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.

中文翻译：

---

情绪管理理论的计算建模：人们对媒体效价和唤醒的偏好选择的漂移扩散模型

情绪管理理论 (MMT) 假设人们选择娱乐内容是为了维持情感稳态。然而，该假设缺乏对每个情感属性对个人媒体内容选择的单独影响的正式量化，以及解释媒体选择的综合认知机制。在这里，我们提出了一个计算决策模型，该模型在数学上形式化了这种情感媒体决策过程。我们使用三个决策实验通过漂移扩散模型对这种形式化进行了实证测试。与 MMT 相反，所有三项研究都表明，人们更喜欢负面效价和高唤醒度的媒体内容，并且盛行的情绪不会像 MMT 预测的那样影响媒体选择。我们还发现，当选择具有较大的价差时，人们会不那么谨慎。我们的结果支持所提出的情感属性对媒体选择的影响的数学形式化，挑战从 MMT 得出的核心预测，并引入一种新的媒体选择机制（反应谨慎）。