The use of spatial frequency through time in face identification
Bibliographic record
Abstract
Face perception has received much attention since it became obvious that something special characterizes this stimulus class (e.g. Bodamer, 1947; Farah et al., 1998 ; Yin, 1969). One promising avenue for the study of face perception involves psychophysical procedures that can determine the information effectively used by human observers. Here we studied the effective use of spatial frequency information through time in face identification using Bubbles (Gosselin & Schyns, 2001; Vinette, Gosselin & Schyns, 2004). We submitted five subjects to 3000 dynamic grayscale faces (6 × 6 deg of visual angle × 180 ms) sampled by dot multiplying their Fourier spectrum with a 2D white Gaussian noise convolved with a Gaussian function (Std's = 0.156 of the Nyquist frequency and 79 ms). The subjects performance was maintained at 75% of correct identifications by adjusting, on a trial by trial basis, the surface under the sampling noise. Using multiple linear regression on response accuracy and sampling noise, we revealed that subjects tend to use a narrow band of low spatial frequency (about 5.8 cycles per face) throughout and, from 100 to 150 ms after stimulus onset, a broader frequency band centered on 15.6 cycles per face. These results suggest that face identification occurs in a two-step process : an initial sweep mainly interested in low spatial frequencies and a later one focussing on mid to high spatial frequencies, which appears particularly important for the efficient resolution of the perceptual task. This two-sweep process is compatible with the proposal of Liu, Harris and Kanwisher (2002) that face identification at an individual level follows a more global categorization of the stimulus as a face. Results are also in agreement with studies that showed a natural bias in face perception for spatial frequencies between 5.62 and 22.5 cycles per face (e.g. Nasanen, 1999; Schyns, Bonnar & Gosselin, 2002 ; Vuilleumier et al., 2003).
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".