Individual differences in the visual strategies underlying facial expression categorization
Bibliographic record
Abstract
Having the skills to decode the facial expressions of others is crucial for successful social interactions. Individual differences in this ability exist among the healthy population. We used Bubbles and eye-tracking to investigate how the visual information extraction strategies used for facial expression categorization are related to individual differences in the ability to perform this task. In the Bubbles task, 41 participants (4000 trials per participant) were asked to categorize facial expressions (six basic emotions plus neutral and pain). Sparse versions of these stimuli were created by sampling facial information at random spatial locations and at five non-overlapping spatial frequency bands. For each participant, a classification image showing what information in the stimuli correlated with accuracy was constructed by performing a multiple linear regression on the bubbles locations and accuracy. Subsequently, a group classification image was constructed by calculating a weighted average of all the individual classification images using an index of individual performance as weights (i.e. the number of bubbles necessary to maintain an average accuracy of 61%, transformed into z-scores across participants). We found that the most efficient observers use the left eye area more than the least efficient observers (r=0.43, p<0.05). An ideal observer analysis showed that the area comprising both eyes is the most informative to discriminate across all expressions, confirming that the most efficient observers use a strategy closer to the ideal one. The eye-tracking task (N=20) was identical to the Bubbles task, except that the face stimuli were presented without bubbles. We observed a similar pattern of results: the best participants had a leftward bias in their fixation maps. We propose that the best participants have a more efficient right hemisphere face processor, which allows them to process the most diagnostic information-the eyes-more efficiently, and results in a leftward bias in the information utilization. Meeting abstract presented at VSS 2012
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".