Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages

The aesthetic appearance of websites can influence the perception of their usability, reliability, and trustworthiness. Several studies investigated the relationship between single aesthetic features and explicit aesthetic judgments, demonstrating the existence of attribution bias. However, only a limited amount of studies focused on the interaction between multiple visual properties and have considered not only explicit ratings but also implicit judgments. In this work, we investigate the differences between explicit and implicit judgments of web pages. Our approach, based on the analysis of physiological signals, uses machine learning and neural network models to estimate users' implicit aesthetic pleasure. Young adults (\textit{N}=59, 33 females, Mean age = 21.52 years) assessed the aesthetic appeal of websites and emotional pictures while their physiological activity was recorded. Results demonstrate a moderate association between the visual properties of web pages and both implicit and explicit ratings.