Cognitive Alexithymia Is Associated with the Degree of Risk for Psychosis
Bibliographic record
Abstract
Alexithymia is a personality construct denoting emotion processing problems. It has been suggested to encompass two dimensions: a cognitive and affective dimension. The cognitive dimension is characterized by difficulties in identifying, verbalizing and analyzing emotions, while the affective dimension reflects the level of emotional arousal and imagination. Alexithymia has been previously proposed as a risk factor for developing psychosis. More specifically, the two alexithymia dimensions might be differentially related to the vulnerability for psychosis. Therefore, we examined the two dimensions of alexithymia, measured with the BVAQ in 94 siblings of patients with schizophrenia, 52 subjects at ultra-high risk (UHR) for developing psychosis, 38 patients with schizophrenia and 109 healthy controls. The results revealed that siblings and patients had higher levels of cognitive alexithymia compared to controls. In addition, subjects at UHR for psychosis had even higher levels of cognitive alexithymia compared to the siblings. The levels of affective alexithymia in siblings and patients were equal to controls. However, UHR individuals had significantly lower levels of affective alexithymia (i.e. higher levels of emotional arousal and fantasizing) compared to controls. Alexithymia was further related to subclinical levels of negative and depressive symptoms. These findings indicate that alexithymia varies parametrically with the degree of risk for psychosis. More specifically, a type-II alexithymia pattern, with high levels of cognitive alexithymia and normal or low levels of affective alexithymia, might be a vulnerability factor for psychosis.
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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 | 0.002 |
| 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".