The impact of gender on visual strategies underlying the discrimination of facial expressions of pain.
Bibliographic record
Abstract
Previous studies have found a female advantage in the recognition/detection (Hill and Craig, 2004; Prkachin et al., 2004) of pain expressions, although this effect is not systematic (Simon et al., 2008; Riva et al., 2011). However, the impact of gender on pain expression recognition visual strategies remains unexplored. In this experiment, 30 participants (15 males) were tested using the Bubbles method (Gosselin & Schyns, 2001), which randomly sampled facial features across five spatial frequency (SF) bands to infer what visual information was successfully used. On each of the 1,512 trial, two bubblized faces, sampled from 8 avatars (2 genders; 4 levels of pain intensity), were presented to participants who identified the one expressing the highest pain level. Three difficulty levels, determined by the percentage of pain difference between the two stimuli (i.e 100%, 66% or 33%) were included. Number of bubbles needed to maintain an average accuracy of 75% was used as a performance measure (Royer et al., 2015). Results indicated a trend towards a higher number of bubbles needed by male (M=57.7, SD=30.4) in comparison to female (M=40.2, SD=23.2), [t(28)=2.02,p=0.05]. Moreover, this difference was significant with the highest level of difficulty [t(28)=2.22, p=0.04], suggesting that pain discrimination was more difficult for male (M=77.6, SD=36.8) than female (M=52.3, SD=24.5). Classification images, generated by calculating a weighted sum of the bubbles position (where accuracies transformed in z-scores were used as weights), revealed that female made a significantly higher use of the lowest band of SF (Zcrit = 2.7, p< 0.05; 5.4–2.7 cycles per face). These results suggest that gender impacts the performance and the visual strategies underlying pain 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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".