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Background <p>Single-paradigm, single-measure eye-tracking protocols have demonstrated utility in distinguishing between autistic and non-autistic children although effect sizes and reproducibility vary. There is a need for brief, scalable digital behavioral biomarkers that integrate complementary information from multiple eye-tracking paradigms and achieve improved accuracy in combination.</p> Methods <p>74 autistic and 63 non-autistic children aged 24–72 months performed five different eye-tracking paradigms (facial emotion processing, gaze-following, dynamic social versus geometric patterns, social interaction, and spinning) lasting 3.25&#xa0;min in hospital or kindergarten settings. A broad set of fixation-based metrics was extracted from each paradigm. We compared discrimination performance of single-paradigm models versus a combined multi-paradigm model using random forest (RF) classifiers. In the autistic group, symptom severity was assessed using standardized clinical measures. We further evaluated whether paradigms contributed complementary information, estimated potential clinical value of multi-paradigm models, and conducted exploratory subgrouping analyses to examine whether eye-tracking–defined profiles aligned with symptom-based groupings.</p> Results <p>All individual paradigms distinguished between autistic and non-autistic children, but RF models found a combined paradigm-based model performed best, achieving an AUC of 95% and an accuracy of 90%. Representational similarity analysis indicated that paradigms contributed partially distinct information rather than reflecting a single redundant dimension of social attention. Decision curve analysis demonstrated that the multi-paradigm model provided added net benefit across clinically relevant threshold probabilities compared with strategies based on treating all or no children as autistic. Clustering of eye-tracking features revealed three autistic subgroups with distinct visual preference profiles, whereas clustering based on clinical symptoms alone identified only two subgroups.</p> Limitations <p>Sex imbalance and group differences in developmental quotient may have confounded some effects. Models were only internally cross-validated in a single cohort and decision curve analysis relied on assumed clinic prevalence, so external validation and testing in larger, more diverse and high-risk samples, including other neurodevelopmental conditions, are needed.</p> Conclusions <p>A brief multi-paradigm eye-tracking battery yields robust case–control discrimination and non-redundant behavioral readouts, suggesting that it may complement symptom-based approaches and provide a scalable behavioral framework for future studies seeking to bridge molecular findings with observable autistic profiles.</p>

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Advantages of combining multiple eye-tracking paradigms for distinguishing young autistic from non-autistic children

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Dan Xu,
  • Lan Zhang,
  • Xiangqin Wang,
  • Xun Zeng,
  • Linghong Huang,
  • Yanan Qing,
  • Menghan Zhou,
  • Qin Li,
  • Xiaojiao Yang,
  • Weihua Zhao,
  • Shuxia Yao,
  • Jiao Le,
  • Keith M. Kendrick

摘要

Background

Single-paradigm, single-measure eye-tracking protocols have demonstrated utility in distinguishing between autistic and non-autistic children although effect sizes and reproducibility vary. There is a need for brief, scalable digital behavioral biomarkers that integrate complementary information from multiple eye-tracking paradigms and achieve improved accuracy in combination.

Methods

74 autistic and 63 non-autistic children aged 24–72 months performed five different eye-tracking paradigms (facial emotion processing, gaze-following, dynamic social versus geometric patterns, social interaction, and spinning) lasting 3.25 min in hospital or kindergarten settings. A broad set of fixation-based metrics was extracted from each paradigm. We compared discrimination performance of single-paradigm models versus a combined multi-paradigm model using random forest (RF) classifiers. In the autistic group, symptom severity was assessed using standardized clinical measures. We further evaluated whether paradigms contributed complementary information, estimated potential clinical value of multi-paradigm models, and conducted exploratory subgrouping analyses to examine whether eye-tracking–defined profiles aligned with symptom-based groupings.

Results

All individual paradigms distinguished between autistic and non-autistic children, but RF models found a combined paradigm-based model performed best, achieving an AUC of 95% and an accuracy of 90%. Representational similarity analysis indicated that paradigms contributed partially distinct information rather than reflecting a single redundant dimension of social attention. Decision curve analysis demonstrated that the multi-paradigm model provided added net benefit across clinically relevant threshold probabilities compared with strategies based on treating all or no children as autistic. Clustering of eye-tracking features revealed three autistic subgroups with distinct visual preference profiles, whereas clustering based on clinical symptoms alone identified only two subgroups.

Limitations

Sex imbalance and group differences in developmental quotient may have confounded some effects. Models were only internally cross-validated in a single cohort and decision curve analysis relied on assumed clinic prevalence, so external validation and testing in larger, more diverse and high-risk samples, including other neurodevelopmental conditions, are needed.

Conclusions

A brief multi-paradigm eye-tracking battery yields robust case–control discrimination and non-redundant behavioral readouts, suggesting that it may complement symptom-based approaches and provide a scalable behavioral framework for future studies seeking to bridge molecular findings with observable autistic profiles.