Face Perception: Importance of phase alignments
Bibliographic record
Abstract
Complex patterns, such as faces, can be described by the combination of their Fourier frequency and phase components. Whereas the role of frequency information has received the majority of research attention, the importance of phase information in face perception has been largely neglected. In the experiments reported here, we sought to investigate the role of phase information on face perception using a discrimination task on arrays of face morphs. In the first experiment, we varied the amount of aligned Fourier phase in different regions of the face frequency spectrum in order to determine whether the information in some regions was more important than others and whether the properties of the underlying neural processes are best understood in terms of frequency bandwidth or number of phase-alignments. In the second experiment, linear filtering was implemented to estimate the information content in different face frequency bands and to determine whether it is the number of phase-alignments or the signal-to-noise ratio of phase-alignments that matter. In the third experiment, we varied the distribution of phase-aligned frequencies to ascertain whether it is the number of contiguous phase-aligned frequencies or the global signal-to-noise ratio that matters. We conclude that there are underlying processes that depend on a certain signal-to-noise ratio of phase-alignments within a contiguous range of face frequencies (we termed these critical band of phase alignments) which operate with equal efficiency throughout the face frequency spectrum.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".