Cognitive and Neural Mechanisms of Implicit Learning

A special issue of Journal of Intelligence (ISSN 2079-3200).

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 3724

Special Issue Editors


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Guest Editor
Center for Research in Cognition and Neurosciences, Faculté des Sciences Psychologiques et de l’Education, Université Libre de Bruxelles (ULB), 50 avenue F.-D. Roosevelt (CP191), B-1050 Bruxelles, Belgium
Interests: learning; modeling

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Guest Editor
1. Department of Psychosocial Science, Faculty of Psychology, University of Bergen, 5020 Bergen, Norway
2. Department of Strategy and Management, Norwegian School of Economics, 5045 Bergen, Norway
Interests: implicit vs. explicit learning; metacognition; intuition

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Guest Editor
Institute of Psychology, Chinese Academy of Scienes, Beijing 100101, China
Interests: implicit learning; crossmodal learning; subliminal perception

Special Issue Information

Dear Colleagues,

Implicit learning covers all unintentional learning, in which people incidentally acquire knowledge of the structure of stimuli, even without awareness of the content of that knowledge (Dienes 2008). Since the seminal study of Arthur Reber, research on implicit learning has developed over the past 55 years. Recently, more and more work has been completed to explore the cognitive and neural mechanisms of implicit learning. The related findings have deepened our understanding of implicit learning and have largely influenced other research domains. Despite this, some questions remain open in the field of implicit learning. What can be learned implicitly? What is the relationship between implicit learning and intelligence or other cognitive abilities? What is the relationship between implicit and explicit learning? What is the neural basis for implicit learning? This topic provides a good platform to share new findings and show the advances in research in this area.

This Special Issue welcomes articles that address any of the related questions using paradigms, such as implicit sequence learning, artificial grammar learning, implicit category learning, statistical learning, and so on.  Behavioral studies, imaging studies, eye-movement studies, theoretical contributions, and literature reviews are also welcome.

Reference

  • Dienes, Zoltán. 2008. Subjective measures of unconscious knowledge. Progress in Brain Research 168: 49–64.

Prof. Dr. Arnaud Destrebecqz
Prof. Dr. Elisabeth Norman
Prof. Dr. Qiufang Fu
Guest Editors

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Keywords

  • implicit sequence learning
  • statistical learning
  • artificial grammar learning
  • implicit category learning
  • implicit learning in specific populations
  • implicit learning and consciousness research

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Published Papers (1 paper)

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Review

26 pages, 2051 KiB  
Review
Modulating Visuomotor Sequence Learning by Repetitive Transcranial Magnetic Stimulation: What Do We Know So Far?
by Laura Szücs-Bencze, Teodóra Vékony, Orsolya Pesthy, Nikoletta Szabó, Tamás Zsigmond Kincses, Zsolt Turi and Dezso Nemeth
J. Intell. 2023, 11(10), 201; https://doi.org/10.3390/jintelligence11100201 - 13 Oct 2023
Cited by 3 | Viewed by 2894
Abstract
Predictive processes and numerous cognitive, motor, and social skills depend heavily on sequence learning. The visuomotor Serial Reaction Time Task (SRTT) can measure this fundamental cognitive process. To comprehend the neural underpinnings of the SRTT, non-invasive brain stimulation stands out as one of [...] Read more.
Predictive processes and numerous cognitive, motor, and social skills depend heavily on sequence learning. The visuomotor Serial Reaction Time Task (SRTT) can measure this fundamental cognitive process. To comprehend the neural underpinnings of the SRTT, non-invasive brain stimulation stands out as one of the most effective methodologies. Nevertheless, a systematic list of considerations for the design of such interventional studies is currently lacking. To address this gap, this review aimed to investigate whether repetitive transcranial magnetic stimulation (rTMS) is a viable method of modulating visuomotor sequence learning and to identify the factors that mediate its efficacy. We systematically analyzed the eligible records (n = 17) that attempted to modulate the performance of the SRTT with rTMS. The purpose of the analysis was to determine how the following factors affected SRTT performance: (1) stimulated brain regions, (2) rTMS protocols, (3) stimulated hemisphere, (4) timing of the stimulation, (5) SRTT sequence properties, and (6) other methodological features. The primary motor cortex (M1) and the dorsolateral prefrontal cortex (DLPFC) were found to be the most promising stimulation targets. Low-frequency protocols over M1 usually weaken performance, but the results are less consistent for the DLPFC. This review provides a comprehensive discussion about the behavioral effects of six factors that are crucial in designing future studies to modulate sequence learning with rTMS. Future studies may preferentially and synergistically combine functional neuroimaging with rTMS to adequately link the rTMS-induced network effects with behavioral findings, which are crucial to develop a unified cognitive model of visuomotor sequence learning. Full article
(This article belongs to the Special Issue Cognitive and Neural Mechanisms of Implicit Learning)
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