Veridical mapping in the development of exceptional autistic abilities
Bibliographic record
Abstract
Superior perception, peaks of ability, and savant skills are often observed in the autistic phenotype. The enhanced perceptual functioning model (Mottron et al., 2006a) emphasizes the increased role and autonomy of perceptual information processing in autistic cognition. Autistic abilities also involve enhanced pattern detection, which may develop through veridical mapping across isomorphic perceptual and non-perceptual structures (Mottron et al., 2009). In this paper, we elaborate veridical mapping as a specific mechanism which can explain the higher incidence of savant abilities, as well as other related phenomena, in autism. We contend that savant abilities such as hyperlexia, but also absolute pitch and synaesthesia, involve similar neurocognitive components, share the same structure and developmental course, and represent related ways by which the perceptual brain deals with objective structures under different conditions. Plausibly, these apparently different phenomena develop through a veridical mapping mechanism whereby perceptual information is coupled with homological data drawn from within or across isomorphic structures. The atypical neural connectivity characteristic of autism is consistent with a developmental predisposition to veridical mapping and the resulting high prevalence of savant abilities, absolute pitch, and synaesthesia in autism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".