Infants’ Looking to Surprising Events: When Eye-Tracking Reveals More than Looking Time
Bibliographic record
Abstract
Research on infants' reasoning abilities often rely on looking times, which are longer to surprising and unexpected visual scenes compared to unsurprising and expected ones. Few researchers have examined more precise visual scanning patterns in these scenes, and so, here, we recorded 8- to 11-month-olds' gaze with an eye tracker as we presented a sampling event whose outcome was either surprising, neutral, or unsurprising: A red (or yellow) ball was drawn from one of three visible containers populated 0%, 50%, or 100% with identically colored balls. When measuring looking time to the whole scene, infants were insensitive to the likelihood of the sampling event, replicating failures in similar paradigms. Nevertheless, a new analysis of visual scanning showed that infants did spend more time fixating specific areas-of-interest as a function of the event likelihood. The drawn ball and its associated container attracted more looking than the other containers in the 0% condition, but this pattern was weaker in the 50% condition, and even less strong in the 100% condition. Results suggest that measuring where infants look may be more sensitive than simply how much looking there is to the whole scene. The advantages of eye tracking measures over traditional looking measures are discussed.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".