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Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning.

Accepted version
Peer-reviewed

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Article

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Authors

Karlaftis, Vasilis M  ORCID logo  https://orcid.org/0000-0003-1285-1593
Giorgio, Joseph 
Vértes, Petra E 
Wang, Rui 
Shen, Yuan 

Abstract

Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.

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Keywords

1701 Psychology, 1109 Neurosciences, Biomedical, Behavioral and Social Science, Neurosciences, Bioengineering, Brain Disorders, Basic Behavioral and Social Science, Mental Health, Neurological, 1.2 Psychological and socioeconomic processes, 1.1 Normal biological development and functioning, 2.3 Psychological, social and economic factors

Journal Title

Nat Hum Behav

Conference Name

Journal ISSN

2397-3374
2397-3374

Volume Title

3

Publisher

Springer Science and Business Media LLC
Sponsorship
European Commission (290011)
Leverhulme Trust (RF-2011-378)
European Commission (316746)
Wellcome Trust (205067/Z/16/Z)
ESRC (ES/M500409/1)
Biotechnology and Biological Sciences Research Council (BB/P021255/1)
Alan Turing Institute (EP/N510129/1)
MQ: Transforming Mental Health (MQ17-24 Vertes)
Medical Research Council (MR/K020706/1)
Wellcome Trust (095183/Z/10/Z)
This work was supported by grants to ZK from the Biotechnology and Biological Sciences Research Council (H012508 and BB/P021255/1), the Leverhulme Trust (RF-2011-811 378), the Alan Turing Institute (TU/B/000095), the Wellcome Trust (205067/Z/16/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2011-290011, AEW from the Wellcome Trust (095183/Z/10/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2012-316746, PT from Engineering and Physical Sciences Research Council (EP/L000296/1), PEV from the MRC (MR/K020706/1).
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