Charitable and memorable? Probing the effects of prosocial decisions on face memory in younger and older adults
Bibliographic record
Abstract
Separate lines of evidence suggest that prosocial motives are increasingly prioritized with adult age, and that episodic memory remains sensitive to motivational influences throughout the adult lifespan. The current study was designed to examine the potential interplay of prosociality and episodic memory. By pairing a financial decision-making task with an incidental face recognition task, we examined the influence of prosocial decision-making on subsequent memory for associated information (i.e. faces) in healthy younger and older adults (N = 128). During the decision-making task, participants were presented with hypothetical financial transfers from their own account to a food bank charity, which was represented by the face of a food bank client. Prosocial reward was operationalized as the amount transferred to the charity per trial. During a subsequent surprise memory test, participants were asked to discriminate between faces seen during the decision-making task and new faces. Results revealed that older adults were more satisfied by, and more likely to accept, charitable transfers than younger adults, but there were no effects of prosocial reward on face recognition performance in either age group. These findings suggest that incidental encoding may not be sensitive to transient affective states associated with prosocial decisions in younger and older adults.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".