Learning to Overexert Cognitive Control in a Stroop Task.
How do people learn when to allocate how much cognitive control to which task? According to the Learned Value of Control (LVOC) model, people learn to predict the value of alternative control allocations from features of a situation. This suggests that people may generalize the value of control lear...
| Publicado en: | Cognitive, Affective & Behavioral Neuroscience Vol. 21; no. 3; pp. 453 - 472 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2021
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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=150935709&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150935709 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15307026 NA4 jtl: Cognitive, Affective & Behavioral Neuroscience issn: 15307026 maglogo: N pubinfo: dt: Jun2021 vid: 21 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150935709 147941708 10.3758/s13415-020-00845-x 150935709 ppf: 453 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Learning to Overexert Cognitive Control in a Stroop Task. aug: au: Bustamante, Laura Lieder, Falk Musslick, Sebastian Shenhav, Amitai Cohen, Jonathan affil: Princeton Neuroscience Institute, Princeton University, 08540, Princeton, NJ, USA sug: ab: How do people learn when to allocate how much cognitive control to which task? According to the Learned Value of Control (LVOC) model, people learn to predict the value of alternative control allocations from features of a situation. This suggests that people may generalize the value of control learned in one situation to others with shared features, even when demands for control are different. This makes the intriguing prediction that what a person learned in one setting could cause them to misestimate the need for, and potentially overexert, control in another setting, even if this harms their performance. To test this prediction, we had participants perform a novel variant of the Stroop task in which, on each trial, they could choose to either name the color (more control-demanding) or read the word (more automatic). Only one of these tasks was rewarded each trial and could be predicted by one or more stimulus features (the color and/or word). Participants first learned colors and then words that predicted the rewarded task. Then, we tested how these learned feature associations transferred to novel stimuli with some overlapping features. The stimulus-task-reward associations were designed so that for certain combinations of stimuli, transfer of learned feature associations would incorrectly predict that more highly rewarded task would be color-naming, even though the actually rewarded task was word-reading and therefore did not require engaging control. Our results demonstrated that participants overexerted control for these stimuli, providing support for the feature-based learning mechanism described by the LVOC model. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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