How is quantification of social deficits useful for studying autism and schizophrenia?
Bibliographic record
Abstract
Imagine going to see your M.D., describing how you feel unwell, and being told that you have 'deficits in your immune system'.You would presumably inquire about the nature of the deficits, and whether they involve, say, autoimmune disorder or reduced levels of leukocytes.Now imagine a trip to the psychiatrist, whereupon, after a battery of tests, you are told that you have 'deficits in social cognition'.What have you learned?Pinkham et al. (2019) compared individuals with autism to those with schizophrenia, for a large set of psychological tests designed to assess social-cognitive performance.Their comprehensive study was the largest to date in terms of sample sizes and numbers of tests, for such comparisons.They found that performance levels did not differ significantly between these two clinical groups for most of the tests.Their primary inference, couched in well-justified caveats, was an indication of 'the potential benefit of applying treatments transdiagnostically'.An alternative interpretation of this and similar studies is that poor performance on standard psychological tests of social cognition provides little information about the diagnosis, causes, or treatments of psychiatric conditions.This interpretation is based on three main points.First, similar levels of deficits in social cognition, as indexed by self-report and task-based psychological tests, can result from similar, partially overlapping, independent, or opposite biological causes.Table 1 lists studies in which patients with autism and schizophrenia were compared for both deficit-associated psychological traits (measured, or based on previous work) and their neurological mechanisms assessed by EEG and/or MRI.In each case, similar psychological deficits showed evidence of opposite underlying causes.Such results suggest that More-extensive comparisons of autism with schizophrenia, for diverse phenotypes, are provided in Crespi and Go (2015, Table 2).
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".