Beyond nature: Investigating the restorative properties of environments through the comparison of historical, modern urban, and natural image exposures
Year: 2026
Type: article
Abstract: The present study investigates the restorativeness, psychological, and psychophysiological benefits of exposure to different environments—natural, historical, mixed, and modern urban—using an immersive laboratory and AI-generated environments. Participants were exposed to environments prior to a stress-induction phase and completed self-reports alongside psychophysiological measurements (heart rate and electrodermal activity). Results revealed that natural and mixed environments were perceived as most restorative, whereas modern urban environments were rated as the least restorative and most negatively valenced. Historical environments, rated lower in a global score of restorativeness, were perceived as fascinating, coherent, engaging, and pleasant, and they increased positive emotions and reduced arousal levels after stress induction, similarly to natural environments. Historical settings were found to be the less boring environments. Overall, findings contribute to the evidence that both natural and certain built environments, particularly historical ones, can support emotional recovery and psychological well-being, whereas modern urban settings may hinder it. • Examination of restorative responses to AI-generated Natural, Historical, Mixed, and Modern environments. • Historical settings improved mood and restorativeness, similar to natural ones. • Modern urban environments elicit higher stress and lower restorative qualities. • Findings support integrating nature and history to enhance urban well-being.
Authors: L. Miola, N. Cellini, S. Bellini, F. Pazzaglia
Institution: University of Padua
Language: English
Topic: Urban Green Space and Health
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,380
Funder: European Commission
OpenAlex ID: W7124736710
