Neurocognitive Variety in Neurotypical Environments: The Source of “Deficit” in Autism
Bibliographic record
Abstract
As recent developments in autism research offer alternative explanations to the mainstream options, it can now be argued that the so-called cognitive deficits in the social domain associated with autism have been mischaracterized or, at least, oversimplified. We will use predictive models within a 4E (i.e., embodied, embedded, enactive and extended) conception of cognition to address the question of cognitive impairment in psychiatrics and autism. Such models force us to reassess what “cognitive deficit” means by integrating the environment not only in its usual sense (evo-developmental), but by understanding all cognitive performances as embedded in environments (or fields of affordances) that shape and sustain them. By adopting a predictive 4E perspective, we aim to show that the “cognitive deficits” associated with autism are in fact mismatches between environmental resources and the particular form of neurological functioning of autistic people (neurodiversity), brought about by the fact that the cultural niches that set up the relevant fields of affordances are structured by and for neurotypicals. This mismatch leads to epistemic injustices, both testimonial and hermeneutic, that feed back into research on autism and clinical approaches, thereby making the “deficits” appear based on individual shortcomings. In this context, autism interventions should partly focus on the development of social policies aimed at modifying those aspects of cultural niches that make environments unsuitable for the full development of all individuals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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; 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".