There can be only one: Change detection is better for singleton faces, but not for faces in general
Bibliographic record
Abstract
Change detection is a powerful tool to study visual attention to objects and scenes because successful change detection requires attention. For example, people are better able to detect a change to the only face in an array than they are changes to other objects (Ro et al., 2001), suggesting that faces draw attention. To the extent that such attention advantages depend on experience, they might vary with age. Our study had two primary goals: (a) to explore the nature and limitations of the change detection advantage for faces, and (b) to determine whether that advantage changes with age and experience. Children, ages 7 to 12 years, viewed an original and changed array of objects that alternated repeatedly, separated by a blank screen, until they detected the one changing object. The arrays consisted of varying numbers of faces or houses, any one of which could change to another exemplar from the same category. Consistent with earlier work, in the presence of a singleton face, changes to that face were detected more quickly and changes to houses in the array were detected more slowly, suggesting that the singleton face drew attention. This advantage was specific to faces–singleton houses show no benefit. However, the advantage for faces occurred only for singleton faces–when multiple faces were present in the display, change detection was no better for faces than for houses. This singleton advantage for faces was present for all age groups even though older subjects showed better overall change detection performance. Apparently, people prioritize single faces over other objects, but they do not generally prioritize faces over other objects when multiple faces appear in a display.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".