Episodic simulation of helping behavior in younger and older adults during the COVID-19 pandemic.
Bibliographic record
Abstract
Imagining helping a person in need increases one's willingness to help beyond levels evoked by passively reading the same stories. We examined whether episodic simulation can increase younger and older adults' willingness to help in novel scenarios posed by the COVID-19 pandemic. Across 3 studies we demonstrate that episodic simulation of helping behavior increases younger and older adults' willingness to help during both everyday and COVID-related scenarios. Moreover, we show that imagining helping increases emotional concern, scene imagery, and theory of mind, which in turn relate to increased willingness to help. Studies 2 and 3 also showed that people produce more internal, episodic-like details when imagining everyday compared to COVID-related scenarios, suggesting that people are less able to draw on prior experiences when simulating such novel events. These findings suggest that encouraging engagement with stories of people in need by imagining helping can increase willingness to help during the pandemic.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".