Young Children Use Probability to Infer Happiness and the Quality of Outcomes
Bibliographic record
Abstract
Happiness with an outcome often depends on whether better or worse outcomes were initially more likely. In five experiments, we found that young children ( N = 620, Experiments 1–4) and adults ( N = 254, Experiment 5) used probability to infer emotions and assess outcome quality. In Experiments 1 and 2, 5- and 6-year-olds (but not 4-year-olds) inferred that an agent would be less happy with an outcome if a better outcome were initially more likely. In Experiment 3, 4- to 6-year-olds used probability to assess quality. These findings suggest a developmental lag between 4-year-olds’ assessments of quality and happiness. We replicated this lag in Experiment 4. In Experiment 5, adults used probability to assess both quality and happiness. We suggest that children and adults may use probability to establish a standard against which actual outcomes are compared. Doing so might allow them to make probability-based inferences of happiness without drawing on counterfactual reasoning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".