Strategic regulation of cognitive control by emotional salience: a neural network model
Abstract
We present a neural network model of stimulus processing, which uses a mechanism of adaptive attentional control to regulate the moment to moment deployment of attention according to both the demands of the current task, and the demands of emotionally salient information. This mechanism allows negative emotional information to reduce cognitive control to aid in the detection of threats, which produces a momentary withdrawal from the current task set to allow unbiased processing of available information. The combination of cognitive and emotional regulation of task set allows this model to address inter-trial aspects of emotional interference in colour naming. In particular, we focus on the nature of the emotional interference in colour naming (McKenna & Sharma, 2004) as well as in word reading (Algom, Chajut, & Lev, 2004) and show how this form of interference is functionally distinct from the classic Stroop effect. Our model addresses a range of findings in colour naming and word reading tasks and is informed by recent neuroimaging data concerning the interaction between the anterior cingulate and prefrontal cortices. The model is used to explore the interface between cognition and emotion with a series of predictions, including a qualitative distinction between state and trait forms of anxiety.
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Attention control mechanisms involved in the orienting, maintaining, and shifting of attention are thought to determine the degree to which emotional information detrimentally impacts performance by altering attention from task demands.
“Attention control mechanisms involved in the orienting, maintaining, and shifting of attention are thought to determine the degree to which emotional information detrimentally impacts performance by altering attention from task demands [55,56].”
From A randomized controlled trial of yoga vs nonaerobic exercise for veterans with PTSD: Understanding efficacy, mechanisms of change, and mode of delivery (2021) · cites this paper as [56]
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Levels of cognitive understanding: Reflective and impulsive cognition in alcohol use and misuse
Sharma, D., & Cane, J. E. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-816720-5.00018-9
Self-citationDing, K., Li, C., Wang, J., & Yu, D. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629783
- Authors
- Wyble, B., Sharma, D., & Bowman, H.
- Affiliations
- University of Kent (GB)
- Venue
- Cognition & Emotion
- Publisher
- Informa UK Limited
- Published
- 2008-09
- Volume
- 22
- Issue
- 6
- Pages
- 1019-1051
- ISSN
- 0269-9931 (ISSN-L)
- 1464-0600
- OpenAlex
- W1992782256
- Cited by
- 44
- References
- 68
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 1 · 2022: 2 · 2021: 5 · 2020: 2 · 2019: 3 · 2018: 3 · 2017: 1 · 2016: 4 · 2015: 2 · 2014: 4 · 2012: 1
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognition & Emotion (publishedVersion)
- Kent Academic Repository (University of Kent) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- cognitive control, emotional salience, attentional control, neural network model, emotional interference, color naming, word reading, Stroop interference, threat detection, anterior cingulate cortex, prefrontal cortex, anxiety, emotion-cognition interaction
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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