Individual differences in facial expression recognition ability are linked to differences in the efficiency at using the diagnostic visual information
Bibliographic record
Abstract
There is a common assumption that the ability in facial expression recognition is related to the adequacy of visual strategies, but it has been recently shown that multiple eye fixation patterns may lead to comparable performances (Yitzhak et al., 2020). However, since there is a partial dissociation between eye fixations and visual information utilization, it remains possible that facial recognition ability is associated with this latter component of visual strategies. We tested this hypothesis using the Bubbles method (Gosselin & Schyns, 2001), which allows to measure the visual information successfully used to complete a task. Participants (N=69, 34 males) completed five tasks measuring their facial expression recognition ability: Reading the Mind in the Eyes Test, Films Expression Task, Megamix and two tasks of basic facial expressions categorization. They also completed 4000 trials of a Bubbles task in which they categorized facial expressions (anger, disgust, fear, joy). A principal component analysis was conducted on the five ability measures and the two extracted components were used as ability indexes. Visual information utilization patterns across participants were classified using a K-mean clustering analysis. Three patterns were revealed; in all groups, participants mostly relied on the mouth area, followed by the eyes area, to successfully categorize facial expressions. However, a gradient of efficiency was observed across the three groups, suggesting an increase in the efficiency at using both the eyes and mouth area across the three groups. Most importantly, the ability at recognizing facial expressions also varied across those three groups, with the lower ability group showing the least efficient visual strategy, and the higher ability group showing the most efficient visual strategy. Taken together, these results support the existence of an association between facial expression recognition ability and visual information utilization patterns.
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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 | 0.003 |
| 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) | 0.005 | 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".