Does shrinking the perceptual field of view affect horizontal tuning in upright face identification?
Bibliographic record
Abstract
The face inversion effect (FIE) is characterized by an important drop in recognition performance when facial stimuli are rotated by 180° in the picture plane. Pachai and coll. (2013) showed that inversion disrupts the processing of horizontal information (see also Goffaux & Dakin, 2010) and reported a significant positive correlation between horizontal tuning and the magnitude of the face inversion effect. Recently, Van Belle & Rossion (2015) showed that face inversion reduces the size of the perceptual field of view (PFV). This offers an elegant explanation for the performance drop with inverted faces since a small PFV restricts feature extraction to only a few (maybe one) at a time; a proposition reminiscent of the holistic hypothesis. To make the link between the lack of horizontal tuning with inverted faces and the PFV hypothesis, we measured orientation tuning in five participants for upright faces presented either through a small aperture (a gaze-contingent approach), or as a whole. First, the participants were asked to learn the face-name association for 10 identities. They practiced in each condition until they reached an accuracy of 95%. In the second phase, images were randomly filtered in the orientation domain with orientation bubbles (Duncan et al., 2014) to precisely reveal orientation utilization. Participants performed 400 trials per condition. The signal-to-noise ratio was adjusted so that the same performance level (55%) was obtained in both conditions. Congruently with what was observed for FIE, the signal-to-noise ratio was significantly higher when faces were presented through a small aperture than as a whole [t(4) = 12.9, p < 0.001]. Despite this large effect, the small aperture condition is not linked to a decrease in horizontal tuning. Our results show that the smaller PFV associated with the FIE cannot explain the lack of horizontal tuning with inverted faces. Meeting abstract presented at VSS 2016
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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 | 0.004 |
| 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) | 0.003 | 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".