No Words For Feelings, No Adaptative Emotional Regulation Strategies ?
Bibliographic record
Abstract
Alexithymia is a multi-faceted personality trait described by difficulties identifying (DIF), describing (DDF), and attending (EOT) to one's feelings. The underlying processes that may explain alexithymia's vulnerability to affective disorders are not systematically understood (Luminet & Nielson, 2024) but may involve emotional regulation (ER). Although we use a variety of ER strategies daily, only a minimal number of studies have explored other ER strategies in the context of alexithymia (Preece et al., 2023). The objective of our study was to investigate the relationship between alexithymia and ER strategies by adopting a facet-level dimensional approach allowing to investigate alexithymia for positive and negative emotions. Participants (N=122) aged 18 to 57 were administered the Toronto Alexithymia Scale (TAS-20) and the Perth Alexithymia Questionnaire (PAQ) to measure alexithymia; the Emotion Regulation Questionnaire (ERQ) and the Cognitive Emotion Regulation Questionnaire (CERQ) to measure the use of ER strategies, and the PANAS to measure affectivity. Multiple regressions were performed with positive and negative affectivity as covariates. The results show that alexithymia was predictive of an overall greater use of expressive suppression (all ps <.01) and a lower use of cognitive reappraisal strategies (cognitive reappraisal, centration on action; acceptance; rumination; self-blame, all ps <.05). However, the use of ER strategies was differentially predicted by specific alexithymia facets. While the facet difficulties describing negative feelings (N-DDF) was associated with more expressive suppression (b=.6, p=<.001), the facet difficulties identifying negative feelings (N-DIF) was associated with less expressive suppression (b=-.45, p=<.001) and the facet difficulties describing positive feelings (P-DDF) with more cognitive reappraisal (b=.34, p=.032). Moreover, difficulties identifying positive feelings (P-DIF) was associated with a lower use of acceptance (b=-0.46, p=0.004), which was not the case for difficulties identifying negative feelings. Our results confirm that alexithymia is associated with ER particularities, as observed in previous studies, and illustrate the value of adopting a facet-level approach while studying alexithymia. The ER strategy selection seems to be conditioned by an emotional avoidance mechanism, marked by a lower use of strategies involving an internal focus on emotions (such as cognitive reappraisal strategies). Future studies should confirm these findings by incorporating measures of emotional regulation that include other strategies (e.g., behavioral strategies as measured by the Behavioural Emotion Regulation Questionnaire). Our study also calls for further investigation of alexithymia for specific emotional valences as measured by the differences in ER observed in association with difficulties appraising positive and negative feelings.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".