Increasing affective distance - leftward prism adaptation amplifies alexithymia in healthy females
Bibliographic record
Abstract
Introduction: Emotional processing is linked with spatial attention, which prioritizes emotional stimuli over neutral ones. The interconnection between spatial and emotional processing may rely on the overlap between the networks underpinning such cognitive functions. Recent evidence has indeed identified a link between the rightward visuospatial bias exhibited by healthy individuals and the challenge in understanding emotional states, so-called alexithymia. However, while spatial attention has been manipulated by prism adaptation (PA), a well-known sensorimotor training, whether this is possible with emotional processing has never been investigated. Methods: Ninety-five participants completed alexithymia questionnaires, Toronto Alexithymia Scale (TAS-20) and Perth Alexithymia Questionnaire (PAQ), before and after a single session of either leftward or rightward deviating PA. Results: While both males and females showed the expected sensorimotor aftereffect solely leftward PA modulated alexithymia scores, and it did so only for women. The results indicate that leftward PA not only affects visuospatial performance, but also emotional processing, particularly in how individuals perceive and interpret emotional proximity and distance. Discussion: Alexithymia may be, therefore, metaphorically linked to impaired perception of emotional closeness and remoteness. These findings suggest that PA may modulate emotional capacities in a sex-dependent manner, offering insights into its therapeutic potential while also highlighting the need for caution as prolonged PA-based interventions may affect emotional well-being.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".