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Variation in working memory capacity and cognitive control: Goal maintenance and microadjustments of control
Abstract
Variation in working memory capacity (WMC) and cognitive control was examined in four experiments. In the experiments high- and low-WMC individuals performed a choice reaction time task (Experiment 1), a version of the antisaccade task (Experiment 2), a version of the Stroop task (Experiment 3), and an arrow version of the flanker task (Experiment 4). An examination of response time distributions suggested that high- and low-WMC individuals primarily differed in the slowest responses in each experiment, consistent with the notion that WMC is related to active maintenance abilities. Examination of two indicators of microadjustments of control (posterror slowing and conflict adaptation effects) suggested no differences between high- and low-WMC individuals. Collectively these results suggest that variation in WMC is related to some, but not all, cognitive control operations. The results are interpreted within the executive attention theory of WMC.
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“In terms of human cognition, such congruence supports cognitive flow and lowers cognitive load [135], which in turn fosters trust [31, 136].”
From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [135]
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“Breakdowns in this alignment, however, increase cognitive burden [135] and require frequent user intervention, which ultimately erodes trust.”
From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [135]
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Cited by
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Self-citationWhat Guides Our Choices? Modeling Developers' Trust and Behavioral Intentions Towards Genai
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What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI
Choudhuri, R., Trinkenreich, B., Pandita, R., Kalliamvakou, E., Steinmacher, I., Gerosa, M., Sanchez, C., & Sarma, A. (2025) · arXiv · doi:10.48550/arxiv.2505.17418 · Cited by 14
AI Where It Matters: Where, Why, and How Developers Want AI Support in Daily Work
Choudhuri, R., Badea, C., Bird, C., Butler, J. L., DeLine, R., & Houck, B. (2026) · Proceedings of the IEEE/ACM 48th International Conference on Software Engineering: Software Engineering in Practice · doi:10.1145/3786583.3786885 · Cited by 1
Metadata
- Authors
- Unsworth, N., Redick, T. S., Spillers, G. J., & Brewer, G. A.
- Affiliations
- Department of Psychology, University of Oregon, Eugene, OR, USA
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
- Department of Psychology, Arizona State University, Tempe, AR, USA
- Venue
- Quarterly Journal of Experimental Psychology
- Publisher
- SAGE Publications
- Published
- 2012-02
- Volume
- 65
- Issue
- 2
- Pages
- 326-355
- ISSN
- 1747-0218
- 1747-0226
- Cited by
- 84
- References
- 18
- Language
- en
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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