Link between initial fixation location and spatial frequency utilization in face recognition
Bibliographic record
Abstract
Recent face perception studies have explored individual differences with regard to visual processing strategies. Two main strategies, associated with distinct eye movement patterns, have been highlighted: global (or holistic) face processing involves fixations near the center of the face to facilitate simultaneous peripheral processing of key facial features (i.e. eyes and mouth); local (or analytic) face processing involves fixations directed to those facial features (Chuk et al, 2014; Miellet et al, 2011). Since it has been shown that peripheral processing entails lower spatial resolution (Goto et al, 2001), global face processing may theoretically be linked to lower spatial frequency (SF) sampling. By contrast, local face processing may allow the extraction of higher SFs. However, the link between eye movements and SF extraction has not yet been empirically verified for face recognition. Thus, the current study proposes to investigate this question. The eye movements of 21 Canadian participants were monitored while they completed an Old/New face recognition task. Subsequently, the SF Bubbles method (Willenbockel et al., 2010) was used to measure the same participants’ SF utilization during a face identification task. Fixation duration maps were computed for each participant using the iMap4 toolbox (Lao et al., 2017), and participants’ individual SF tuning peaks, obtained with SF Bubbles, were calculated. In line with previous studies, our participants’ initial fixations generally landed near the center of the face, with varying degrees of proximity to this location (Euclidean distances: from 25.76 to 146.03 pixels; SD of 32.08 pixels). Crucially, SF peaks significantly correlated with the location of the first fixation (r = −0.46; p = 0.037): participants using higher SFs initially gazed closer to the left eye than participants using lower SFs. These results suggest that greater reliance on high SFs is associated with early fixations toward the left eye 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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".