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Record W7079600207 · doi:10.17605/osf.io/97hsv

Mechanisms of saying versus thinking reappraisals (study 3)

2025· other· en· W7079600207 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsMediationAffect (linguistics)Test (biology)Empirical researchModality (human–computer interaction)

Abstract

fetched live from OpenAlex

In two recent studies from our lab (study 2 pre-registration: https://osf.io/pcv56), we found that sharing reappraisals – either by saying reappraisals out loud or writing them down – was more effective at regulating negative affect than thinking about reappraisals. In this study, we plan to build on these results in three ways: (1) We aim to refine the methods we use to test whether increased effort and/or believability help explain why sharing reappraisals can make them more effective. Across studies 1-2, we found qualitative evidence that sharing reappraisals (via saying or writing) was more effortful than thinking reappraisals and that participants perceived their reappraisals as more believable when they shared them than when they just thought about them. In study 2, we additionally found quantitative evidence using retrospective single-item measures that participants found shared reappraisals more effortful and believable than thought reappraisals. However, these variables did not statistically mediate the association between reappraisal modality and reappraisal efficacy. This lack of mediation could be attributable to measurement differences (i.e., negative affect measured at the trial level vs. single retrospective measures of effort/believability for each condition). Thus, in the present study we will collect measures of effort and believability at the trial level, in addition to related questions at the of the study (see Dependent Variable section). This will allow us to test whether effort and believability might explain differences in efficacy across reappraisal conditions. This is the primary aim of the current study (i.e., testing mechanism). (2) We aim to provide stronger evidence that participants are indeed engaging in reappraisal in our reappraisal conditions by including a non-reappraisal control condition. Across studies 1-2, to ensure participants were attentive and responsive to the task across all reappraisal conditions, we conducted an extensive quality assurance procedure (described under “Participants” section). Additionally, we conducted a manipulation check by compiling participants’ average negative affect ratings of images in each reappraisal condition to compare with the average valence ratings of the images while passively viewing (i.e., without reappraisal) using the pre-tested valence ratings provided by OASIS (Kurdi, Lozano, & Banaji, 2017). We found that average ratings during the reappraisal conditions induced about half as much negative affect as normative responses to our task images (according to OASIS norms). This provides evidence that participants were engaging with the task and using reappraisal to reduce negative affect across conditions. Nonetheless, such post-hoc comparisons have their weaknesses, so in the present study we will include a baseline condition with no reappraisal (i.e., a “look” condition) to compare reappraisal conditions against. Given that we found no differences between writing and saying reappraisals, we will only include the following three conditions in the present study: think reappraisal, say reappraisal, and no reappraisal. (3) We aim to explore the role of individual difference moderators. In study 2, we found that individual differences in emotional sharing, as indexed by the Interpersonal Regulation Questionnaire (IRQ; Williams et al., 2018), moderated the association between reappraisal modality and negative affect. Those who reported sharing more with others to regulate emotions in daily life (i.e., higher IRQ scores) experienced greater benefits of sharing during the task (i.e., greater difference between sharing and not sharing reappraisals). In the present study, we will attempt to replicate this result and examine the specificity of this result by also examining a few additional possible moderators related to emotion regulation and expressivity (i.e., Emotion Regulation Questionnaire – reappraisal subscale only, Gross & John, 2003; Emotional Expressivity Scale, Kring, Smith, & Neale, 1994; and the Toronto Alexithymia Scale – describing emotions subscale only, Bagby et al., 1994).

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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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0150.007
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.336
Teacher spread0.305 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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