如何理解情绪——对预测心智—情绪建构论的辩护
作者简介:吴雪梅,兰州大学哲学社会学院讲师(甘肃兰州 730000)。
基金项目:
本文为兰州大学中央高校基本科研业务费专项资金资助“基于预测心智的情绪建构论研究:一种AI增强的视角”(2025lzujbkyqk002)的阶段性成果
摘要: 如何理解情绪是目前情绪识别技术所面临的基础困境。当前西方心灵哲学界在情绪定义上主要可划分为三类:实在论、建构论与互构论。有证据表明,持有实在论立场的激进生成论与持有建构论立场的预测心智—情绪建构论最值得情感计算领域重视。其中,预测心智—情绪建构论主张情绪实例是大脑依据过去的经验,通过概念对感觉输入进行预测的心理事件。激进生成论主张情绪是生物体通过身体与环境直接互动的行为反应模式。近期,激进生成论对基于预测心智的感知理论提出质疑,主张坚持表征主义的预测心智论无法解释穆勒—莱尔错觉。如果该质疑成立,则意味着预测心智—情绪建构论的情绪定义也将受到挑战。但根据对相关研究的分析可以发现,激进生成论的质疑不成立。预测心智—情绪建构论通过强调情绪的主动性与动态性,不仅能够在哲学层面提供一个更具理论前景的情绪定义,而且也有助于情绪识别技术有效提升其在情绪识别上的实时能力与准确性。
How to Understand Emotion——A Defense of the Theory of Predictive Mind-Constructed Emotion
Abstract: Emotion recognition technology faces a fundamental challenge:the lack of consensus on the definition of emotion. In current Western philosophy of mind, there are three main types of views on emotion:realism, constructivism, and mutual constitution theory. Evidence suggests that two standpoints merit particular attention in affective computing:radical enactivism and the theory of predictive mind-constructed emotion. The latter holds that an instance of emotion is a psychological event, where the brain categorizes incoming sensory inputs as similar to past experiences through concepts. In contrast, radical enactivism argues that emotion is a behavioral response mode of organisms, arising from their direct bodily interaction with the environment. Recently, radical enactivism has raised questions about the theory of predictive mind. It argues that the theory of predictive mind based on representationalism cannot explain the Müller-Lyer illusion. Were this challenge valid, the definition of emotion within the theory of predictive mind-constructed emotion would likewise be undermined. However, analysis of both theories reveals that radical enactivism's critique is untenable. By emphasizing the active and dynamic nature of emotions, the theory of predictive mind-constructed emotion not only offers a philosophically promising definition of emotion but also enhances the real-time processing capabilities and accuracy in emotion recognition technology.