@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 {4500,
	title = {A perceptually inspired generative model of rigid-body contact sounds},
	journal = {Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19)},
	year = {2019},
	month = {09/2019},
	abstract = {<p>Contact between rigid-body objects produces a diversity of impact and friction sounds. These sounds can be synthesized with detailed simulations of the motion, vibration and sound radiation of the objects, but such synthesis is computationally expensive and prohibitively slow for many applications. Moreover, detailed physical simulations may not be necessary for perceptually compelling synthesis; humans infer ecologically relevant causes of sound, such as material categories, but not with arbitrary precision. We present a generative model of impact sounds which summarizes the effect of physical variables on acoustic features via statistical distributions fit to empirical measurements of object acoustics. Perceptual experiments show that sampling from these distributions allows efficient synthesis of realistic impact and scraping sounds that convey material, mass, and motion.</p>
},
	author = {James Traer and Maddie Cusimano and Josh H. McDermott}
}
@conference {4526,
	title = {Scrape, rub, and roll: causal inference in the perception of sustained contact sounds },
	booktitle = {Cognitive Science},
	year = {2019},
	month = {07/2019},
	address = {Montreal, Qu{\'e}bec, Canada},
	author = {Maddie Cusimano and James Traer and Josh H. McDermott}
}
