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Titolo:
CHARACTERISTICS OF DISSOCIABLE HUMAN LEARNING-SYSTEMS
Autore:
SHANKS DR; STJOHN MF;
Indirizzi:
UNIV LONDON UNIV COLL,DEPT PSYCHOL LONDON WC1E 6BT ENGLAND UNIV CALIF SAN DIEGO,DEPT COGNIT SCI LA JOLLA CA 92093
Titolo Testata:
Behavioral and brain sciences
fascicolo: 3, volume: 17, anno: 1994,
pagine: 367 - 395
SICI:
0140-525X(1994)17:3<367:CODHL>2.0.ZU;2-E
Fonte:
ISI
Lingua:
ENG
Soggetto:
COMPLEX PROCEDURAL KNOWLEDGE; SKIN-CONDUCTANCE RESPONSES; RULE-GOVERNED BEHAVIOR; IMPLICIT MEMORY; CONTINGENCY AWARENESS; EXPLICIT MEMORY; VERBALIZABLE KNOWLEDGE; UNCONSCIOUS PERCEPTION; ARTIFICIAL GRAMMARS; TASK-PERFORMANCE;
Keywords:
ARTIFICIAL GRAMMAR; CATEGORIZATION; CONNECTIONISM; CONSCIOUSNESS; EXPLICIT IMPLICIT PROCESSES; INSTANCES; LEARNING; MEMORY; RULES;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Social Sciences Citation Index
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
163
Recensione:
Indirizzi per estratti:
Citazione:
D.R. Shanks e M.F. Stjohn, "CHARACTERISTICS OF DISSOCIABLE HUMAN LEARNING-SYSTEMS", Behavioral and brain sciences, 17(3), 1994, pp. 367-395

Abstract

A number of ways of taxonomizing human learning have ben proposed. Weexamine 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 involvesthe 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 oftheses 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 isoften 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 taxonomizinghuman learning. We discuss various computational models of these two forms of learning.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 03/07/20 alle ore 00:40:27