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    Exploring the relationship between audio–visual perception in Fuzhou universities and college students' attention restoration quality using machine learning by Shaofeng Chen, Zhengyan Chen, Jiawen Hong, Xiaowen Zhuang, Chenxi Su, Zheng Ding

    Published 2025-07-01
    “…XGBoost models and SHAP interpretability analysis were employed to reveal the effects and interaction mechanisms of variables.Results(1) Attention recovery quality is significantly higher in liberal arts and agricultural/forestry universities than in science and engineering universities, with boundary effects and the synergistic design of humanistic soundscapes being key factors; (2) SHAP analysis identifies humanistic soundscapes, natural soundscapes, and color complexity as core influencing factors, with their effects exhibiting significant threshold characteristics; (3) Linear interaction mechanisms among audiovisual elements are discovered, such as the interaction between vegetation density and building enclosure degree enhancing recovery efficacy, and the synergistic design of musical soundscapes and paving materials can optimize perceptual experiences.ConclusionBy innovatively integrating multi-source data and machine learning techniques, this study systematically analyzes the relationship between campus audiovisual environments and attention recovery, breaking through the limitations of traditional linear analysis. …”
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