Facial affect recognition in context in adults with and without TBI
Bibliographic record
Abstract
Introduction: Several studies have reported impaired emotion recognition in adults with traumatic brain injury (TBI), but studies have two major design features that limit application of results to real-world contexts: (1) participants choose from among lists of basic emotions, rather than generating emotion labels, and (2) images are typically presented in isolation rather than in context. To address these limitations, we created an open-labeling task with faces shown alone or in real-life scenes, to more closely approximate how adults with TBI label facial emotions beyond the lab. Methods: Participants were 55 adults (29 female) with moderate to severe TBI and 55 uninjured comparison peers, individually matched for race, sex, and age. Participants viewed 60 photographs of faces, either alone or in the pictured person's real-life context, and were asked what that person was feeling. We calculated the percent of responses that were standard forced-choice-task options, and also used sentiment intensity analysis to compare verbal responses between the two groups. We tracked eye movements for a subset of participants, to explore whether gaze duration or number of fixations helped explain any group differences in labels. Results: Over 50% of responses in both groups were words other than basic emotions on standard affect tasks, highlighting the importance of eliciting open-ended responses. Valence of labels by participants with TBI was attenuated relative to valence of Comparison group labels, i.e., TBI group responses were less positive to positive images and the same was true for negative images, although the TBI group responses had higher lexical diversity. There were no significant differences in gaze duration or number of fixations between groups. Discussion: Results revealed qualitative differences in affect labels between adults with and without TBI that would not have emerged on standard forced-choice tasks. Verbal differences did not appear to be attributable to differences in gaze patterns, leaving open the question of mechanisms of atypical affect processing in adults with TBI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".