Neural systems underlying the learning of cognitive effort costs.
People balance the benefits of cognitive work against the costs of cognitive effort. Models that incorporate prospective estimates of the costs of cognitive effort into decision making require a mechanism by which these costs are learned. However, it remains an open question what brain systems are i...
| Publicado en: | Cognitive, Affective & Behavioral Neuroscience Vol. 21; no. 4; pp. 698 - 717 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151838663&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151838663 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15307026 NA4 jtl: Cognitive, Affective & Behavioral Neuroscience issn: 15307026 maglogo: N pubinfo: dt: Aug2021 vid: 21 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151838663 150194688 10.3758/s13415-021-00893-x 151838663 ppf: 698 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Neural systems underlying the learning of cognitive effort costs. aug: au: Sayalı, Ceyda Badre, David affil: Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA sug: ab: People balance the benefits of cognitive work against the costs of cognitive effort. Models that incorporate prospective estimates of the costs of cognitive effort into decision making require a mechanism by which these costs are learned. However, it remains an open question what brain systems are important for this learning, particularly when learning is not tied explicitly to a decision about what task to perform. In this fMRI experiment, we parametrically manipulated the level of effort a task requires by increasing task switching frequency across six task contexts. In a scanned learning phase, participants implicitly learned about the task switching frequency in each context. In a subsequent test phase, participants made selections between pairs of these task contexts. We modeled learning within a reinforcement learning framework, and found that effort expectations that derived from task-switching probability and response time (RT) during learning were the best predictors of later choice behavior. Prediction errors (PE) from these two models were associated with FPN during distinct learning epochs. Specifically, PE derived from expected RT was most correlated with the fronto-parietal network early in learning, whereas PE derived from expected task switching frequency was correlated with the fronto-parietal network late in learning. These results suggest that multiple task-related factors are tracked by the brain while performing a task that can drive subsequent estimates of effort costs. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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