Characterizing the Musical Phenotype in Individuals With Williams Syndrome
Bibliographic record
Abstract
Williams Syndrome (WS), a neurodevelopmental genetic disorder, is characterized by peaks and valleys in mental function: substantial impairments in cognitive domains such as reasoning, arithmetic ability, and spatial cognition, alongside relatively preserved skills in social domains, face processing, language, and music. We report the results of a comprehensive survey on musical behaviors and background administered to the largest sample of individuals with WS to date (n = 118, mean age = 20.4), and compare the results to those obtained from a control group of typically developing normal individuals (n = 118, mean age = 20.9) and two groups of individuals with other neurodevelopmental genetic disorders, Autism (n = 30, mean age = 18.2) and Down Syndrome (n = 40, mean age = 17.2). Individuals with WS were found to be rated higher in musical accomplishment, engagement, and interest than either of the comparison groups, and equivalent on most measures to the control group. Compared to all other groups including the controls, the WS individuals displayed greater emotional responses to music, manifested interest in music at an earlier age, and spent more hours per week listening to music. In addition, the effects of music listening (whether positive or negative) tended to last longer in the WS group. A factor analysis extracted seven principal components that characterize the musical phenotype in our sample, and discriminant function analysis of those factors was able to successfully predict group membership for the majority of cases. We discuss the neurobiological implications of these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".