Computationally modeling mood management theory: a drift-diffusion model of people’s preferential choice for valence and arousal in media

Abstract

Abstract Mood management theory (MMT) hypothesizes 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 media content selection, as well as an integrated cognitive mechanism explaining media selection. Here we present a computational decision-making model that mathematically formalizes this affective media decision-making process. We empirically tested this formalization with the drift-diffusion model using three decision-making experiments. Contrary to MMT, all three studies showed that people prefer negatively valenced and high-arousal media content and that prevailing mood does not shape media selection as predicted by MMT. We also discovered that people are less cautious when choices have larger valence differences. Our results support the proposed mathematical formalization of affective attributes’ influence on media selection, challenge core predictions drawn from MMT, and introduce a new mechanism (response caution) for media selection.