The Effect of Fixation Location on Face Perception in Younger and Older Adults
Bibliographic record
Abstract
Information of different facial features is distributed in various facial regions. For instance, the eye region, compared to other areas, is particularly informative for identity perception. However, not all observer focus on the eye region for this purpose. There are inter-individual differences in the region in which younger individuals fixate for identity perception: some preferred the eyes, the nose for the others. Fixation patterns also differ between age groups: older adults have more fixations on the lower half of the face compared to younger adults. However, when fixation is restricted to specific regions, younger adults demonstrated the best identity perception when fixation was restricted to the eye. Where do younger and older adults look on faces, and can they show improved performance in recognizing faces when focusing on specific facial regions? The first objective of this study is to describe and compare the optimal fixation locations of younger and older observers. The second objective is to investigate how restricting fixation location interacts with age on face perception. Each trial started with the presentation of a fixation square, followed by the target face, and a face selection task. The off-face condition began with a fixation square outside the face’s anticipated display area. The on-face condition started with a fixation square on the forehead, eye, nose, or mouth. Preliminary analysis showed age-related in both conditions (20 younger and 20 older adults). Restricting the fixation location seems to reduce these age-related differences, with older adults showing improved performance, particularly at the eye and nose locations. Notably, an age-related difference emerged at the nose location, where older adults benefited more from nose restriction fixation than younger adults. Both younger and older adults showed greater performance in face perception when the initial fixation was directed to the eye and the nose region.
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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 | 0.004 |
| 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".