Different aspects of facial affect recognition impairment following traumatic brain injury: The role of perceptual and interpretative abilities
Bibliographic record
Abstract
It is well established that many individuals with traumatic brain injury (TBI) are impaired at facial affect recognition, yet little is known about the mechanisms underlying such deficits. In particular, little work has examined whether the breakdown of facial affect recognition abilities occurs at the perceptual level (e.g., recognizing a smile) or at the verbal categorization stage (e.g., assigning the label "happy" to a smiling face). The aim of the current study was to investigate the integrity of these two distinct facial affect recognition subskills in a sample of 38 individuals with moderate-to-severe TBI and 24 demographically matched healthy individuals. Participants were administered an affect matching (perceptual skills) and an affect labeling (verbal categorization skills) task. Statistical analyses revealed that, while individuals with TBI showed significantly higher levels of impairment in the verbal categorization task than in the perceptual task, they performed less well than healthy comparison participants on both tasks. These findings indicate that facial affect recognition impairment can occur at different cognitive stages following TBI, suggesting the necessity of careful screening to offer targeted treatment. Moreover, they provide further neuropsychological evidence supporting the notion that distinct types of subskills are necessary to achieve successful recognition of facial affects.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".