Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap. (31st July 2018)

• Record Type: Journal Article
• Authors: Ong, Desmond C.; Zaki, Jamil; Goodman, Noah D.
• Is Part Of: Topics in cognitive science. Volume 11:Number 2(2019)
• Page Start: 338
• Page End: 357
• Publication Date: 2018-07-31
• Subjects: Emotion -- Affective cognition -- Inference -- Theory of mind; Cognitive science -- Periodicals; Cognitive Science -- Periodicals; 153.05
• DOI: 10.1111/tops.12371
• Languages: English
• ISSNs: 1756-8757

Abstract

Abstract: Research on social cognition has fruitfully applied computational modeling approaches to explain how observers understand and reason about others' mental states. By contrast, there has been less work on modeling observers' understanding of emotional states. We propose an intuitive theory framework to studying affective cognition —how humans reason about emotions—and derive a taxonomy of inferences within affective cognition. Using this taxonomy, we review formal computational modeling work on such inferences, including causal reasoning about how others react to events, reasoning about unseen causes of emotions, reasoning with multiple cues, as well as reasoning from emotions to other mental states. In addition, we provide a roadmap for future research by charting out inferences—such as hypothetical and counterfactual reasoning about emotions—that are ripe for future computational modeling work. This framework proposes unifying these various types of reasoning as Bayesian inference within a common "intuitive Theory of Emotion." Finally, we end with a discussion of important theoretical and methodological challenges that lie ahead in modeling affective cognition. Abstract : An important, but relatively neglected, aspect of human theory of mind is emotion inference: understanding how and why a person feels a certain why is central to reasoning about their beliefs, desires and plans. The authors review recent work that has begun to unveil the structure and determinants of emotion inference, organizing them within a unified probabilistic framework.