“Are You <i>Really</i> Sad?” Infants Show Selectivity in Their Behaviors Toward an Unconventional Emoter
Bibliographic record
Abstract
We examined whether 18‐month‐olds understand how the emotional valence of people's experiences predicts their subsequent emotional reactions, as well as how their behaviors are influenced by the reliability of the emoter. Infants watched a person express sadness after receiving an object that was either inappropriate (conventional emoter) or appropriate (unconventional emoter) to perform an action. Then, infants’ imitation, social referencing, and prosocial behaviors (helping) were examined when interacting with the person. Results showed that during the exposure phase, the unconventional group showed visual search patterns suggesting hypothesis testing and expressed less concern toward the person than the conventional group. In the social referencing task, the conventional group preferred to search for the target of a positive expression as opposed to the disgust object. In contrast, the unconventional group was more likely to trust the person's negative expression. As expected, no differences were found between the groups on the instrumental helping tasks. However, during the empathic helping tasks, the conventional group needed fewer prompts to help than the unconventional group. These findings provide the first evidence that the congruence between a person's emotional responses and her experiences impacts 18‐month‐olds’ subsequent behaviors toward that person.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".