# Analysis of behavioral sequences in social interactions of autistic children: a latent class model based on structured play observation

## TL;DR

This study uses a new method to analyze social behaviors in autistic children during play, revealing patterns that could help improve interventions.

## Contribution

The study introduces a dynamic systems approach to analyze behavioral sequences in ASD children using latent class and temporal analysis.

## Key findings

- ASD children showed lower behavioral sequence complexity compared to typically developing children.
- Three distinct behavioral patterns were identified using latent class analysis.
- The low interaction–high avoidance group had the poorest response to intervention.

## Abstract

This study addresses the unclear dynamic mechanisms underlying social interactions in children with autism spectrum disorder (ASD) by constructing a structured play observation framework.

By combining latent class analysis (LCA) and temporal analysis techniques, this study systematically analyzed the heterogeneous characteristics of social behavioral sequences. Using a longitudinal tracking and cross-sectional design, multimodal data (video coding and physiological indicators) were collected from 60 children with ASD and 40 typically developing (TD) children.

The behavioral sequence complexity of the ASD group was significantly lower than that of the TD group, exhibiting an “avoidance–rigid” cyclical pattern. The LCA model identified three behavioral patterns: high interaction, medium interaction–rigid, and low interaction–high avoidance. The low interaction–high avoidance group demonstrated the poorest intervention response rate.

This study innovatively applies dynamic systems theory to the ASD field, demonstrating that behavioral sequences can serve as intervention targets. It advances evaluation tools from static description to dynamic prediction, providing a scientific basis for personalized intervention planning. The integration of structured observation and multimodal data analysis deepens the understanding of the dynamic mechanisms underlying social impairments in ASD and holds significant theoretical and practical value.