Brain Connectivity-Based Prediction of Combining Remote Semantic Associates for Creative Thinking

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  • Additional Information
    • Availability:
      Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
    • Peer Reviewed:
      Y
    • Source:
      25
    • Subject Terms:
    • Accession Number:
      10.1080/10400419.2023.2192563
    • ISSN:
      1040-0419
      1532-6934
    • Abstract:
      Associative thinking plays a major role in creativity, as it involves the ability to link distant concepts. Yet, the neural mechanisms allowing to combine distant associates in creative thinking tasks remain poorly understood. We investigated the whole-brain functional connectivity patterns related to combining remote associations for creative thinking. Using a connectome predictive modeling approach, we examined whole-brain functional connectivity patterns related to connecting close and distant remote associates in the Combination Association Task (CAT). Brain connectivity networks predicting CAT performance showed contributions from brain functional connectivity mostly related to the Default Mode Network, likely related to associative processes required in all trials of the task. Besides, the functional connectivity pattern of associative remoteness linked to CAT trials also largely involved the Executive Control Network, Dorsal Attention Network and Somatomotor networks, suggesting that more controlled processes played an important role in trials with higher associative remoteness. Critically, the functional connectivity patterns related to higher creative demands of the task share similarities with functional connectivity patterns previously found to predict divergent thinking. Thus, our work potentially offers insights into neural mechanisms that play a role in both convergent and divergent remote thinking.
    • Abstract:
      As Provided
    • Notes:
      https://osf.io/vp9s8/?view_only=91b4dd63fc2f4d2481612e802881fdb2
    • Publication Date:
      2023
    • Accession Number:
      EJ1396983