Discrepancy between WISC-III and WISC-IV Cognitive Profile in Autism Spectrum: What Does It Reveal about Autistic Cognition?
Bibliographic record
Abstract
The cognitive profile and measured intellectual level vary according to assessment tools in children on the autism spectrum, much more so than in typically developing children. The recent inclusion of intellectual functioning in the diagnostic process for autism spectrum disorders leads to the crucial question on how to assess intelligence in autism, especially as some tests and subtests seem more sensitive to certain neurodevelopmental conditions. Our first aim was to examine the cognitive profile on the current version of the most widely used test, the Wechsler Intelligence Scales for Children (WISC-IV), for a homogenous subgroup of children on the autism spectrum, i.e. corresponding to DSM-IV diagnosis of "autism". The second aim was to compare cognitive profiles obtained on the third edition versus 4th edition of WISC, in order to verify whether the WISC-IV yields a more distinctive cognitive profile in autistic children. The third aim was to examine the impact of the WISC-IV on the cognitive profile of another subgroup, children with Asperger's Syndrome. 51 autistic, 15 Asperger and 42 typically developing children completed the WISC-IV and were individually matched to children who completed the WISC-III. Divergent WISC-IV profiles were observed despite no significant intelligence quotient difference between groups. Autistic children scored significantly higher on the Perceptual Reasoning Index than on the Verbal Comprehension Index, a discrepancy that nearly tripled in comparison to WISC-III results. Asperger children scored higher on the VCI than on other indexes, with the lowest score found on the Processing Speed Index. WISC-IV cognitive profiles were consistent with, but more pronounced than WISC-III profiles. Cognitive profiles are a valuable diagnostic tool for differential diagnosis, keeping in mind that children on the autism spectrum might be more sensitive to the choice of subtests used to assess intelligence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".