Social anxiety symptoms, heart rate variability, and vocal emotion recognition in women: evidence for parasympathetically-mediated positivity bias
Bibliographic record
Abstract
Background and Objectives Individuals with social anxiety disorder show pronounced perceptual biases in social contexts, such as being hypervigilant to threat and discounting positive social cues. Parasympathetic activity influences responses to the social environment and may underlie these biases. This study examined the associations among social anxiety symptoms, heart rate variability (HRV), and vocal emotion recognition.Design and Method Female undergraduate students (N = 124) self-reported their social anxiety symptoms using the Social Anxiety Disorder Dimensional Scale and completed a computerized vocal emotion recognition task using stimuli from the Ryerson Audio-Visual Database of Emotional Speech and Song stimulus set. HRV was measured at baseline and during the emotion recognition task.Results Women with more social anxiety symptoms had higher emotion recognition accuracy (p = .021) and rated positive stimuli as less intense (p = .032). Additionally, although those with greater social anxiety symptoms did not have lower resting HRV (p = .459), they did have lower task HRV (p = .026), which mediated their lower positivity bias and greater recognition accuracy.Conclusions A parasympathetically-mediated positivity bias may indicate or facilitate normal social functioning in women. Additionally, HRV during a symptom- or disorder-relevant task may predict task performance and reveal parasympathetic differences that are not found at baseline.
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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 | 0.000 | 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) | 0.001 | 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".