Spatial-Frequency Thresholds for Configural and Featural Discriminations in Upright and Inverted Faces
Bibliographic record
Abstract
Face recognition is thought to rely more on the relative positions of face features (configural information) than on the appearance of the individual face parts (featural information). It also seems to rely on a specific band of spatial frequencies (SFs). In this study, we measured the SFs needed for processing configural and featural information using the method of constant stimuli in combination with a simultaneous-matching paradigm. Stimuli were two-octave-wide bandpass-filtered upright and inverted faces that contained either featural or configural modifications. SF thresholds for featural and configural processing were calculated by interpolating between discrimination accuracy scores. Low-pass and high-pass thresholds for featural and configural processing in upright faces were approximately equal, whereas for inverted faces, the thresholds were closer to the middle of the spectrum for configural processing relative to featural processing. Thus, a broader band of SFs, one that overlapped more with the middle of the frequency spectrum, was needed for configural processing than for featural processing in inverted faces. Our findings emphasise the importance of a narrow mid-range band of frequencies for both configural and featural encoding in upright faces and suggest that configural information is extracted less effectively in inverted faces.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".