The Neuroscience of Implicit Learning

被引:20
|
作者
Williams, John N. [1 ]
机构
[1] Univ Cambridge, Cambridge, England
关键词
artificial grammar learning; statistical learning; sequence learning; serial reaction time task; fMRI; INFERIOR FRONTAL-CORTEX; MEDIAL TEMPORAL-LOBE; REACTION-TIME-TASK; ARTIFICIAL GRAMMAR; PARKINSONS-DISEASE; BROCAS AREA; INDIVIDUAL-DIFFERENCES; NEURAL BASIS; SENTENCE COMPREHENSION; HIPPOCAMPAL SYSTEM;
D O I
10.1111/lang.12405
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Over the past decades, research employing artificial grammar, sequence learning, and statistical learning paradigms has flourished, not least because these methods appear to offer a window, albeit with a restricted view, on implicit learning processes underlying natural language learning. But these paradigms usually provide relatively little exposure, use meaningless stimuli, and do not even necessarily target natural language structures. So the question arises whether they engage the same brain regions as natural language. The aim of this review is to use data from brain imaging, brain stimulation, and the effects of brain damage to identify the main brain regions that show sensitivity to structural regularities in implicit learning paradigms and to consider their relationship to natural language processing and learning.
引用
收藏
页码:255 / 307
页数:53
相关论文
共 50 条
  • [31] Dyslexia, learning, and pedagogical neuroscience
    Fawcett, Angela J.
    Nicolson, Roderick I.
    DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY, 2007, 49 (04): : 306 - 311
  • [32] A deep learning framework for neuroscience
    Blake A. Richards
    Timothy P. Lillicrap
    Philippe Beaudoin
    Yoshua Bengio
    Rafal Bogacz
    Amelia Christensen
    Claudia Clopath
    Rui Ponte Costa
    Archy de Berker
    Surya Ganguli
    Colleen J. Gillon
    Danijar Hafner
    Adam Kepecs
    Nikolaus Kriegeskorte
    Peter Latham
    Grace W. Lindsay
    Kenneth D. Miller
    Richard Naud
    Christopher C. Pack
    Panayiota Poirazi
    Pieter Roelfsema
    João Sacramento
    Andrew Saxe
    Benjamin Scellier
    Anna C. Schapiro
    Walter Senn
    Greg Wayne
    Daniel Yamins
    Friedemann Zenke
    Joel Zylberberg
    Denis Therien
    Konrad P. Kording
    Nature Neuroscience, 2019, 22 : 1761 - 1770
  • [33] Neuroscience - Learning visualized, on the double
    Barinaga, M
    SCIENCE, 1999, 286 (5445) : 1661 - 1661
  • [34] Deep Learning and Computational Neuroscience
    De Schutter, Erik
    NEUROINFORMATICS, 2018, 16 (01) : 1 - 2
  • [35] Implicit covariation learning
    Hendrickx, H
    DeHouwer, J
    PSYCHOLOGICA BELGICA, 1997, 37 (1-2) : 29 - 49
  • [36] Probabilities in implicit learning
    Tseng, Philip
    Hsu, Tzu-Yu
    Tzeng, Ovid J. L.
    Hung, Daisy L.
    Juan, Chi-Hung
    PERCEPTION, 2011, 40 (07) : 822 - 829
  • [37] Implicit learning and consciousness
    Maibom, HL
    PHILOSOPHICAL PSYCHOLOGY, 2005, 18 (02) : 284 - 288
  • [38] Implicit Kernel Learning
    Li, Chun-Liang
    Chang, Wei-Cheng
    Mroueh, Youssef
    Yang, Yiming
    Poczos, Barnabas
    22ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 89, 2019, 89
  • [39] IMPLICIT PRACTICAL LEARNING
    ENNEN, E
    BEHAVIORAL AND BRAIN SCIENCES, 1994, 17 (03) : 404 - 405
  • [40] The Neuropharmacology of Implicit Learning
    Udden, Julia
    Folia, Vasiliki
    Petersson, Karl Magnus
    CURRENT NEUROPHARMACOLOGY, 2010, 8 (04) : 367 - 381