Virtual Personality Assessment Laboratory (V-PAL)
Overview: Given the popularity of games, and their use for education, health and training, there is a unique opportunity to develop games that can identify and adapt to personality differences thus increasing their success. This project, called V-PAL, aims to address this link between personality and game behavior. The goal of the project is developing a preliminary taxonomy of game-based mechanics, grounded on personality research, that allows researchers to use in-game behavioral patterns of individual players to assess their real-world personality. Such virtual personality detection mechanism can then be used by other researchers to adapt the game system further, which would be one ultimate goal – the design of more personalized and adaptive applications that may improve impact on large societal problems.
Previous research has confirmed the existence of correlations between game behavior and various models of personality and motivation. However, none of the previous works adopted personality theories to guide the design and development of a game aimed at assessing players’ personality. This is the target of this proposal. Specifically, the aim is to leverage already existing work in personality to develop a game able to detect and identify specific aspects of personality. The design of the game is driven by personality theory and validated by a wide range of personality measures such as the Need For Cognition, the California Q-Sort, the Reiss Motivation Profiler and the Five Factor Model. The game will be developed as a set of modular challenges and situations that make use of the mechanics individuated in the taxonomy. These situations are constructed to elicit personality preferences. The game will be validated through two iterations to ensure that scenarios are assimilated and that they conform to the intention of the designers. A final summative evaluation will be administered utilizing in-game data as well as various personality measures such as scores from personality questionnaires, informant interviews, and behavior coding. Correlation analysis will be used to investigate relationships between in-game choices emerging from the context of play and personality scores.
Faculty Members: Alessandro Canossa (PI), Magy Seif El-Nasr (Co-PI), and Randy Colvin (Co-PI).