Effects of Image Background on Spatial-Frequency Thresholds for Face Recognition
Bibliographic record
Abstract
A great deal of work has been devoted to the question of which spatial frequencies, if any, are optimal for various visual tasks, such as face and object recognition. However, to date these studies have all been carried out with stimuli set against a uniform background. It is possible that this type of stimulus does not produce ecologically valid results. The natural world in which visual tasks normally take place involves a great deal of luminance variation and distracting visual structure, which may alter the spatial frequencies necessary for a task. We conducted two experiments that examined the effects of image background on the spatial-frequency thresholds (50% maximum of a low-pass or high-pass Butterworth filter) for face recognition by the psychophysical methods of adjustment and constant stimuli. In both experiments we found no significant difference in spatial-frequency thresholds between uniform-grey backgrounds and natural-scene backgrounds, and only minor differences between uniform-grey backgrounds and fractal noise backgrounds. This suggests that results obtained with uniform backgrounds are ecologically valid and that background effects, if they exist, are small.
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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 | 0.005 |
| 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".