Age-related differences in preferred fixated region (PFR) for face perception
Bibliographic record
Abstract
Face regions differ in their information value for face discrimination, with more information in eye compared to nose. Despite this information difference, individuals vary in their preferred fixation region (PFR) when viewing faces: There are eye-lookers and nose-lookers. This could reflect individual differences in optimal processing regions for face perception. Peterson & Eckstein (2013) found nose-lookers performed better when viewing the nose rather than eyes, although results vary across studies (cf. Arizpe et al., 2016). Older adults as a group fixate on the lower halves of faces more than younger adults (e.g., Firestone et al. 2007), and older adults show reduced face perception accuracy (e.g., Konar et al., 2013). Here, we ask: Are nose-lookers more prevalent among older adults? and Is the link between PFR and optimal processing the same in older and younger adults? We investigated PFR in 27 younger and 27 older adults, examining face perception accuracy under free-viewing and region-restricted viewing (forehead, eye, nose, and mouth), following the method of Peterson & Eckstein (2013). Preliminary findings revealed a higher proportion of nose-lookers among older (50%) than younger adults (20%). Older observers generally showed worse performance accuracy than younger adults, but performance between eye-lookers and nose-lookers did not differ for each age group. Overall patterns of performance were similar across age groups for eye-lookers, with peak performance for the eye, and relatively poor performance for forehead and mouth. Older nose-lookers showed a clear benefit for the nose, but younger nose-lookers showed strong performance for both eyes and nose. All nose-lookers showed strong performance for the mouth. These findings suggest that age-related changes in PFR do not fully explain reduced face perception in older adults. We currently are analyzing EEG measures to learn how neural activities associated with face detection (N170) and identification (N250) relate to PFR across adulthood.
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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".