Lab member Xuanjun (Jason) Gong will present a Top Paper in the Communication and Social Cognition Division at NCA21. The paper uses drift diffusion modeling to test core hypotheses drawn from Mood Management Theory. Jason’s slides are available online.
Presentation Title: Computationally Modeling Mood Management Theory: A Drift-Diffusion Model of People’s Preference for Valence and Arousal
Authors: Xuanjun (Jason) Gong, Richard Huskey, Allison Eden, Ezgi Ulusoy
Presentation Date: 11/19
Presentation Time: 3:30 PM – 4:45 PM PST
Open Data, Open Materials: https://github.com/cogcommscience-lab/movie_selection
Abstract: Mood Management Theory predicts that people select entertainment content to maintain affective homeostasis. However, this hypothesis lacks a formal quantification of each affective attributes’ separate impact on an individual’s preference for media content, as well as an integrated cognitive mechanism explaining media selection. Here we present a computational model that mathematically formalizes this affective media decision making process. We empirically tested this formalization with the Drift Diffusion Model using two decision making experiments. We found people prefer low valence and high arousal media content additively, and induced low valence mood makes people prefer low valence media content more. We also found that people make less cautious media selection decisions when the two choices have larger valence differences. Our results support the proposed mathematical formalization of affective attributes’ influence on media selection, challenge core predictions drawn from Mood Management Theory, and introduce a new mechanism (response caution) that underpins media selection.