Social Cognition: Theory and Neuroscience in Fetal Alcohol Spectrum Disorders
Bibliographic record
Abstract
Children with fetal alcohol spectrum disorders (FASD) have deficits across many cognitive, behaviour and social domains. However, despite social difficulty being proposed as a main deficit following prenatal alcohol exposure, the nature of their deficient social behaviour is largely unknown. One process that may underlie difficulties in social functioning is poor social cognition, which refers to one’s understanding of the self, others and social world. The primary goal of this dissertation was to determine whether social cognitive deficits represent a core disability underlying the socio-behavioural problems of FASD using a bottom-up approach. The first level of this approach is represented by face processing. Global and independent face feature processing was compared between FASD and normal controls (NC) using experimental and clinical measures. Eye gaze processing was investigated next using experimental and clinical tasks. At the highest level of the bottom-up approach, social perspective taking, including theory of mind and empathy were examined, along with how these abilities related to parent-rated behaviour. Finally, the lowest level consisted of specific aspects of the social neural network. White matter in three limbic pathways was investigated using diffusion tensor imaging (DTI).\nResults generally supported the bottom-up approach of social cognition in FASD. These children showed impaired processing of face features, when matching mouth shapes and partially occluded identity, compared with NC. The FASD group was slower to process gaze and arrow cues, suggesting impaired attention shifting. Children with FASD also showed impairments in social perspective taking, including understanding false beliefs and empathy, and these impairments were related to parent-rated attention and social problems, and autistic-like traits. Deficits in theory of mind got worse with age in FASD and empathy showed distinct sex-related differences. Although no group differences were observed on DTI indices, groups did show different age-related changes in white matter. In conclusion, deficits at each level of the current bottom-up approach may underlie the social impairments in FASD and may contribute to their broader social behavioural phenotype. The results from this dissertation have potential to inform clinical practice and lead to more effective diagnostic and treatment approaches in FASD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".