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Cited by 1,163Memory and Neural MechanismsMemory Processes and InfluencesMotor Control and Adaptation

Characteristics of dissociable human learning systems

Open access
Shanks, David R., St. John, Mark F.·1994·Behavioral and Brain Sciences
DOI 10.1017/s0140525x00035032
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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.

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  • If responses on a physiological and a self-report measure do not align, a potential explanation is that the physiological measure captures responses along a dimension different from valence.

    “if responses on a physiological and a self-report measure do not align, a potential explanation is that the physiological measure captures responses along a dimension that is different from valence (i.e., sensitivity problem; see Shanks & St. John 1994).”

    From Unawareness of Attitudes, Their Environmental Causes, and Their Behavioral Effects (2025) · cites this paper as Shanks & St. John 1994

    Section 2.3.2 explains that a mismatch between performance and self-report can arise when the measures pick up different information, and distinguishes this Information Criterion from differences in sensitivity to the same information. The paper does not say that a physiological measure captures a dimension other than valence, so the claim's specific explanation is not supported by this source. (LLM, conf=0.83)

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Authors
Shanks, D. R., & St. John, M. F.
Affiliations
University College London (GB)
ORCID
Shanks, David R.
Venue
Behavioral and Brain Sciences
Publisher
Cambridge University Press (CUP)
Published
1994-09
Volume
17
Issue
3
Pages
367-395
ISSN
  • 0140-525X (ISSN-L)
  • 1469-1825
DOI
10.1017/s0140525x00035032
OpenAlex
W2030830933
Cited by
1,163
References
17
Citations by year
2026: 13 · 2025: 15 · 2024: 17 · 2023: 19 · 2022: 27 · 2021: 24 · 2020: 21 · 2019: 27 · 2018: 28 · 2017: 30 · 2016: 28 · 2015: 38 · 2014: 33 · 2013: 40 · 2012: 51
FWCI
34.46
Type
journal article
Language
en
Open access
Licence
www.cambridge.org/core/terms (unspecified, 5635 d)
Locations
  • Behavioral and Brain Sciences (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • University of California, San Diego
  • Medical Research Council
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
Keywords
explicit learning, implicit learning, unconscious learning, conscious learning, rule induction, subliminal learning, conditioning, artificial grammar learning, operant conditioning, sequence learning, reaction time, computational models of learning

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