Effects of Age and Fixation Location on Face Identification
Bibliographic record
Abstract
Face identification declines with age, potentially because older adults (OA) make less use of information around the eyes, which has been shown to drive face identification accuracy. Previous studies using the forced initial fixation approach in younger adults (YA; Peterson & Eckstein, 2013) showed that face identification accuracy could be modulated by initial fixation point. The current study used the forced initial fixation approach with both YA and OA to investigate whether fixating towards the eye and eyebrow region could improve face recognition performance in older adults. 27 YA (M = 25.7, SD = 0.37) and 41 OA (M = 61.1, SD = 5.28) completed this online study. Ten target faces of Caucasian females were selected from the FACES database. On each trial, participants view a fixation point (500 ms) located in one of four randomly, presented locations distributed equidistant between the forehead and the mouth. Participants then viewed a target face for 200 ms, followed by a post-target mask, after which participants chose the target face out of ten options. Overall, OA showed worse face identification performance than YA. Both age groups showed a similar overall pattern of results as found in Peterson and Eckstein (2013), with best performance for fixation points near the eyes and nose; however, age group also interacted with fixation condition: OA showed greater improvement in face recognition when the initial forced location moved downwards from the forehead to the eye or nose regions, whereas the performance of YA improved by a much smaller amount, and performance differences between YA and OA varied considerably across different initial fixation points. These results suggest that altering initial fixations and/or attentional focus to more informative regions of the face may provide a mechanism to enhance face perception in OA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".