Staying in control: Characterizing the mechanisms underlying cognitive control in high and low arousal states.
Published In: British Journal of Psychology, 2024, v. 115, n. 4. P. 665 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Alameda, Clara; Avancini, Chiara; Sanabria, Daniel; Bekinschtein, Tristan A.; Canales‐Johnson, Andrés; Ciria, Luis F. 3 of 3
Abstract
Throughout the day, humans show natural fluctuations in arousal that impact cognitive function. To study the behavioural dynamics of cognitive control during high and low arousal states, healthy participants performed an auditory conflict task during high‐intensity physical exercise (N = 39) or drowsiness (N = 33). In line with the pre‐registered hypotheses, conflict and conflict adaptation effects were preserved during both altered arousal states. Overall task performance was markedly poorer during low arousal, but not for high arousal. Modelling behavioural dynamics with drift diffusion analysis revealed evidence accumulation and non‐decision time decelerated, and decisional boundaries became wider during low arousal, whereas high arousal was unexpectedly associated with a decrease in the interference of task‐irrelevant information processing. These findings show how arousal differentially modulates cognitive control at both sides of normal alertness, and further validate drowsiness and physical exercise as key experimental models to disentangle the interaction between physiological fluctuations on cognitive dynamics. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:British Journal of Psychology. 2024/11, Vol. 115, Issue 4, p665
- Document Type:Article
- Subject Area:Anatomy and Physiology
- Publication Date:2024
- ISSN:0007-1269
- DOI:10.1111/bjop.12715
- Accession Number:180231525
- Copyright Statement:Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.