Superior estimation abilities in two autistic spectrum children
Bibliographic record
Abstract
Anecdotal reports of superior estimation abilities in autistic individuals (e.g., Sacks, 1985 Sacks, O. 1985. The man who mistook his wife for a hat and other clinical tales, London, , UK: Duckworth. [Google Scholar]) have never been confirmed empirically. We present here case studies of 2 children with autistic spectrum diagnoses and report remarkable abilities in estimation for several quantifiable dimensions. K.T. and G.T. were tested at 9 years of age for estimation of rank, numerosity, time, weight, length, surface, distance, and precise enumeration for small numbers. Their performances were compared to those of 6 age- and IQ- matched comparison children. K.T. demonstrated a superior level of performance in estimating rank (e.g., which set has larger numerosity?) but his performance in other tasks was average. G.T. displayed outstanding performance in estimating numerosity, time, weight, surface, length, and distance, with average performance in other tasks. These results show that certain autistic spectrum individuals may develop superior and highly specialized abilities in estimation. We discuss these findings in relation to the role of “veridical mapping” in the development of special ability (Mottron, Dawson, & Soulières, 2009 Mottron, L., Dawson, M. and Soulières, I. 2009. Enhanced perception in savant syndrome: Patterns, structure and creativity. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 364: 1385–1391. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Mottron, Dawson, Soulieres, Hubert, & Burack, 2006a). Veridical mapping is the detection of isomorphism within a code, between two codes, or between one code and isomorphic elements of the world. Within this framework, it is proposed that estimation abilities, like absolute pitch, rely on the ability to map a verbal code with a specific magnitude of a psychophysical dimension.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".