Shifting prosocial intuitions: neurocognitive evidence for a value-based account of group-based cooperation
Bibliographic record
Abstract
Cooperation is necessary for solving numerous social issues, including climate change, effective governance and economic stability. Value-based decision models contend that prosocial tendencies and social context shape people's preferences for cooperative or selfish behavior. Using functional neuroimaging and computational modeling, we tested these predictions by comparing activity in brain regions previously linked to valuation and executive function during decision-making-the ventromedial prefrontal cortex (vmPFC) and dorsolateral prefrontal cortex (dlPFC), respectively. Participants played Public Goods Games with students from fictitious universities, where social norms were selfish or cooperative. Prosocial participants showed greater vmPFC activity when cooperating and dlPFC-vmPFC connectivity when acting selfishly, whereas selfish participants displayed the opposite pattern. Norm-sensitive participants showed greater dlPFC-vmPFC connectivity when defying group norms. Modeling expectations of cooperation was associated with activity near the right temporoparietal junction. Consistent with value-based models, this suggests that prosocial tendencies and contextual norms flexibly determine whether people prefer cooperation or defection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".