Distinct but correlated latent factors support the regulation of learned conflict-control and task-switching

ElsevierVolume 135, June 2022, 101474Cognitive PsychologyHighlights•

We examine the latent structure of learning to adjust cognitive control.

We manipulate the proportion of congruency and task-switching over blocks of trials.

Model fit is best with correlated domain- and context-specific latent factors.

Model fit does not decrease when accounting for awareness, ability, and motivation.

Learned conflict-control and switch-readiness may depend on distinct abilities.

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.

Keywords

Cognitive control

Memory

Attention

Structural equation modeling

View full text

© 2022 Elsevier Inc. All rights reserved.

留言 (0)

沒有登入
gif