Facial expression categorization predominantly relies on mid-spatial frequencies
Bibliographic record
Abstract
Facial expressions are crucial in human communication. Recent decades have seen growing interest in understanding the role of spatial frequencies (SFs) in emotion perception in others. While some studies have suggested a preferential treatment of low versus high SFs, the optimal SFs for recognizing basic facial expressions remain elusive. This study, conducted on Western participants, addresses this gap using two complementary methods: a data-driven method (Exp. 1) without arbitrary SF cut-offs, and a more naturalistic method (Exp. 2) simulating variations in viewing distance. Results generally showed a preponderant role of low over high SFs, but particularly stress that facial expression categorization mostly relies on mid-range SF content (i.e. ∼6-13 cycles per face), often overlooked in previous studies. Optimal performance was observed at short to medium viewing distances (1.2-2.4 m), declining sharply with increased distance, precisely when mid-range SFs were no longer available. Additionally, our data suggest variations in SF tuning profiles across basic facial expressions and nuanced contributions from low and mid SFs in facial expression processing. Most importantly, it suggests that any method that removes mid-SF content has the downfall of offering an incomplete account of SFs diagnosticity for facial expression recognition.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".