情绪预测作为一种计算,而不是一种产生性思维理论
Sean Dae Houlihan1, Max Kleiman-Weiner1,2, Luke B Hewitt1
1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
研究人员开发了一个计算模型,通过推断信念和社会偏好来预测情绪. 该模型准确匹配了人类对20种情绪的预测,强调了社会因素在情绪感知中的重要性.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 社会心理学 社会心理学
背景情况:
- 人类观察者可以从有限的事件信息中预测情绪.
- 了解情绪预测对于人工智能和社会互动建模至关重要.
研究的目的:
- 提出一种正式的计算模型,用于预测社会困境中的情绪.
- 研究推断信念和社会偏好在情绪预测中的作用.
主要方法:
- 利用逆规划推断信念和偏好 (股权,声誉).
- 通过将事件与期望和偏好进行比较,进行计算机"评估".
- 学习函数将评估映射到20个情感标签上.
主要成果:
- 该模型准确地预测了人类观察者的定量情绪预测.
- 推断的社会偏好,而不仅仅是货币偏好,对于几乎所有情绪预测都至关重要.
- 模型和人类都使用了最小的个性化信息来调整预测.
结论:
- 该框架整合了反向规划,评估和情感概念.
- 它提供了一种计算方法来理解情感的直观理论.
- 社会偏好在预测情绪反应方面发挥着重要作用.
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