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

Research output: Contribution to journalArticle

Authors

  • Vasilis M. Karlaftis
  • Joseph Giorgio
  • Petra E Vértes
  • Rui Wang
  • Yuan Shen
  • Andrew Welchman
  • Zoe Kourtzi

Colleges, School and Institutes

External organisations

  • Institute of Psychology, Chinese Academy of Sciences, Beijing, China,
  • Nottingham Trent University
  • University of Cambridge

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.

Details

Original languageEnglish
Pages (from-to)297-307
Number of pages11
JournalNature Human Behaviour
Volume3
Issue number3
Publication statusPublished - 14 Jan 2019