An fMRI study on alexithymia and affective state recognition in the Reading the Mind in the Eyes Test
Bibliographic record
Abstract
Recognizing others' affective states is essential for successful social interactions. Alexithymia, characterized by difficulties in identifying and describing one's own emotions, has been linked to deficits in recognizing emotions and mental states in others. To investigate how neural correlates of affective state recognition are affected by different facets of alexithymia, we conducted a functional magnetic resonance imaging study with 53 healthy participants (aged 19-36 years, 51% female) using the Reading the Mind in the Eyes Test (RMET) and three different measures of alexithymia [Toronto Structured Interview for Alexithymia (TSIA), Toronto Alexithymia Scale (TAS-20), and Bermond-Vorst Alexithymia Questionnaire]. In addition, we examined brain activity during the RMET and replicated previous findings with task-related brain activation in the inferior frontal and temporal gyri, as well as the insula. No association was found between alexithymia and behavioral performance in the RMET, possibly due to the low number of participants with high alexithymia levels. Region of interest based analyses revealed no associations between alexithymia and amygdala or insula activity during the RMET. At the whole-brain level, both a composite alexithymia score and the unique variance of the alexithymia interview (TSIA) were associated with greater activity in visual processing areas during the RMET. This may indicate that affective state recognition performance in alexithymia relies on a higher compensatory activation in visual areas.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".