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

Mechanisms of saying versus thinking reappraisals (study 3)

2025· other· en· W7079600207 sur OpenAlexaboutno aff

Notice bibliographique

RevueOpen Science Framework · 2025
Typeother
Langueen
DomaineComputer Science
ThématiqueGeochemistry and Geologic Mapping
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMediationAffect (linguistics)Test (biology)Empirical researchModality (human–computer interaction)

Résumé

récupéré en direct d'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).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,027
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,036

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,027
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,003
Communication savante0,0040,005
Science ouverte0,0010,003
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,031
Tête enseignante GPT0,336
Écart entre enseignants0,305 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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