Application of SAADSC to analyze the influence of family function on children’s social skill development: A study combining neural networks and biological behavioral data
Abstract
Understanding the biological foundations of children’s social skill development provides critical insights into how family dynamics shape behavioral outcomes. This study leverages advanced deep learning techniques to analyze the complex interplay between family function and children’s social competence. Specifically, the self-attentive adversarial deep subspace clustering (SAADSC) algorithm was applied to process and interpret high-dimensional data, including neurobiological signals, hormonal markers, and behavioral observations. The results reveal a strong correlation between family cohesion and neural network patterns associated with social skill development. Integrating biological indicators such as cortisol levels and heart rate variability with social-behavioral metrics significantly enhanced the model’s predictive accuracy. By combining neural network analysis with biological and behavioral data, the study highlights the crucial role of biological mechanisms in mediating the influence of family environments on social development. These findings emphasize the value of interdisciplinary approaches in advancing our understanding of developmental biology and social neuroscience.
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