LINGUIST List 4.969

Sun 21 Nov 1993

FYI: Call for commentators: learning

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Directory

1. "Stevan Harnad", Learning - Implicit vs. Explicit: BBS Call for Commentators

Below is the abstract of a forthcoming target article by

 D.R. SHANKS and M.F. ST. JOHN on

 IMPLICIT VS. EXPLICIT LEARNING

that has been accepted for publication in Behavioral and Brain Sciences
(BBS), an international, interdisciplinary journal providing Open Peer
Commentary on important and controversial current research in the
biobehavioral and cognitive sciences.

Commentators must be current BBS Associates or nominated by a current
BBS Associate. To be considered as a commentator for this article, to
suggest other appropriate commentators, or for information about how to
become a BBS Associate, please send email to:

harnadclarity.princeton.edu or harnadpucc.bitnet or write to:
BBS, 20 Nassau Street, #240, Princeton NJ 08542 [tel: 609-921-7771]

To help us put together a balanced list of commentators, please give
some indication of the aspects of the topic on which you would bring
your areas of expertise to bear if you were selected as a commentator.
An electronic draft of the full text is available for inspection by
anonymous ftp according to the instructions that follow after the abstract.
____________________________________________________________________

 CHARACTERISTICS OF DISSOCIABLE HUMAN LEARNING SYSTEMS

 David R. Shanks
 Department of Psychology
 University College London
 London WC1E 6BT, England
 david.shankspsychol.ucl.ac.uk

 Mark F. St. John
 Department of Cognitive Science
 University of California at San Diego
 La Jolla, CA 92093
 mstjohncogsci.ucsd.edu

 KEYWORDS: learning; memory; consciousness; explicit/implicit
 processes; rules; instances; unconscious processes

 ABSTRACT: The proposal that there exist independent explicit
 and implicit learning systems is based on two further
 distinctions: (i) learning that takes place with versus without
 concurrent awareness, and (ii) 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 implicit
 learning evidence from subliminal learning, conditioning,
 artificial grammar learning, instrumental learning, and
 reaction times in sequence learning. 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.