Correlating fluency theory-based visual aesthetic liking of landscape with landscape types and features

Abstract

ABSTRACT People inherently assess landscapes by creating spontaneous aesthetic liking judgments based on the surrounding stimuli. To understand these judgements objectively, use may be made of the fluency theory of aesthetic pleasure (the psychological processes through which people experience beauty). This study aims to predict people’s visual aesthetic preferences based on fluency theory and to correlate these preferences with landscape types and features. An ordinary least squares (OLS) regression model was developed to predict visual aesthetic liking, using image statistics as explanatory variables. We determined types of landscape using Landscape Character Assessment (LCA) and applied viewshed analyses distinguishing between near, medium, and far zones. We identified landscape features by content analysis making use of machine learning-based image recognition supplied by Google Cloud Vision API. The results show that vegetation and geological forms were the most significant features for people’s visual aesthetic liking, followed by waterscapes and built structures/human settlements. The viewshed analyses indicated that ‘medium-altitude, low-gradient artificial areas’ were visible in photographs with high aesthetic visual liking in all zones (i.e., at all distances). When the photographs showing this type of landscape are examined, the artificial areas in the photographs turn out to consist mostly of historical buildings or remains. This finding suggests that historical sites are not just important for their cultural value, but for their visual aesthetic value as well.