Different spatial frequency tuning for face identification and facial expression recognition in adults
Bibliographic record
Abstract
Facial identity and facial expression represent invariant and changeable aspects of faces, respectively. The current study investigated how human observers (n=5) use spatial frequency information to recognize identity versus expression. We measured contrast thresholds for the identification of faces with varying expression and for the recognition of facial expressions across varying identity as a function of the center spatial frequency of narrow-band additive spatial noise. At a viewing distance of 60 cm, the peak threshold representing maximum sensitivity was at 11 cycles/face width for identifying the faces of two males or two females with varying expression. The peak threshold was significantly higher for recognizing facial expressions across varying identity: it was at 16 cycles/face width for discriminating between happiness and sadness, and between fear and anger, whether the expression was high or low in intensity. In a second phase we investigated the effect of viewing distance. As viewing distance increased from 60 to 120 and 180 cm, the peak threshold for identifying faces shifted gradually from 11 to 8 cycle/face width, while the peak threshold for recognizing facial expressions shifted gradually from 16 to 11 cycles/face width. The patterns from human observers were different from an ideal observer using all available information, which behaved similarly in recognizing identity and expression. In conclusion, we found, regardless of viewing distance, the optimal spatial frequency band for the recognition of facial expressions is higher than that for the identification of faces. The patterns suggest that finer details are necessary for recognizing facial expressions than for identifying faces and that the system is only partially scale invariant.
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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 | 0.001 |
| 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) | 0.003 | 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".