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Record W3146120052 · doi:10.1016/j.heliyon.2021.e06599

Loss-related mental states impair executive functions in a context of sadness

2021· article· en· W3146120052 on OpenAlexaff
Geneviève Beaulieu‐Pelletier, Marc‐André Bouchard, Frédérick L. Philippe

Bibliographic record

VenueHeliyon · 2021
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsSadnessAffect (linguistics)Context (archaeology)PsychologyExecutive functionsAnxietyAffect regulationCognitionCognitive psychologyDevelopmental psychologyClinical psychologyPsychiatryAngerBiology

Abstract

fetched live from OpenAlex

Stress and anxiety have been shown to temporally impair executive functions, but the role of other emotions, such as sadness, has been inconclusive. Moreover, the role of affect regulation in this relationship has not been extensively studied. The present research investigated whether certain types of mental states (mental output resulting from the use of affect regulation within a specific context or with respect to a specific material or theme) relative to the context of loss would predict impairment of executive functions. Participants were randomly assigned to read either a loss-related newspaper article inducing sadness or a neutral newspaper article. Results showed that low mental states relative to loss (maladaptive affect regulation) predicted impairment of executive functions following an induction of sadness, but not following the neutral induction. Conversely, high mental states (adaptive affect regulation) were not predictive of impairment of executive functions in both the sadness and neutral condition. These findings have implications for the boundaries within which emotion can disrupt high-order cognitive processes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.000

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 head0.046
GPT teacher head0.399
Teacher spread0.353 · 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 teacher head, not a consensus.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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