Imagination and the prosocial personality: Mapping the effect of episodic simulation on helping across prosocial traits
Bibliographic record
Abstract
Abstract Prior work suggests that imagining helping others increases prosocial intentions and behavior toward those individuals. But is this true for everyone, or only for those who tend toward—or away from—helping more generally? The current study (N = 283) used an imagined helping paradigm and a battery of behavioral and self‐report measures of trait prosociality to determine whether the prosocial benefits of imagination depend upon an individual's general tendency to help others. Replicating prior work, we found links between imagination and prosociality and support for a three‐factor model of prosociality comprising altruistically, norm‐motivated, and self‐reported prosocial behaviors. Centrally, the effects of imagination on prosociality were slightly larger forlessaltruistic individuals but independent of norm‐motivated and self‐reported prosociality. These results suggest leveraging people's abilities for episodic simulation as a promising strategy for increasing prosociality in general, and perhaps particularly for those least likely to help otherwise.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".