Against the backdrop of rapid digitization of cultural heritage, assessing the public’s genuine perception of digital heritage has become a critical issue in the study of cultural sustainability and communication. This study takes the “Digital Dunhuang Museum” exhibition in Guangzhou as a case, focusing on the differences and underlying mechanisms in public aesthetic perception of digital Dunhuang murals. Integrating eye-tracking experiments, subjective image evaluations, and semi-structured interviews, the research innovatively introduces multimodal visual behaviour and physiological data as core indicators in the field of digital cultural heritage. It systematically compares the explicit attitudes and implicit responses of audiences with different artistic backgrounds during the aesthetic perception process. The results reveal that participants with an art-related background show significantly higher scores in subjective dimensions such as pleasure, attraction, and visiting intention. They also demonstrate stronger visual engagement and emotional arousal in physiological dimensions, including the number of fixations, total fixation duration, and pupil diameter changes. This study constructs a mechanism of aesthetic perception for digital cultural heritage based on “visual attention–cognitive processing–emotional arousal”, enriching the public’s understanding of digital cultural heritage conservation and communication from both cognitive and emotional perspectives. The findings provide empirical support for the design of digital exhibitions of cultural heritage and expand the methodological and cognitive approaches in cultural sustainability research, offering important theoretical and practical implications.
Building similarity graph...
Analyzing shared references across papers
Loading...
Yuxin Chen
Hebei University of Economics and Business
Yang Peng
Southern University of Science and Technology
Yuanjun Tan
South China Normal University
Sustainability
South China Normal University
Building similarity graph...
Analyzing shared references across papers
Loading...
Chen et al. (Tue,) studied this question.
synapsesocial.com/papers/68bb5f3e6d6d5674bcd03474 — DOI: https://doi.org/10.3390/su17177887