Behavioral and Brain Sciences

Target Article

Characteristics of dissociable human learning systems

David R. Shanksa1 and Mark F. St. Johna2

a1 Department of Psychology, University College London, London WC1E 6BT, England Electronic mail: david.shanks@psychol.ucl.ac.uk

a2 Department of Cognitive Science, University of California at San Diego, La Jolla, CA 92093 Electronic mail: mstjohn@cogsci.ucsd.edu

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.

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