@proceedings {5155,
	title = {Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind},
	volume = {44},
	year = {2022},
	month = {07/2022},
	pages = {854-861},
	address = {Toronto, CA},
	abstract = {It is commonly believed that expressions visually signal rich diagnostic information to human observers. We studied how observers interpret the dynamic expressions that people spontaneously produced during a real-life high-stakes televised game. We find that human observers are remarkably poor at recovering what events elicited others{\textquoteright} facial and bodily expressions. Beyond simple inaccuracy, people{\textquoteright}s causal reasoning exhibits systematic model-based patterns of errors. We show that latent emotion representations can explain people{\textquoteright}s reasoning about the unseen causes of expressions. A hierarchical Bayesian model simulates which events people infer to be the cause of others{\textquoteright} expressions by comparing the emotions inferred from the expressions against the emotions people were predicted to experience in various situations. This causal model provides a close, parameter-free fit to human causal judgments, suggesting that humans interpret expressions in the context of emotion predictions generated by a causally-structured mental model of other minds.},
	keywords = {Affective Cognition, Bayesian Theory of Mind, Causal Reasoning, Emotion Recognition, Emotion Understanding, intuitive theory},
	url = {https://escholarship.org/uc/item/7sn3w3n2},
	author = {Sean Dae Houlihan and Desmond Ong and Maddie Cusimano and Rebecca Saxe}
}
@article {3513,
	title = {Learning physical parameters from dynamic scenes.},
	journal = {Cognitive Psychology},
	volume = {104},
	year = {2018},
	month = {8/2018},
	pages = {57-82},
	abstract = {<p>Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum et al., 2011), we work with more ex- pressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultane- ously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.</p>
},
	keywords = {intuitive physics, intuitive theory, learning, physical reasoning, probabilistic inference},
	doi = {10.1016/j.cogpsych.2017.05.006},
	url = {https://www-sciencedirect-com.libproxy.mit.edu/science/article/pii/S0010028517301822},
	author = {Ullman, Tomer D. and Stuhlm{\"u}ller, Andreas and Noah D. Goodman and Joshua B. Tenenbaum}
}
