The role of contour information in the spatial frequency tuning of upright and inverted faces
Bibliographic record
Abstract
Using the spatial frequency (SF) Bubbles technique, we recently revealed that the same SFs are used for the identification of upright and inverted faces (Willenbockel et al., in press; see also Gaspar, Sekuler, & Bennett, 2008). In these articles, the faces were presented through an elliptical aperture hiding contours. Given that contours do contain information useful for face identification, real-world differences between upright and inverted face SF processing might have been missed. Here, we examined the role of contour information in the SF tuning of upright and inverted face identification using SF Bubbles. We created a bank of 20 faces, and each face was randomly assigned either to set A or to set B. Six participants saw the faces from set A with contours and the faces from set B without contours (shown through an elliptical aperture), whereas six other participants saw the faces from set A without contours and faces from set B with contours. On each trial, a face was selected and its SFs were sampled randomly (for details, see Willenbockel et al., in press). Participants completed one thousand trials in each condition. Multiple linear regressions were performed on the random SF filters and response accuracy. Without contours, we closely replicated Willenbockel et al.: the same SFs correlated with accurate identification of upright and inverted faces (a single band beginning at ∼6 cycles per face (cpf) and ending at ∼15 cpf). The presence of contour information led to a similar increase in the diagnosticity of low spatial frequencies, irrespective of face orientation; and to a decrease in the diagnosticity of higher spatial frequencies for inverted faces (upright faces with contour: a single band beginning at ∼2.3 cpf and ending at ∼16.5 cpf; upright faces without contour: a single band beginning at ∼4 cpf and ending at ∼20 cpf).
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.
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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".