Embodied remorse: Physical displays of remorse increase positive responses to public apologies, but have negligible effects on forgiveness.
Bibliographic record
Abstract
Public apologies struggle to communicate genuineness. Previous studies have shown that, in response to public apologies, perceptions of remorse and levels of forgiveness are often low, while skepticism about motive is high. Furthermore, attempts to reduce mistrust of public apologies by manipulating the verbal component of the message have had limited success. Across 6 studies (combined N = 3,818), we examined whether people respond more positively to public apologies if the apologies are accompanied by nonverbal demonstrations of remorse: operationalized as kneeling (Studies 1 and 6) and crying (Studies 2-5). Overall, embodied remorse had small-to-medium effects on perceived remorse, and through this relationship had reliable effects on perceived likelihood of reoffending, empathy, positive appraisals of the transgressor, and satisfaction with the apology. Positive effects of embodiment emerged regardless of whether transgressions were committed by a collective (Studies 1, 2, and 6) or an individual (Studies 3-5), and were equally strong regardless of whether or not the transgressor issued an apology (Studies 4 and 5). Furthermore, embodied remorse appeared to lie beyond suspicion: if anything, those low in dispositional trust were more positively influenced by embodied remorse than those high in dispositional trust. Despite all these positive effects, embodied remorse did not have a significant effect on forgiveness in any of the studies, and an internal meta-analysis revealed a significant effect that was of negligible size. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".