Mechanisms of saying versus thinking reappraisals (study 3)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,015 | 0,007 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».