ArticleCognitive psychology2022
Distinct but correlated latent factors support the regulation of learned conflict-control and task-switching.
Article in Cognitive psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Insights into the neural mechanisms supporting cognitive stability and flexibility from human intracranial electroencephalography.Neuroscience and biobehavioral reviews · 2026Review
- Modeling of control over task switching and cross-task interference supports a two-dimensional model of cognitive stability and flexibility.Psychonomic bulletin & review · 2025Review
- Contextual control demands determine whether stability and flexibility trade off against each other.Attention, perception & psychophysics · 2024Article
- Insights into control over cognitive flexibility from studies of task-switching.Current opinion in behavioral sciences · 2024Article
- Principles of cognitive control over task focus and task switching.Nature reviews psychology · 2023Article
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3 authors.
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Abstract
Cognitive control is guided by learning, as people adjust control to meet changing task demands. The two best-studied instances of "control-learning" are the enhancement of attentional task focus in response to increased frequencies of incongruent distracter stimuli, reflected in the list-wide proportion congruent (LWPC) effect, and the enhancement of switch-readiness in response to increased frequencies of task switches, reflected in the list-wide proportion switch (LWPS) effect. However, the latent architecture underpinning these adaptations in cognitive stability and flexibility - specifically, whether there is a single, domain-general, or multiple, domain-specific learners - is currently not known. To reveal the underlying structure of control-learning, we had a large sample of participants (N = 950) perform LWPC and LWPS paradigms, and afterwards assessed their explicit awareness of the task manipulations, as well as general cognitive ability and motivation. Structural equation modeling was used to evaluate several preregistered models representing different plausible hypotheses concerning the latent structure of control-learning. Task performance replicated standard LWPC and LWPS effects. Crucially, the model that best fit the data had correlated domain- and context-specific latent factors. Thus, people's ability to adapt their on-task focus and between-task switch-readiness to changing levels of demand was mediated by distinct (though correlated) underlying factors. Model fit remained good when accounting for speed-accuracy trade-offs, variance in individual cognitive ability and self-reported motivation, as well as self-reported explicit awareness of manipulations and the order in which different levels of demand were experienced. Implications of these results for the cognitive architecture of dynamic cognitive control are discussed.
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