The Level and Nature of Autistic Intelligence II: What about Asperger Syndrome?
Bibliographic record
Abstract
A distinctively uneven profile of intelligence is a feature of the autistic spectrum. Within the spectrum, Asperger individuals differ from autistics in their early speech development and in being less likely to be characterized by visuospatial peaks. While different specific strengths characterize different autistic spectrum subgroups, all such peaks of ability have been interpreted as deficits: isolated, aberrant, and irreconcilable with real human intelligence. This view has recently been challenged by findings of autistic strengths in performance on Raven's Progressive Matrices (RPM), an important marker of general and fluid intelligence. We investigated whether these findings extend to Asperger syndrome, an autistic spectrum subgroup characterized by verbal peaks of ability, and whether the cognitive mechanisms underlying autistic and Asperger RPM performance differ. Thirty-two Asperger adults displayed a significant advantage on RPM over Wechsler Full-Scale and Performance scores relative to their typical controls, while in 25 Asperger children an RPM advantage was found over Wechsler Performance scores only. As previously found with autistics, Asperger children and adults achieved RPM scores at a level reflecting their Wechsler peaks of ability. Therefore, strengths in RPM performance span the autistic spectrum and imply a common mechanism advantageously applied to different facets of cognition. Autistic spectrum intelligence is atypical, but also genuine, general, and underestimated.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 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".