Sex Differences in Emotion Recognition and Emotional Inferencing Following Severe Traumatic Brain Injury
Bibliographic record
Abstract
The primary objective of the current study was to determine if men and women with traumatic brain injury (TBI) differ in their emotion recognition and emotional inferencing abilities. In addition to overall accuracy, we explored whether differences were contingent upon the target emotion for each task, or upon high- and low-intensity facial and vocal emotion expressions. A total of 160 participants (116 men) with severe TBI completed three tasks – a task measuring facial emotion recognition (DANVA-Faces), vocal emotion recognition (DANVA-Voices) and one measuring emotional inferencing (emotional inference from stories test (EIST)). Results showed that women with TBI were significantly more accurate in their recognition of vocal emotion expressions and also for emotional inferencing. Further analyses of task performance showed that women were significantly better than men at recognising fearful facial expressions and also facial emotion expressions high in intensity. Women also displayed increased response accuracy for sad vocal expressions and low-intensity vocal emotion expressions. Analysis of the EIST task showed that women were more accurate than men at emotional inferencing in sad and fearful stories. A similar proportion of women and men with TBI were impaired (≥ 2 SD s when compared to normative means) at facial emotion perception, χ 2 = 1.45, p = 0.228, but a larger proportion of men was impaired at vocal emotion recognition, χ 2 = 7.13, p = 0.008, and emotional inferencing, χ 2 = 7.51, p = 0.006.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".