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CHARACTERISTICS OF DISSOCIABLE HUMAN LEARNING SYSTEMS

Shanks, David R. and St. John, Mark F. (1994) CHARACTERISTICS OF DISSOCIABLE HUMAN LEARNING SYSTEMS.

Short 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.

Long 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.

Keywords:	learning; memory; consciousness; explicit/implicit processes; rules; instances; unconscious processes
Subjects:	Psychology: Cognitive Psychology Computer Science: Neural Nets Psychology: Learning and Memory Neuroscience: Neural Modelling
ID code:	bbs00000483
Deposited by:	David Shanks on 01 May 2001