Balancing Prosocial Effort Across Social Categories: Mental Accounting Heuristics in Helping Decisions
Bibliographic record
Abstract
Three studies examine whether individuals might use mental accounting heuristics in helping decisions, budgeting their prosocial effort in similar ways to how money is budgeted. In a hypothetical scenario study ( N = 283), participants who imagined that they previously helped someone of a specific social category (e.g., “family,” “colleagues”) were less willing to help someone of that category again. Similarly, when reporting actual instances of day-to-day help in a diary study ( N = 443), having helped more than usual in a social category yesterday was associated with less effort and less time spent on helping in the same category today. In contrast, helping more than usual in other social categories did not reduce helping today. Finally, a scenario study ( N = 489) suggested that the mental accounting effect in helping decisions may, in part, be explained by perceived utility of help (helping others in the same social category is seen as less rewarding).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".