Can the Prosocial Benefits of Episodic Simulation Transfer to Different People and Situational Contexts?
Bibliographic record
Abstract
Previous research has found that episodic simulation of events of helping others can effectively enhance intentions to help the same person involved and the identical situational context as the imagined scenarios. However, to date, no study has examined systematically whether this ‘prosocial simulation effect’ can be transferred to response scenarios involving different people and/or situational contexts to the imagined scenarios. Here, we investigated this question across two experiments, systematically varying the degree to which the simulated scenario differed from the response scenario, both in terms of the persons in need and/or the situational contexts. Moreover, we examined whether the degree of overlap in simulated and response scenarios would influence the magnitude of prosocial simulation effect. Results from both experiments showed that the prosocial simulation effect can be transferred to response scenarios involving different people and situational contexts to the simulated scenarios. However, this finding was primarily driven by response scenarios that had a high degree of overlap (i.e., similar targets and situational contexts) to the simulated scenarios. We discuss potential mechanisms underlying the transferability of the prosocial simulation effect and consider the implication of our findings to the practical implementation of this effect to promote prosociality in the real world.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".