Are the face inversion effect and the composite face effect mediated by different spatial frequencies?
Bibliographic record
Abstract
Last year at VSS, we showed that the same spatial frequencies (SFs) are used for the identification of upright and inverted inner facial features (Abstract #153). Here, we report three follow-up experiments based on the same SF Bubbles technique to shed light on the relationship between the face inversion effect (Yin, 1969) and the composite face effect (Young, Hellawell, & Hay, 1987). In Experiment 1, we replicated our previous findings on the face inversion effect in a 10-choice identification task with 300 trials per orientation and per observer and with 20 faces from the set of Goffaux and Rossion (2006) revealed through an elliptical aperture hiding contour information—the same SFs were used to identify upright and inverted faces. In Experiment 2, we displayed the faces of Experiment 1 with contour information. For upright face identification, we replicated our previous results for inverted faces, however, the use of SFs was shifted toward lower SFs. Intriguingly, this shift is in the opposite direction to that predicted by Goffaux and Rossion (2006) who found that holistic processing is largely supported by low SFs. In Experiment 3, we re-examined SF tuning in the composite face paradigm of Goffaux and Rossion (2006) using the SF Bubbles technique. Preliminary results confirm and extend their results. In sum, holistic processing—as indexed by the composite face effect—and face identification appear to be mediated by different SFs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".