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Introduction Exposure to natural and rural landscapes has been associated with stress reduction and psychological restoration. However, conventional photograph-based experiments often provide limited control over individual landscape elements and their positions within the visual field. This study examined whether depth-controlled, AI-generated rural landscape stimuli could support stress recovery and clarify how foreground and midground elements relate to landscape preference and EEG-based responses. Methods Depth estimation, layer masking, and FLUX-based image editing were used to construct 27 controlled rural landscape stimuli. Selected foreground and midground elements were modified while the background context was kept relatively stable. Thirty participants completed a stress-induction task followed by rural landscape viewing and a duration-matched blank-screen control condition. Systolic blood pressure, diastolic blood pressure, heart rate, EEG beta/alpha (β/α) stress index, and POMS mood scores were measured at baseline, after stress induction, and after recovery. Landscape preference ratings and image-level EEG responses were analyzed for the rural landscape stimuli. Results Compared with blank-screen viewing, rural landscape viewing produced significantly greater recovery in all seven stage-level outcomes after Holm correction. Rural-minus-blank recovery differences were −4.45 mmHg for systolic blood pressure, −3.93 mmHg for diastolic blood pressure, −5.85 bpm for heart rate, and −0.10 for the EEG β/α stress index. Within the rural landscape stimuli, foreground water was associated with higher preference ratings (estimate = 0.500, SE = 0.084, p < 0.001), whereas foreground road was associated with lower preference (estimate = −0.354, SE = 0.084, p < 0.001). EEG-based image-level associations were weaker. Foreground water and forest-based context were associated with lower β/α values in exploratory models, but the matched water-versus-road EEG contrast was not significant after correction. Discussion Validated AI-generated rural images can serve as controlled stimuli in environmental psychology research. The findings suggest that rural landscape viewing may promote stronger physiological, EEG-based, and affective recovery than passive blank-screen viewing, while visual preference and EEG-based restoration may follow partly distinct depth-layered pathways. Given the exploratory design, the depth-layer findings should be interpreted cautiously and require confirmation in more systematically balanced stimulus sets.
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Xiong et al. (2026) studied this question.
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