Gender differences in the visual strategies underlying facial expression categorization
Bibliographic record
Abstract
Decoding facial expressions of emotions is a crucial ability for successful social interactions. Gender differences have been found in the neural responses to emotional faces (McClure et al., 2004), suggesting that men and women process facial expressions differently. The present study used the Bubbles technique (Gosselin & Schyns, 2001) to verify whether the visual strategies used in facial expression categorization (six basic emotions as well as neutral and pain expressions) differ across gender. Sparse versions of emotional faces were created by sampling facial information at random spatial locations and at five non-overlapping spatial frequency bands. The average accuracy was maintained at 56% (halfway between chance and perfect performance) by adjusting the number of bubbles on a trial-by-trial basis using QUEST (Watson & Pelli, 1983). Thus, the number of bubbles reflected the participants' relative aptitude for this task. Forty-one participants (14 men) each categorized 4000 sparsed stimuli. On average, women performed better than men (t(39) = 3.08, p<0.05). Classification images showing which information in the stimuli correlated with participants’ accuracy were constructed separately for each gender by performing a multiple linear regression on the bubbles' locations and accuracy. A pixel test was applied to the classification image to determine statistical significance (Zcrit = 3.36, p<0.05; corrected for multiple comparisons). Women used both eyes and mouth areas more efficiently than men. In order to verify if the visual strategy is modulated by gender when the ability to perform the task is factored out, we selected 12 men and women that were matched on the average number of bubbles (i.e., the 12 men (vs. women) with the highest (vs. lowest) performance), and we repeated the analysis described above. When performance was controlled for, women used the mouth area more than men, again suggesting that gender influences the visual strategy used for categorizing facial expressions. Meeting abstract presented at VSS 2013
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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 | 0.000 |
| 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) | 0.006 | 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".