The Developmental Approach to Autism Science: Considering Cognitive Ability in the Age of Neuroimaging Research
Bibliographic record
Abstract
Over the last 75 years, the developmental approach to developmental disability has touted-as one of its central tenets-the need to consider development in our understanding of developmental difference. This idea can be examined in many ways, but operationally and pragmatically, it has meant considering the impact of cognitive ability on our dependent variables and being mindful of the comparisons we make, and in the conclusions that we draw from those comparisons. The argument for considering the role of cognitive ability in our understanding of group differences originates in the study of intellectual disability, where there was a desire to understand profiles of strengths and weaknesses. This profile, or group difference approach, is also common to the field of autism and is particularly relevant to neuroscience-focused work. While many autistic people do not have co-occurring intellectual disability, IQ differences between groups can still obscure findings. Here, we aim to highlight reasons why the consideration of cognitive ability in neuroscience research on autism is relevant and critical to the field.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".