When is giving an impulse? An ERP investigation of intuitive prosocial behavior
Bibliographic record
Abstract
Human prosociality is often assumed to emerge from exerting reflective control over initial, selfish impulses. However, recent findings suggest that prosocial actions can also stem from processes that are fast, automatic and intuitive. Here, we attempt to clarify when prosocial behavior may be intuitive by examining prosociality as a form of reward seeking. Using event-related potentials (ERPs), we explored whether a neural signature that rapidly encodes the motivational salience of an event-the P300-can predict intuitive prosocial motivation. Participants allocated varying amounts of money between themselves and charities they initially labelled as high- or low-empathy targets under conditions that promoted intuitive or reflective decision making. Consistent with our predictions, P300 amplitude over centroparietal regions was greater when giving involved high-empathy targets than low-empathy targets, but only when deciding under intuitive conditions. Reflective conditions, alternatively, elicited an earlier frontocentral positivity related to response inhibition, regardless of target. Our findings suggest that during prosocial decision making, larger P300 amplitude could (i) signal intuitive prosocial motivation and (ii) predict subsequent engagement in prosocial behavior. This work offers novel insight into when prosociality may be driven by intuitive processes and the roots of such behaviors.
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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 | 0.003 |
| 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 | 0.000 |
| 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".