Relationships Between Alexithymia, Affect Recognition, and Empathy After Traumatic Brain Injury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To determine (1) alexithymia, affect recognition, and empathy differences in participants with and without traumatic brain injury (TBI); (2) the amount of affect recognition variance explained by alexithymia; and (3) the amount of empathy variance explained by alexithymia and affect recognition. PARTICIPANTS: Sixty adults with moderate-to-severe TBI; 60 age and gender-matched controls. PROCEDURES: Participants were evaluated for alexithymia (difficulty identifying feelings, difficulty describing feelings, and externally-oriented thinking); facial and vocal affect recognition; and affective and cognitive empathy (empathic concern and perspective-taking, respectively). RESULTS: Participants with TBI had significantly higher alexithymia; poorer facial and vocal affect recognition; and lower empathy scores. For TBI participants, facial and vocal affect recognition variances were significantly explained by alexithymia (12% and 8%, respectively); however, the majority of the variances were accounted for by externally-oriented thinking alone. Affect recognition and alexithymia significantly accounted for 16.5% of cognitive empathy. Again, the majority of the variance was primarily explained by externally-oriented thinking. Affect recognition and alexithymia did not explain affective empathy. CONCLUSIONS: Results suggest that people who have a tendency to avoid thinking about emotions (externally-oriented thinking) are more likely to have problems recognizing others' emotions and assuming others' points of view. Clinical implications are discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it