Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap

摘要

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 cognitionhow humans reason about emotionsand 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 inferencessuch as hypothetical and counterfactual reasoning about emotionsthat 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.