Recognition of facial and vocal affect following traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: Studies of facial affect recognition by people with traumatic brain injury (TBI) have shown this to be a significant problem. Vocal affect recognition also appears to be challenging for this population, but little is known about the degree to which one modality is impaired compared to the other. This study compared facial and vocal affect recognition of high and low intensity emotion expressions in people with moderate-to-severe TBI. METHODS: The Diagnostic Analysis of Nonverbal Accuracy-2 (Adult Faces; Voices) was administered to 203 participants with TBI. RESULTS: Adults with TBI identified vocal emotion expressions with greater accuracy than facial emotion expressions. Facial affect recognition impairment was identified in 34% of participants, 22% were classified as having vocal affect recognition impairment and 15% showed impairment in both modalities. Participants were significantly less accurate at identifying low vs high intensity emotion expressions in both modalities. Happy facial expressions were significantly better identified than all other emotions. Errors were distributed across the emotion categories for vocal expressions. CONCLUSIONS: The degree of facial affect impairment was significantly greater than vocal affect impairment in this sample of people with moderate-to-severe TBI. Low intensity emotion expressions were particularly problematic and an advantage for positively valenced facial emotion expressions was indicated.
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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 | 0.000 | 0.005 |
| 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) | 0.002 | 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".