What types of gratitude expressions promote prosocial behavior?: A registered report
Bibliographic record
Abstract
Prosociality confers benefits to those who engage in the behavior, as well as those who receive it. So, how can prosociality, especially repeated acts of giving, be encouraged? Past research shows that receiving an expression of gratitude can promote prosocial behavior. However, existing work has focused on the benefits of receiving gratitude in comparison to receiving nothing, thereby leaving the content of gratitude expressions unexamined. Drawing upon past research demonstrating the benefits of identity-relevant appeals, we compare how two different gratitude expressions – one that references an individual's kind personality (i.e., prosocial character ) and another that references an individual's kind actions (i.e., prosocial action ) – impact future prosocial behavior in comparison to each other and to a no-gratitude control condition using two high-powered pre-registered studies. In both studies, participants (603 undergraduate students in Experiment 1; 876 online adult sample in Experiment 2) engaged in a prosocial task and were then randomly assigned to receive one of two gratitude expressions or a no-gratitude control condition. Afterwards, participants had the opportunity to help again. Comparing helping rates across conditions we found no significant differences among helping behavior in Experiment 1. However, in Experiment 2, results indicated that individuals who received an expression of gratitude, regardless of content, donated more than individuals that solely received an acknowledgement of their help. We interpret these findings as mixed support for gratitude's function as a moral reinforcer and consider why gratitude expressions referencing an individual's generous character vs. action may have had little impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".