Intuitive cooperators: Time pressure increases children's cooperative decisions in a modified public goods game
Bibliographic record
Abstract
There is mounting empirical evidence to suggest that adults are intuitively cooperative. When presented with a cooperative dilemma between self-maximizing and benefitting the common good, decisions made quickly are more likely to be cooperative, whereas slow decisions tend to favor self-interest. To investigate the ontogenetic origins of intuitive cooperation, we examined the development of intuitive cooperation in middle childhood. We presented 150 children (7-12 years of age) with an online child-friendly public goods game where participants had a choice between giving two resources to themselves or four to their group. Participants were assigned to one of three decision time conditions; speeded, neutral, or delayed. We found that when decisions were speeded, children were more likely to cooperate compared to when decisions were unconstrained or delayed. Furthermore, children's intuitive choices only favored cooperation if they believed their peers were also cooperative. This pattern of findings held across the age range included in this study. Our findings suggest that in middle and late childhood, children are intuitively cooperative when making decisions to benefit the common good. HIGHLIGHTS: Time pressure increases children's cooperation in a public goods game, compared to when decisions are delayed or unconstrained. Between 7 and 12 years of age children engage in costly cooperation most of the time regardless of decision time. When children believe others are generally cooperative, their intuition is to cooperate. From middle to late childhood, intuitive decisions favor costly cooperation towards the common good.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".