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Characteristics of dissociable human learning systems

Published online by Cambridge University Press:  04 February 2010

David R. Shanks  and
Mark F. St. John
David R. Shanks
Affiliation:
Department of Psychology, University College London, London WC1E 6BT, England Electronic mail: david.shanks@psychol.ucl.ac.uk
Mark F. St. John
Affiliation:
Department of Cognitive Science, University of California at San Diego, La Jolla, CA 92093 Electronic mail: mstjohn@cogsci.ucsd.edu
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Abstract

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning.

Keywords

artificial grammarcategorizationconnectionismconsciousnessexplicit/implicit processesinstanceslearningmemoryrules
Type
Target Article
Information
Behavioral and Brain Sciences , Volume 17 , Issue 3 , September 1994 , pp. 367 - 395
Copyright
Copyright © Cambridge University Press 1994

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