Reduced neural responses to vocal fear: a potential biomarker for callous‐uncaring traits in early childhood
Bibliographic record
Abstract
OBJECTIVE: Callous-unemotional (CU) traits are characterized by a lack of guilt and empathy, and low responsiveness to distress and fear in others. Children with CU traits are at-risk for engaging in early and persistent conduct problems. Individuals showing CU traits have been shown to have reduced neural responses to others' distress (e.g., fear). However, the neural components of distress responses in children with CU traits have not been investigated in early childhood. In the current study, we examined neural responses that underlie the processing of emotionally valenced vocal stimuli using the event-related potential technique in a group of preschoolers. METHOD: Participants between 2 and 5 years old took part in an auditory oddball task containing English-based pseudowords spoken with either a fearful, happy, or a neutral prosody while electroencephalography data were collected. The mismatch negativity (MMN) component, an index of the automatic detection of deviant stimuli within a series of stimuli, was examined in association with two dimensions of CU traits (i.e., callousness-uncaring and unemotional dimensions) reported by primary caregivers. RESULTS: Findings suggest that the callousness-uncaring dimension of CU traits in early childhood is associated with reduced responses to fearful vocal stimuli. CONCLUSIONS: Reduced neural responses to vocal fear could be a biomarker for callous-uncaring traits in early childhood. These findings are relevant for clinicians and researchers attempting to identify risk factors for early callous-uncaring traits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".