Emotional processing in children with conduct problems and callous/unemotional traits
Bibliographic record
Abstract
BACKGROUND: A considerable body of evidence now suggests that conduct problem (CP) children with callous/unemotional (CU) traits differ in many ways from CP children without these characteristics. Previous research has suggested that there are important differences for youth with CP and CU characteristics in their ability to process emotional information. The current study investigated the ability of children with disruptive behaviour disorders to label emotional faces and stories. METHODS: Participants (aged 7-12) were involved in a summer day treatment and research programme for children with disruptive behaviour problems. Two tasks were administered that were designed to measure participant's ability to recognize and label facial expressions of emotion, as well as their ability to label emotions in hypothetical situations. RESULTS: Results indicated that children with higher levels of CU traits, regardless of whether they had elevated CP scores, were less accurate in identifying sad facial expressions. Interestingly, children with higher CU scores were more accurate in labelling fear than were children with lower CU scores, while children with high CP but low CU traits were less accurate than other children in interpreting fearful facial emotions. Further, children's recognition of various emotional vignettes was not associated with CP, CU traits or their interaction. CONCLUSIONS: The current study demonstrated that it was the combination of CP and a high number of CU traits that differentiated emotional attributions. Consistent with previous research, youth with CU traits had more difficulty in identifying sad facial expressions. However, contrasting with some previous studies, higher CU traits were associated with more accurate perceptions of fearful expressions. It is possible that there is something specific to fear recognition for individuals with more psychopathic, CU traits that actually make them more successful for observing or recognizing fearful expressions. Additional research is needed to clarify both the recognition and processing of fear expression in CP children with and without CU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".