Mood is an integrative and diffuse affective state that is thought to exert a pervasive effect on cognition and behavior. At the same time, mood itself is thought to fluctuate slowly as a product of feedback from interactions with the environment. Here we present a new computational theory of the valence of mood—the Integrated Advantage model—that seeks to account for this bidirectional interaction. Adopting theoretical formalisms from reinforcement learning, we propose to conceptualize the valence of mood as a leaky integral of an agent’s appraisals of the Advantage of its actions. This model generalizes and extends previous models of mood wherein affective valence was conceptualized as a moving average of reward prediction errors. We give a full theoretical derivation of the Integrated Advantage model and provide a functional explanation of how an integrated-Advantage variable could be deployed adaptively by a biological agent to accelerate learning in complex and/or stochastic environments. Specifically, drawing on stochastic optimization theory, we propose that an agent can utilize our hypothesized form of mood to approximate a momentum-based update to its behavioral policy, thereby facilitating rapid learning of optimal actions. We then show how this model of mood provides a principled and parsimonious explanation for a number of contextual effects on mood from the affective science literature, including expectation- and surprise-related effects, counterfactual effects from information about foregone alternatives, action-typicality effects, and action/inaction asymmetry. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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作为综合优势的情绪模型。


情绪是一种综合且分散的情感状态，被认为对认知和行为产生普遍影响。与此同时，情绪本身被认为是与环境互动反馈的产物，会缓慢波动。在这里，我们提出了一种新的情绪效价计算理论——综合优势模型——试图解释这种双向相互作用。采用强化学习的理论形式主义，我们建议将情绪效价概念化为智能体对其行为优势评估的泄漏积分。该模型概括并扩展了先前的情绪模型，其中情感效价被概念化为奖励预测误差的移动平均值。我们给出了综合优势模型的完整理论推导，并提供了生物代理如何自适应地部署综合优势变量以加速复杂和/或随机环境中的学习的功能解释。具体来说，利用随机优化理论，我们建议代理可以利用我们假设的情绪形式来近似对其行为策略进行基于动量的更新，从而促进最佳行动的快速学习。然后，我们展示这种情绪模型如何为情感科学文献中的许多对情绪的情境影响提供原则性和简约的解释，包括与期望和惊喜相关的影响、有关已知替代方案的信息的反事实影响、行动典型性影响、以及行动/不行动的不对称性。 （PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）