Comprehension of affective prosody in women with post‐traumatic stress disorder related to childhood abuse
Bibliographic record
Abstract
OBJECTIVE: Although deficits in memory and cognitive processing are evident in post-traumatic stress disorder (PTSD), difficulties with social cognition and the impact of such difficulties on interpersonal functioning are poorly understood. Here, we examined the ability of women diagnosed with PTSD related to childhood abuse to discriminate affective prosody, a central component of social cognition. METHOD: Women with PTSD and healthy controls (HCs) completed two computer-based tasks assessing affective prosody: (i) recognition (categorizing foreign-language excerpts as angry, fearful, sad, or happy) and (ii) discrimination (identifying whether two excerpts played consecutively had the 'same' or 'different' emotion). The association of performance with symptom presentation, trauma history, and interpersonal functioning was also explored. RESULTS: Women with PTSD were slower than HCs at identifying happiness, sadness, and fear, but not anger in the speech excerpts. The presence of dissociative symptoms was related to reduced accuracy on the discrimination task. An increased severity of childhood trauma was associated with reduced accuracy on the discrimination task and with slower identification of emotional prosody. CONCLUSION: Exposure to childhood trauma is associated with long-term, atypical development in the interpretation of prosodic cues in speech. The findings have implications for the intergenerational transmission of trauma.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".