Spatial frequencies for detection of pain facial expressions revealed by reverse correlation
Bibliographic record
Abstract
The ability to detect pain facial expressions is a crucial step before help can be provided. Because of the biological importance of this skill, it is plausible to expect that an observer can detect that expression even from a relatively large distance. Accordingly, in VSS2019, we presented a study showing that pain facial expression detection relies on low spatial frequencies (SF; Guérette et al., 2019); low SF are available from farther away than high SF. These results were obtained using posed facial expressions, with a method that involves repeating the same stimuli. In the present study, we used Reverse Correlation (Mangini & Biederman, 2004) to verify in which SF the mental representation of pain facial expressions are encoded. This method has the advantage of revealing the expectations about the appearance of an expression, and the latter may be closer to spontaneous expressions encountered in day-to-day social interactions. On each trial, a neutral face was used as background stimulus, on which sinusoidal white noise was added. Participants were asked to choose which of two noisy faces better represented a target emotion. Three target emotion conditions were used: pain, fear, and happiness. Fear and happiness are respectively considered the most similar and dissimilar expressions to pain (Wang et al., 2015). Mental representations of pain involved SF ranging from 1.13 to 12.3 cycles per face (cpf), peaking at 3.78 cpf. Fear and happiness relied on a similar range of SF (4.17 and 3.63 cpf, respectively). These results show that low SF are encoded in mental representations of pain facial expressions. This finding is congruent with previous findings that accurate detection of pain relies on low SF, and add evidence to the idea that pain expressions are communicated in a way to be detected from far away and using coarse visual information.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".