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- W4386438172 abstract "When faced with distraction, we can focus more on goal-relevant information (targets) or focus less on goal-conflicting information (distractors). How people use cognitive control to distribute attention across targets and distractors remains unclear. We address this question by developing a novel Parametric Attentional Control Task that can tag participants' sensitivity to target and distractor information. We use these precise measures of attention to develop a novel process model that can explain how participants control attention toward targets and distractors. Across three experiments, we find that participants met the demands of this task by independently controlling their processing of target and distractor information, exhibiting distinct adaptations to manipulations of incentives and conflict. Whereas incentives preferentially led to target enhancement, conflict in the previous trial preferentially led to distractor suppression. These distinct drivers of control altered sensitivity to targets and distractors early in the trial, promptly followed by reactive reconfiguration toward task-appropriate feature sensitivity. To provide a process-level account of these empirical findings, we develop a novel neural network model of evidence accumulation with attractor dynamics over feature weights that reconfigure target and distractor processing. These results provide a computational account of control reconfiguration that provides new insights into how multivariate attentional signals are optimized to achieve task goals. (PsycInfo Database Record (c) 2023 APA, all rights reserved)." @default.
- W4386438172 created "2023-09-06" @default.
- W4386438172 creator A5071496598 @default.
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- W4386438172 date "2023-09-04" @default.
- W4386438172 modified "2023-09-27" @default.
- W4386438172 title "Humans reconfigure target and distractor processing to address distinct task demands." @default.
- W4386438172 doi "https://doi.org/10.1037/rev0000442" @default.
- W4386438172 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37668574" @default.
- W4386438172 hasPublicationYear "2023" @default.
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