Heritable aspects of biological motion perception and its covariation with autistic traits
Bibliographic record
Abstract
The ability to detect biological motion (BM) and decipher the meaning therein is essential to human survival and social interaction. However, at the individual level, we are not equally equipped with this ability. In particular, impaired BM perception and abnormal neural responses to BM have been observed in autism spectrum disorder (ASD), a highly heritable neurodevelopmental disorder characterized by devastating social deficits. Here, we examined the underlying sources of individual differences in two abilities fundamental to BM perception (i.e., the abilities to process local kinematic and global configurational information of BM) and explored whether BM perception shares a common genetic origin with autistic traits. Using the classical twin method, we found reliable genetic influences on BM perception and revealed a clear dissociation between its two components-whereas genes account for about 50% of the individual variation in local BM processing, global BM processing is largely shaped by environment. Critically, participants' sensitivity to local BM cues was negatively correlated with their autistic traits through the dimension of social communication, with the covariation largely mediated by shared genetic effects. These findings demonstrate that the ability to process BM, especially with regard to its inherent kinetics, is heritable. They also advance our understanding of the sources of the linkage between autistic symptoms and BM perception deficits, opening up the possibility of treating the ability to process local BM information as a distinct hallmark of social cognition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".