The Bandwidth of Diagnostic Horizontal Structure for Face Identification
Bibliographic record
Abstract
Horizontally oriented spatial frequency components are a diagnostic source of face identity information, and sensitivity to this information predicts upright identification accuracy and the magnitude of the face-inversion effect. However, the bandwidth at which this information is conveyed, and the extent to which human tuning matches this distribution of information, has yet to be characterized. We designed a 10-alternative forced choice face identification task in which upright or inverted faces were filtered to retain horizontal or vertical structure. We systematically varied the bandwidth of these filters in 10° steps and replaced the orientation components that were removed from the target face with components from the average of all possible faces. This manipulation created patterns that looked like faces but contained diagnostic information in orientation bands unknown to the observer on any given trial. Further, we quantified human performance relative to the actual information content of our face stimuli using an ideal observer with perfect knowledge of the diagnostic band. We found that the most diagnostic information for face identification is conveyed by a narrow band of orientations along the horizontal meridian, whereas human observers use information from a wide range of orientations.
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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 | 0.001 | 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".