Mapping Visit Behaviors in a Virtual Art Gallery to Visitor Engagement Profiles Using Latent Class Analysis

Virtual art galleries represent a rapidly growing and potentially unique context for art engagement, but little is known about the psychological nature of these experiences. To better characterize virtual art gallery visits, we quantified a set of in-depth behaviors related to art viewing, navigation, and intensive art engagement in a virtual gallery. Data included coordinate position and gaze proxy data, recorded from 264 unconstrained participant visits to a virtual gallery that contained 24 diverse artworks curated throughout 3 rooms. Nine classified visit behaviors—related to the overall visit, navigation, art viewing, and deep engagement—were used in a latent class analysis as indicators for estimating possible visitor engagement classes. The analysis resulted in a 4-class model which classified visitors as disengaged, typical, engaged, and revisitors. Finally, we examined how each class differed in terms of their art knowledge, personality, and other individual differences. Significant differences were found for several variables, including aesthetic fluency, openness to experience, extraversion, age, and nausea.