Role of Neuro-behavioral Systems and Sensitivity Sensory Processing in Alexithymia
Bibliographic record
Abstract
Objective: The present study aimed to predict alexithymia based on BAS/BIS activity and sensitivity sensory processing. Method: Using random stratified sampling, 400 people (183 male and 217 female) were selected. They were assessed by BAS/BIS questionnaire (carver & White, 1994), The Higly Sensitivity Sensory Processing Scale (HSPS) (Aron & Aron, 1997), and Persian version of the Toronto Alexithymia Scale (Bagby, Parker, & Taylor, 1994). Data were analyzed by Pearson correlation coefficient and stepwise regression analysis. Results: The finding demonstrated that there were significant positive relationships between alexithymia and sensitivity sensory processing, low sensory threshold and ease of exicitation, and behavioral activation system. Alexithymia had negative significant relationships with aesthetic sensitivity and behavioral inhibition system (P<.01). It was found that Ease of excitation, low sensory threshold and BAS explained 76% of the variance of alexithymia. Conclusion: The findings emphasize the need to recognize the role of BAS/BIS activity and sensitivity sensory processing in predicting students’ alexithymia.
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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 | 0.000 |
| 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".