Alexithymia Is Linked with a Negative Bias for Past and Current Events in Healthy Humans
Bibliographic record
Abstract
Although research provides a rich literature about the influence of emotional states on temporal cognition, evidence about the influence of the style of emotion processing, as a personality trait, on temporal cognition is extremely limited. We provide a novel contribution to the field by exploring the relationship between difficulties of identifying and describing feelings and emotions (alexithymia) and time perspective. One hundred and forty-two healthy participants completed an online version of the TAS-20 scale, which measures alexithymia, and the Zimbardo Time Perspective Inventory, which monitors individual differences in time-orientation regarding the past, present, and future. The results show greater attention to past negative aspects in participants whose TAS-20 score was indicating borderline or manifest alexithymia, as compared to non-alexithymic individuals. Moreover, the higher the TAS-20 score, the higher the tendency was to focus on negative aspects of the past and interpret the present fatalistically. These results suggest that difficulties in identifying and describing feelings and emotions are associated with a negative bias for past and present events. Theoretical and clinical implications of this finding 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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".