Evidence map›Paper›PMID 35405421›Full record

ArticleCognitive psychology2022

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

Christina Bejjani, Rick H Hoyle, Tobias Egner

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Christina BejjaniDepartment of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States; Duke Institute for Brain Sciences, Durham, NC 27708, United States. Electronic address: christina.bejjani@duke.edu.
Rick H HoyleDepartment of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Duke Institute for Brain Sciences, Durham, NC 27708, United States.
Tobias EgnerDepartment of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States; Duke Institute for Brain Sciences, Durham, NC 27708, United States.

Funding

TRANSLATION AND DISSEMINATION COREP30DA023026 · NIDA · DUKE UNIVERSITY · PI HOYLE, RICK · 2008 to 2023
$19.6M
Neural Mechanisms of Cognitive Meta-FlexibilityR01MH116967 · NIMH · DUKE UNIVERSITY · PI EGNER, TOBIAS · 2019 to 2023
$2.0M
NIDA NIH HHS P30 DA023026NIMH NIH HHS R01 MH116967
6 · The paper itself

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.

Indexed as

AttentionLearningAdaptation, PhysiologicalCognitionHumansMotivationReaction TimeAttentionCognitive controlMemoryStructural equation modeling

Identifiers

PMID35405421
PMCPMC9170285

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.