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Acknowledgements
Maud Boyer is a post-doctoral researcher supported by Grant RPG-53 from the International Human Frontiers of Science Program. Arnaud Destrebecqz is a post-doctoral researcher supported by a grant from the Fyssen Foundation. Axel Cleeremans is a Senior Research Associate of the National Fund for Scientific Research (Belgium). This work was supported by FRFC Grant#2.4605.95 F, by a grant from the European Commission (HPRN-CT-1999-000065), and by a grant from the Fyssen Foundation to Maud Boyer. We thank Pierre Perruchet, Robert French, Tim Curran, David Shanks, and an anonymous referee for insightful comments on previous versions of this article.
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Boyer, M., Destrebecqz, A. & Cleeremans, A. Processing abstract sequence structure: learning without knowing, or knowing without learning?. Psychological Research 69, 383–398 (2005). https://doi.org/10.1007/s00426-004-0207-4
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DOI: https://doi.org/10.1007/s00426-004-0207-4