The Bandwidth of Diagnostic Horizontal Structure for Face Identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it