This paper offers a prospectus of what might be achievable in the development of emotional recognition devices. It provides a conceptual overview of the free energy principle; including Markov blankets, active inference, and—in particular—a discussion of selfhood and theory of mind, followed by a brief explanation of how these concepts can explain both neural and cultural models of emotional inference. The underlying hypothesis is that emotion recognition and inference devices will evolve from state-of-the-art deep learning models into active inference schemes that go beyond marketing applications and become adjunct to psychiatric practice. Specifically, this paper proposes that a second wave of emotion recognition devices will be equipped with an emotional lexicon (or the ability to epistemically search for one), allowing the device to resolve uncertainty about emotional states by actively eliciting responses from the user and learning from these responses. Following this, a third wave of emotional devices will converge upon the user's generative model, resulting in the machine and human engaging in a reciprocal, prosocial emotional interaction, i.e., sharing a generative model of emotional states.

中文翻译：

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情绪识别的自由能原理研究

本文概述了情感识别设备的开发可能实现的目标。它提供了自由能原理的概念概述；包括马尔可夫毯子、主动推理，特别是对自我和心理理论的讨论，然后简要解释这些概念如何解释情感推理的神经和文化模型。基本假设是，情绪识别和推理设备将从最先进的深度学习模型演变为主动推理方案，超越营销应用并成为精神病学实践的辅助手段。具体来说，本文提出第二波情绪识别设备将配备情绪词典（或认知上搜索情绪词典的能力），允许设备通过主动引发用户的响应并从中学习来解决情绪状态的不确定性。这些回应。在此之后，第三波情感设备将汇聚到用户的生成模型上，导致机器和人类进行相互的、亲社会的情感交互，即共享情感状态的生成模型。