Thanks for Nothing: Expressing Gratitude Invites Exploitation by Competitors
Bibliographic record
Abstract
Previous research has revealed that expressing gratitude motivates prosocial behavior in cooperative relationships. However, expressing gratitude in competitive interactions may operate differently. Across five studies, we demonstrate that individuals interacting with grateful counterparts become more likely to engage in selfish behavior during competitive interactions. In Studies 1a and 1b, participants who interacted with counterparts expressing gratitude were more likely to make aggressive offers in distributive negotiations than those who interacted with counterparts expressing neutral emotion. In Study 2, we find that inferences of the tendency to forgive mediates the relationship between gratitude expression and selfish behavior. In Study 3, we contrast expressions of gratitude with another positive-valence emotion: excitement. We show that expressing gratitude promotes self-interested behavior compared to expressing excitement or neutral emotion. In Study 4, we find that gratitude expression triggers self-serving deception. Taken together, our findings suggest that expressing gratitude can be costly in competitive interactions: people infer that grateful counterparts are forgiving and, therefore, they are more likely to exploit their counterparts for selfish gain.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".