Eyes on the target: A comparison of fine-grained sensitivity to triadic gaze between 8-year-olds and adults
Bibliographic record
Abstract
Adults are able to determine which object in the environment someone is looking at with high precision (triadic gaze). By age 6, children can detect large (10°) differences in triadic gaze (Doherty et al., 2009). Here, we developed a child-friendly procedure to compare sensitivity to small horizontal differences in triadic gaze between 8-year-olds and adults (n = 18/group). Participants sat in front of a computer monitor on which they saw faces fixating a series of points (separated by 1.6°) that were physically marked on a board halfway between them and the monitor. The task was to indicate whether each face appeared to be looking to the left or right of one of three target points (center, 6.4° left or 6.4° right). All participants were at least 75% correct on a practice block completed before each experimental block. Adults were highly sensitive to deviations from the central target, with a mean error of 0.83° (calculated from the .25 and .75 points on the fitted psychometric curves). 8-year-olds were not as sensitive (M error = 2.05°, p <.0001). When the targets were peripheral, participants overestimated the degree to which the face was looking toward the periphery (e.g., judging the face to be looking to the left of the left target), with a larger error in children (M = 2.26°) than in adults (M = 0.91°, p <.05). Relative to 8-year-olds, 10-year-olds (n = 10 tested to date) appear to show a more adult-like pattern characterized by a steeper slope and a more adult-like asymptote. These results indicate for the first time that by age 8, children can detect small differences in triadic gaze, but that sensitivity is not yet as refined as it will become in adulthood.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".