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Record W4233539465 · doi:10.32920/ryerson.14654418

Examining the role of cognitive bias in emotion regulation

2021· preprint· en· W4233539465 on OpenAlexaff
Aleksandra Usyatynsky

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsSystems, Applications & Products in Data Processing (Canada)Toronto Metropolitan UniversityQueen's University
Fundersnot available
KeywordsInterpretation (philosophy)PsychologyCognitive biasAffect (linguistics)Expressive SuppressionCognitive reappraisalCognitive bias modificationCognitionAttentional biasCognitive psychologySocial psychologyCommunicationNeuroscience

Abstract

fetched live from OpenAlex

Individuals experiencing depressive symptoms interpret ambiguous situations negatively and use helpful emotion regulation strategies less often than those without symptoms. Theory suggests these strategies are used less due to interference from negatively biased interpretations. This study examined whether interpretation bias interferes with emotion regulation by experimentally manipulating interpretations in a positive or negative direction. Method: Undergraduate students were randomly assigned to positive and negative bias training groups. Interpretation bias and emotion regulation questionnaires were completed before and after training. Results: The training succeeded in inducing bias change only for the positive group, and emotion regulation strategy use did not change in either group. Discussion: Interpretation bias was not found to affect emotion regulation. Possible explanations include: bias change in the positive group was not large enough to alter emotion regulation; the task eliciting emotion regulation was ill-suited for this study; and interpretation bias and emotion regulation are unrelated.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0000.000

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 head0.142
GPT teacher head0.315
Teacher spread0.173 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2021
Admission routes1
Has abstractyes

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