Generalizing across moral sub-domains: infants bidirectionally link fairness and unfairness to helping and hindering
Bibliographic record
Abstract
Across two experiments, we investigated whether infants use prior behavior to form expectations about future behavior within the moral domain, focusing on the sub-domains of fairness and help/harm. In Experiment 1, 14- to 27-month-old infants were familiarized to an agent who either helped or hindered another agent to obtain her goal. At test, infants saw the helper or hinderer perform either a fair or unfair distribution of resources to two recipients. Infants familiarized to helping looked longer to the unfair distribution than the fair distribution at test, whereas infants familiarized to hindering looked equally at both test events, suggesting that hindering led infants to suspend baseline expectations of fairness. In Experiment 2, infants saw these events in reverse. Following familiarization to fair behavior, infants looked equally to helping and hindering; in contrast, following familiarization to unfair behavior, infants looked significantly longer to helping than hindering on test, suggesting that prior unfair behavior led infants to expect the agent to hinder another agent's goals. These results suggest that infants utilize prior information from one moral sub-domain to form expectations of how an individual will behave in another sub-domain, and that this tendency seems to manifest more strongly when infants initially see hindering and unfair distributions than when they see helping and fair distributions. Together, these findings provide evidence for consilience within the moral domain, starting by at least the second year of life.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".