Fuzzy Affective Player Models: A Physiology-Based Hierarchical Clustering Method

Current approaches to game design improvements rely on time-consuming gameplay testing processes, which rely on highly subjective feedback from a target audience. In this paper, we propose a generalizable approach for building predictive models of players’ emotional reactions across different games and game genres, as well as other forms of digital stimuli. Our input agnostic approach relies on the following steps: (a) collecting players' physiologically-inferred emotional states during actual gameplay sessions, (b) extrapolating the causal relations between changes in players' emotional states and recorded game events, and (c) building hierarchical cluster models of players' emotional reactions that can later be used to infer individual player models via fuzzy cluster membership vectors. We expect this work to benefit game designers by accelerating the affective playtesting process through the offline simulation of players' reactions to game design adaptations, as well as to contribute towards individually-tailored affective gaming.