Using users’ physiological responses for the estimation of websites’ aesthetic judgments
Year: 2019
Type: preprint
Abstract: The aesthetic appearance of websites can influence the perception of their usability, reliability,and trustworthiness. Several studies investigated the relationship between single aestheticfeatures and explicit aesthetic judgments, demonstrating the existence of an attribution bias.However, only a limited amount of studies focused on the interaction between multiple visualproperties and have considered not only explicit ratings, but also implicit judgments. In thiswork, we employ a novel approach, based on the analysis of physiological signals (implicitmeasures) and the application of machine learning and neural network models to predict users’perceived aesthetic pleasure from the empirical analysis of web pages’ advanced visual properties(e.g. symmetry, visual complexity, colorfulness, ratio between visual and textual areas). Youngadults (N=59, 33 females, Mean age = 21.52 years) assessed the aesthetic appeal of websitesand emotional pictures while their physiological activity was recorded. Results using recursivepartitioning and generalized linear models demonstrate the possibility of predicting the averageaesthetic rating of a website using both explicit (behavioral ratings) and implicit measures(physiological activities).
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3162069742