A longitudinal study on language acquisition in monozygotic twins concordant for autism and hyperlexia
Bibliographic record
Abstract
BACKGROUND: Hyperlexia, a strong orientation towards written materials, along with a discrepancy between the precocious acquisition of decoding skills and weaker comprehension abilities, characterizes up to 20% of autistic children. Sometimes perceived as an obstacle to oral language acquisition, hyperlexia may alternatively be the first step in a non-social pathway of language acquisition in autism. METHOD: We describe two monozygotic twin brothers, both autistic and hyperlexic, from the ages of 4 to 8 years old. Following an in-depth diagnostic assessment, we investigated cross-sectionally and longitudinally their verbal and non-verbal cognitive abilities, language, reading and writing skills, interests, and strengths. RESULTS: The twins' features, including their high non-verbal level of intelligence, their special interests, and their skills in various domains, were highly similar. Their language consisted exclusively of letters and numbers until their fourth year. After that, their vocabulary broadened until they developed full sentences, and their perception-related interests expanded and merged over time to serve the development of other skills. CONCLUSION: Our results show that hyperlexic skills can be harnessed to favor oral language development. Given the strong concordance between the twins' cognitive and behavioral phenotypes, we discuss the environmental and genetic influence that could explain their abilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".