The Odds Tell Children What People Favor
Bibliographic record
Abstract
In pursuing goals, people seek favorable odds. We investigated whether young children use this fact to infer goals from people’s actions across two experiments on Canadian 3-7-year-olds (total N = 316, 167 girls and 149 boys). Participants’ demographic information was not formally collected, but the region is predominantly middle-class and White. In Experiment 1, 3-year-olds saw a story where one agent went to a gumball machine with mostly red gumballs and another agent went to a machine with mostly purple ones. When asked which agent wanted a red gumball, children mostly selected the agent who chose the mostly-red machine. Moreover, children responded at chance in a control condition where they judged which agent knew they would get a red gumball. In Experiment 2a, 3-7-year-olds saw a story where an agent either chose between two gumball machines or two open bowls of gumballs. In both conditions, the agent chose a location with mostly red gumballs over one with mostly blue gumballs, but ended up with a blue gumball. Children were more likely to infer the agent had wanted a red gumball when the agent had made a probabilistic choice (machines) than a determinative choice (bowls), though inferences that the red gumball was preferred also increased with age. In Experiment 2b, a preregistered follow-up, American adults responded similarly to the older children. Together our findings suggest that children infer goals by drawing on the understanding that people seek favorable odds, though the clearest findings come from children aged 6 years and older.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".