Emotional processing in alexithymia: Behavioral evidence
Bibliographic record
Abstract
Social cognition plays a crucial role in primate survival. A key facet of social cognition is the recognition of others’ emotions, which facilitates social interactions by providing insights into others’ mental states and enabling us to adjust our behaviors accordingly. This dissertation focuses on understanding emotional processing deficits associated with alexithymia - a personality trait characterized by difficulties identifying and describing emotions - in a neurotypical population. To achieve this goal, we designed two experiments to investigate whether the emotional processing deficits associated with alexithymia extend to neutral expressions (Experiment I) and to explore their possible connection to altered holistic processing (Experiment II). In both experiments, perceptual difficulty was increased by adding visual noise, known to exacerbate alexithymia-related deficits. Alexithymia traits were independently measured using the Toronto Alexithymia Scale (TAS-20). In the first experiment, 35 university students were presented with 30 images of fearful faces, neutral faces, and objects, which they were asked to categorize. Results showed lower accuracy across all categories associated with increasing alexithymia scores. In the second experiment, 90 upright and inverted faces (displaying fear, happiness, and neutral expressions) were presented to 49 university students in a similar design to Experiment I. Participants with higher alexithymia scores performed worse when holistic processing was disrupted by image inversion, compared to upright presentation. Our analysis suggests that individuals with higher levels of alexithymia exhibit a general impairment in perceptual categorization, contrasting with the anticipated emotion-specific deficits (Experiment I) and rely on holistic processing (Experiment II).
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".