Different Spatial Frequency Tuning for Judgments of Eye Gaze and Facial Identity
Bibliographic record
Abstract
Humans use the direction of people's gaze as a cue to their mental and emotional states. Human adults are most efficient in using low (coarse details) to mid (finer details) spatial frequencies to discriminate facial identities (Gao & Maurer, 2011). Adults are highly sensitive to changes in the direction of gaze (Vida & Maurer, 2012a). Here we tested whether adults' sensitivity to eye gaze, like their sensitivity to identity, is tuned to a limited range of spatial frequencies. In Experiment 1, participants (n=4) viewed faces presented with filtered noise that masked one of 10 narrow spatial frequency bands, with the centre frequency of the noise varying between blocks. Participants discriminated between two male faces or two female faces, or between gaze shifted to the left or right by 4.8° or 8°. We measured participants' contrast thresholds for each task, and used an ideal observer analysis to evaluate the importance of each frequency band for human sensitivity, taking into account the amount of information available to perform the task. For judgments of identity, participants relied on low to mid frequencies, but not on higher frequencies, a pattern consistent with previous studies (Gao & Maurer, 2011). For eye gaze, a small range of mid to high frequencies was most important, and the highest frequency important for gaze was higher than that for identity. In Experiment 2, participants (n=6) discriminated among horizontal and among vertical shifts of gaze. The most important frequencies were the same as for judgments of gaze in Experiment 1. However, the surrounding frequencies were less important for horizontal than vertical judgments, a result that may reflect finer tuning for horizontal judgments. Together, these results provide the first evidence that sensitivity to gaze is tuned to higher spatial frequencies than sensitivity to facial identity. Meeting abstract presented at VSS 2014
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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".