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Record W4386726799 · doi:10.1080/02699931.2023.2256056

Looking on the bright side: the impact of ambivalent images on emotion regulation choice

2023· article· en· W4386726799 on OpenAlexafffund
Scarlett Horner, Lauryn Burleigh, Zachary Traylor, Steven G. Greening

Bibliographic record

VenueCognition & Emotion · 2023
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAmbivalencePsychologyAffordanceSocial psychologyIntensity (physics)Developmental psychologyCognitive psychologyOptics

Abstract

fetched live from OpenAlex

Previous research has found that people choose to reappraise low intensity images more often than high intensity images. However, this research does not account for image ambivalence, which is presence of both positive and negative cues in a stimulus. The purpose of this research was to determine differences in ambivalence in high intensity and low intensity images used in previous research (experiments 1-2), and if ambivalence played a role in emotion regulation choice in addition to intensity (experiments 3-4). Experiments 1 and 2 found that the low intensity images were more ambivalent than the high intensity images. Experiment 2 further found a positive relationship between ambivalence of an image and reappraisal affordances. Experiments 3 and 4 found that people chose to reappraise ambivalent images more often than non-ambivalent images, and they also chose to reappraise low intensity images more often than high intensity images. These experiments support the idea that ambivalence is a factor in emotion regulation choice. Future research should consider the impact ambivalent stimuli have on emotion regulation, including the potential for leveraging ambivalent stimuli to improve one's emotion regulation ability.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score1.000

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)0.0010.001

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 head0.118
GPT teacher head0.387
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2023
Admission routes2
Has abstractyes

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