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

Vasilis M. Karlaftis, Joseph Giorgio, Petra E. Vértes, Rui Wang, Yuan Shen, Peter Tino, Andrew E. Welchman () and Zoe Kourtzi ()
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Vasilis M. Karlaftis: University of Cambridge
Joseph Giorgio: University of Cambridge
Petra E. Vértes: University of Cambridge
Rui Wang: Chinese Academy of Sciences
Yuan Shen: Nottingham Trent University
Peter Tino: University of Birmingham
Andrew E. Welchman: University of Cambridge
Zoe Kourtzi: University of Cambridge

Nature Human Behaviour, 2019, vol. 3, issue 3, 297-307

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41562-018-0503-4

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