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Traces of statistical learning in the brain's functional connectivity after artificial language exposure

  • Pallabi Sengupta
    ,
  • Miguel Burgaleta
    ,
  • Gorka Zamora-López
    ,
  • ,
  • Ana Sanjuan
    ,
  • Gustavo Deco
  • Pompeu Fabra University
Research Output:
Contribution to journal
Article
Peer-review

Open access

Publication metrics

Metrics

SciVal
Citations
2
Scopus
Citations
SciVal
FWCI
0.09
SciVal
Author count
7
SciVal
Paper percentile
28

Abstract

Our environment is full of statistical regularities, and we are attuned to learn about these regularities by employing Statistical Learning (SL), a domain-general ability that enables the implicit detection of probabilistic regularities in our surrounding environment. The role of brain connectivity on SL has been previously explored, highlighting the relevance of structural and functional connections between frontal, parietal, and temporal cortices. However, whether SL can induce changes in the functional connections of the resting state brain has yet to be investigated. To address this question, we applied a pre-post design where participants (n = 38) were submitted to resting-state fMRI acquisition before and after in-scanner exposure to either an artificial language stream (formed by 4 concatenated words) or a random audio stream. Our results showed that exposure to an artificial language stream significantly changed (corrected p < 0.05) the functional connectivity between Right Posterior Cingulum and Left Superior Parietal Lobule. This suggests that functional connectivity between brain networks supporting attentional and working memory processes may play an important role in statistical learning.

Publication Information

Output type

Research Output:
Contribution to journal
Article
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 246-253 (8 pages)

Journal (Volume, Issue Number)

Neuropsychologia (Volume 124)

Publication milestones

  • Published - 18/02/2019

Publication status

Published - 18/02/2019

ISSN

0028-3932

Publication IDs

  • Scopus: 85060101543