Relationships Between Alexithymia, Affect Recognition, and Empathy After Traumatic Brain Injury
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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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".