The mechanisms of social evaluation in infancy: A preregistered exploration of infants' eye-movement and pupillary responses to prosocial and antisocial events
Bibliographic record
Abstract
Past research shows infants selectively touch and look longer at characters who help versus hinder others (Hamlin et al., 2007; 2010); however, the mechanisms underlying this tendency remain under-specified. The current preregistered experiment approaches this question by examining infants’ real-time looking behaviors during prosocial and antisocial events, and exploring how individual infants’ looking behaviors correlate with helper preferences. Using eye-tracking, 34 five-month-olds were familiarized with two blocks of the “hill” scenario originally developed by Kuhlmeier et al., (2003), in which a climber tries unsuccessfully to reach the top of a hill and is alternately helped or hindered. Infants’ visual preferences were assessed after each block of 6 helping and hindering events by proportional looking time to the helper versus hinderer in an image of the characters side-by-side. Results showed that, at the group level, infants looked longer at the helper after viewing 12 (but not after viewing 6) helping and hindering videos. Moreover, individual infants’ average preference for the helper was predicted by their looking behaviors, particularly those suggestive of an understanding of the climber’s unfulfilled goal. These results shed light on how infants process helping/hindering scenarios, and suggest that goal understanding is important for infants’ helper preferences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".