Dae Houlihan - Computational Cognitive Neuroscience of Emotion
I'm a Neukom Computational Science Postdoctoral Fellow and Lecturer in the Cognitive Science Program at Dartmouth. My research explores the cognitive reasoning that underpins emotional intelligence.
People's reasoning about emotions can appear sophisticated, flexible and reliable, but can also appear erroneous, biased and inconsistent. These characteristics may seem irreconcilable, but they share a common origin. Both the remarkable abilities and the dramatic limitations of human emotion understanding illuminate the cognitive mechanisms of social intelligence arguably responsible for our species' extraordinary success.
My work aims to reverse-engineer social intelligence by emulating people's reasoning about emotions. My approach emphasizes the use of probabilistic programs, causal methods, and generative models to explain how social cognition works, why it succeeds, and where it fails. By describing social cognition in computational terms, these models support the simultaneous development of formal psychological theory and of machines with human-like emotional intelligence.
I completed my PhD at MIT in the Department of Brain and Cognitive Sciences. At Dartmouth, I work with Luke Chang and Jonathan Phillips. At MIT, I work with Rebecca Saxe, Josh Tenenbaum and John Gabrieli. I'm a member of the Center for Brains, Minds and Machines and a fellow at The Dalai Lama Center for Ethics. Sometimes I post things on bsky/twitter.
|My thesis was featured in the Society for Affective Science Newsletter, along with a thoughtful summary by Dr. Marissa Ogren:|
|Understanding emotions: How do we predict what others are feeling? (SAS)|
|The paper, Emotion prediction as computation over a generative theory of mind, was featured on the front page of MIT News. The article by Anne Trafton is:|
|Computational model mimics humans' ability to predict emotions (MIT News)|