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Record W3196920401 · doi:10.1177/09567976211005465

Acute Psychosocial Stress Increases Cognitive-Effort Avoidance

2021· article· en· W3196920401 on OpenAlexaff
Mario Bogdanov, Jonas P. Nitschke, Sophia LoParco, Jennifer A. Bartz, A. Ross Otto

Bibliographic record

VenuePsychological Science · 2021
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyCognitionTask (project management)Continuous performance taskStress (linguistics)Developmental psychologyEffects of sleep deprivation on cognitive performanceElementary cognitive taskPreferenceCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

Adverse effects following acute stress are traditionally thought to reflect functional impairments of central executive-dependent cognitive-control processes. However, recent evidence demonstrates that cognitive-control application is perceived as effortful and aversive, indicating that stress-related decrements in cognitive performance could denote decreased motivation to expend effort instead. To investigate this hypothesis, we tested 40 young, healthy individuals (20 female, 20 male) under both stress and control conditions in a 2-day study that had a within-subjects design. Cognitive-effort avoidance was assessed using the demand-selection task, in which participants chose between performing low-demand and high-demand variants of a task-switching paradigm. We found that acute stress indeed increased participants' preference for less demanding behavior, whereas task-switching performance remained intact. Additional Bayesian and multiverse analyses confirmed the robustness of this effect. Our findings provide novel insights into how stressful experiences shape behavior by modulating our motivation to employ cognitive control.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.385
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations77
Published2021
Admission routes1
Has abstractyes

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