Neuropsychological Approach on Expressed Emotion in Neurotypical and Autism Spectrum Disorder: A Path Model Analysis
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects individual social communication with a range of restricted behaviour patterns. People with ASD will also have difficulties with social emotional reciprocity, which is not predominantly found in neurotypical individuals. Individuals with ASD have difficulty connecting with neurotypical (i.e., nonautistic) people because they fail to identify other people's emotions and mental states. Alexithymia is a personality characteristic defined by a subclinical inability to identify and explain one's own emotions. Alexithymia is defined by a significant dysfunction in emotional awareness, social attachment, and interpersonal relationships. It is distinguished by impaired emotional awareness, which has been increasing in diagnostic frequency in a variety of neuropsychiatric diseases, with notable overlap with ASD. To empirically measure the condition of alexithymia in neurotypical individuals (N = 12) and people diagnosed with ASD (N = 12), were assessed with the Observer Alexithymia Scale (OAS) by Haviland et al., 2000. The mean age of the neurotypical is (M = 21.67; SD = 2.60) and the ASD is (M = 18.33; SD = 2.22). Using SPSS ver.20, the data was analysed using descriptive and inferential statistics methods. The results indicate the significant difference between neurotypical and autism spectrum disorder individuals with the condition of alexithymia. The path model, which was drawn from the SPSS AMOS version 20, emphasises the causal relationship between variables of interest from the Observer Alexithymia Scale. This study found that individuals with ASD have significant corroboration to alexithymia when compared to neurotypical individuals.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".