Experimental Games and Social Decision Making

Origins of the concepts cause, cost, and goal in prereaching infants

Proceedings of the National Academy of Sciences, 2019. [All Versions]

Leveraging Facial Expressions and Contextual Information to Investigate Opaque Representations of Emotion

Emotion, 2019. [All Versions]

Theory of mind as inverse reinforcement learning

Current Opinion in Behavioral Sciences, 2019. [All Versions]. This paper reviews the idea that Theory of Mind --- humans' ability to reason about other people's mental states --- can be formalized as inverse reinforcement learning. Under this framework, expectations about how mental states produce behavior are captured in a reinforcement learning (RL) model. Predicting other people’s actions is achieved by simulating a RL model with the hypothesized beliefs and desires, while mental-state inference is achieved by inverting this model. Although many advances in inverse reinforcement learning (IRL) did not have human Theory of Mind in mind, this paper focuses on what they reveal when conceptualized as cognitive theories

Planning with theory of mind

Trends in Cognitive Sciences, 2022. [All Versions]. [Preprint]. A perspective on understanding Theory of Mind through planning that consists of abstract structured causal representations and supports efficient search and selection from innumerable possible actions. Planning requires that Theory of Mind consists of abstract structured causal representations and supports efficient search and selection from innumerable possible actions. Theory of Mind contrasts with less cognitively demanding alternatives: statistical predictive models of other people’s actions, or model-free reinforcement of actions by their effects on other people. Theory of Mind is likely used to plan novel interventions and predict their effects, for example, in pedagogy, emotion regulation, and impression management