Quantifying physiological arousal to rural visual configurations: A mixed-effects modelling framework for hypothesis generation

Abstract

Rural revitalization is reshaping village landscapes, but prevailing visual-assessment approaches still rely heavily on greenness, land-use composition, ecological indicators, or subjective ratings. These measures support planning but provide limited evidence about which combinations of natural, built, and open elements are associated with short-term physiological arousal at the human viewing scale. We therefore developed a proof-of-concept framework that converts rural photographs into measurable visual profiles and evaluates short-term autonomic responses during controlled viewing. Four villages in eastern China were selected using five site criteria to capture varied planning and landscape conditions. Candidate scenes were first screened during field walking using wearable sensing and synchronized first-person video, after which 40 photographs were evaluated under controlled laboratory viewing. Visual characteristics were quantified through the proportions of visible scene elements, edge density, color entropy, and exploratory principal component analysis, from which contrasting scene configurations were described post hoc rather than specified in advance. Mixed-effects models examined changes in skin conductance and heart rate variability while accounting for repeated observations within participants, and the physiological results were subsequently interpreted together with the visual-feature profiles at the configuration level. Some visual configurations were associated with higher skin conductance responses, whereas changes in heart rate variability did not differ significantly among the image sets. The observed arousal patterns could not be reduced to greenness, edge density, or color entropy alone; instead, the combined organization of built elements, natural elements, openness, and structural detail provided a more informative basis for interpretation. The contribution lies in adapting field screening, computer vision, physiological sensing, and trial-level mixed-effects modelling as a proof-of-concept framework for hypothesis generation in rural visual assessment. The framework is not intended to infer affective valence, restorative mechanisms, or long-term health effects, but to identify physiologically salient rural visual configurations for future validation through virtual reality (VR), field-based experiments, or valence-sensitive measures.