Effects of Autistic Traits on Emotion Regulation in Neurotypical Adults
Bibliographic record
Abstract
Background: Individuals with Autism Spectrum Disorder (ASD) seem to have lower emotion regulation competence (Samson, Huber, & Gross, 2012). It has been reported that ASD is a continuum of social-communication disability (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001) and that neurotypical individuals are also part of that continuum and have autistic traits. Therefore, neurotypical individuals with more autistic traits would be expected to have lower emotion regulation competence than those with less autistic traits. Additionally, low levels of resting heart rate variability (HRV) have been associated with poor social functioning and emotional rigidity (Butler, Wilhelm, & Gross, 2006), which characterize ASD. Consequently, it is hypothesized that neurotypical individuals with more autistic traits should also have lower resting HRV. Objectives: To analyse if neurotypical adults with more autistic traits use less efficient emotion regulation strategies and the relation to cardiac vagal control. Methods: 80 undergraduate students participated in the study. None of the participants had a diagnosis of ASD. Participants were requested to answer four questionnaires: the Autism-Spectrum Quotient (AQ; Baron-Cohen et al., 2001), which comprises 50 items and assesses 5 autistic traits in the general population; the Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004), which comprises 36 items and assesses 6 factors of emotional dysregulation; the Emotion Regulation Questionnaire (ERQ; Gross & John, 2003), which comprises 10 items and assesses 2 emotion regulation strategies, cognitive reappraisal and expressive suppression; and finally, the 20-item Toronto Alexithymia Scale (TAS-20; Bagby, Parker, & Taylor, 1994), which comprises 20 items and assesses 3 factors of alexithymia. In the end, participants’ HRV was measured for 5 minutes. Results: Data collection is still being carried out and therefore definite results cannot be drawn. However, preliminary results seem to indicate that participants who have more autistic traits have in general more difficulties regulating their emotions. They use more often suppression than reappraisal as emotion regulation strategy and demonstrate more difficulties in two factors of the DERS (“Lack of emotional awareness” and “Lack of emotional clarity”). Results also seem to indicate that those with more autistic traits have a higher score in alexithymia. Concerning HRV, preliminary results indicate that those with more autistic traits have higher resting HRV. Conclusions: Preliminary results indicate that, neurotypical individuals who have more autistic traits have a less adaptive emotion regulation profile compared to neurotypical individuals with less autistic traits. They use more frequently expressive suppression and less frequently cognitive reappraisal and have more difficulties understanding and being aware of their emotions. This could be explained by the fact that, similarly to individuals with ASD, neurotypical individuals with more autistic traits have more difficulties taking another person mental perspective. This is also supported by findings that those with more autistic traits have a higher score in alexithymia, showing that they have more difficulties identifying and describing emotions. The unexpected HRV result might be explained by differences in the pattern of physiological responding (Zahn, Rumsey, & Kammen, 1987).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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 teacher head, 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".