Experience sampling reveals the role that covert goal states play in task-relevant behavior

Mckeown, Brontë; Strawson, Will H.; Zhang, Meichao; Turnbull, Adam; Konu, Delali; Karapanagiotidis, Theodoros; Wang, Hao-Ting; Leech, Robert; Xu, Ting; Hardikar, Samyogita; Bernhardt, Boris; Margulies, Daniel; Jefferies, Elizabeth; Wammes, Jeffrey; Smallwood, Jonathan

Abstract

Cognitive neuroscience has gained insight into covert states using experience sampling. Traditionally, this approach has focused on off-task states. However, task-relevant states are also maintained via covert processes. Our study examined whether experience sampling can also provide insights into covert goal-relevant states that support task performance. To address this question, we developed a neural state space, using dimensions of brain function variation, that allows neural correlates of overt and covert states to be examined in a common analytic space. We use this to describe brain activity during task performance, its relation to covert states identified via experience sampling, and links between individual variation in overt and covert states and task performance. Our study established deliberate task focus was linked to faster target detection, and brain states underlying this experience—and target detection—were associated with activity patterns emphasizing the fronto-parietal network. In contrast, brain states underlying off-task experiences—and vigilance periods—were linked to activity patterns emphasizing the default mode network. Our study shows experience sampling can not only describe covert states that are unrelated to the task at hand, but can also be used to highlight the role fronto-parietal regions play in the maintenance of covert task-relevant states.

Citation

Mckeown, B., Strawson, W. H., Zhang, M., Turnbull, A., Konu, D., Karapanagiotidis, T., …Smallwood, J. (2023). Experience sampling reveals the role that covert goal states play in task-relevant behavior. Scientific Reports, 13(1), Article 21710. https://doi.org/10.1038/s41598-023-48857-0

• Journal Article Type: Article
• Acceptance Date: Nov 30, 2023
• Online Publication Date: Dec 7, 2023
• Publication Date: Dec 7, 2023
• Deposit Date: Dec 13, 2023
• Publicly Available Date: Dec 13, 2023
• Journal: Scientific Reports
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 1
• Article Number: 21710
• DOI: https://doi.org/10.1038/s41598-023-48857-0
• Public URL: https://durham-repository.worktribe.com/output/1987657