Dae Houlihan

|Research Associate
Academic Appointments

  • Fellow, Neukom Institute for Computational Science

  • Postdoctoral Fellow in the Program in Cognitive Science

  • Lecturer

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Dae studies the cognitive mechanisms of social intelligence. Human social cognition shows remarkable sophistication and flexibility, as well as dramatic errors and limitations. Dae's research investigates how social cognition works, when it is effective, and what causes it to fail. He uses probabilistic programs to model how people reason about social situations, plan interactions, and interpret each others' emotional expressions. His work aims to reverse-engineer the mechanisms of social cognition in computational terms that are useful both for understanding the human mind and for building machines with human-like emotional intelligence. He is a Fellow of The Neukom Institute for Computational Science.

Personal Website

Contact

Dae.Houlihan@dartmouth.edu
Moore Hall, Room 233
HB 6207

Department(s)

Cognitive Science Program

Education

Ph.D. Massachusetts Institute of Technology

Selected Publications

  • Houlihan, S. D., Kleiman-Weiner, M., Hewitt, L. B., Tenenbaum, J. B., & Saxe, R. (2023). Emotion prediction as computation over a generative theory of mind. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2251), 20220047. https://doi.org/10.1098/rsta.2022.0047.

  • Houlihan, S. D., Ong, D., Cusimano, M., & Saxe, R. (2022). Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind. Proceedings of the Annual Meeting of the Cognitive Science Society, 44, 854–861. https://escholarship.org/uc/item/7sn3w3n2.

  • Houlihan, S. D., Tenenbaum, J. B., & Saxe, R. (2021). Linking Models of Theory of Mind and Measures of Human Brain Activity. In M. Gilead & K. N. Ochsner (Eds.), The Neural Basis of Mentalizing (pp. 209–235). Springer International Publishing. https://doi.org/10.1007/978-3-030-51890-5_11.

  • Saxe, R., & Houlihan, S. D. (2017). Formalizing emotion concepts within a Bayesian model of theory of mind. Current Opinion in Psychology, 17, 15–21. https://doi.org/10.1016/j.copsyc.2017.04.019.

Courses