Alexithymia predicts arousal-based processing deficits and discordance between emotion response systems during emotional imagery.
Bibliographic record
Abstract
Alexithymia is believed to involve deficits in emotion processing and imagery ability. Previous findings suggest that it is especially related to deficits in processing the arousal dimension of emotion, and that discordance may exist between self-report and physiological responses to emotional stimuli in alexithymia. The current study used a well-established emotional imagery paradigm to examine emotion processing deficits and discordance in participants (N = 86) selected based on their extreme scores on the Toronto Alexithymia Scale-20. Physiological (skin conductance, heart rate, and corrugator and zygomaticus electromyographic responses) and self-report (valence, arousal ratings) responses were monitored during imagery of anger, fear, joy, and neutral scenes and emotionally neutral high arousal (action) scenes. Results from regression analyses indicated that alexithymia was largely unrelated to responses on valence-based measures (facial electromyography, valence ratings), but that it was related to arousal-based measures. Specifically, alexithymia was related to higher heart rate during neutral and lower heart rate during fear imagery. Alexithymia did not predict differential responses to action versus neutral imagery, suggesting specificity of deficits to emotional contexts. Evidence for discordance between physiological responses and self-report in alexithymia was obtained from within-person analyses using multilevel modeling. Results are consistent with the idea that alexithymic deficits are specific to processing emotional arousal, and suggest difficulties with parasympathetic control and emotion regulation. Alexithymia is also associated with discordance between self-reported emotional experience and physiological response to emotion, consistent with prior evidence.
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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".