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Comparing model predictions of response bias and variance in cue combination
Rama Natarajan · Iain Murray · Ladan Shams · Richard Zemel

Wed Dec 10 07:30 PM -- 12:00 AM (PST) @ None #None

We explore a recently proposed mixture model approach to understanding interactions between conflicting sensory cues. Alternative model formulations, differing in their sensory noise models and inference methods, are compared based on their fit to experimental data. Heavy-tailed sensory likelihoods yield a better description of the interesting response behavior than standard Gaussian noise models. We study the underlying case for this result, and then present several testable predictions of these models.

Author Information

Rama Natarajan (University of Toronto)
Iain Murray (University of Edinburgh)

Iain Murray is a SICSA Lecturer in Machine Learning at the University of Edinburgh. Iain was introduced to machine learning by David MacKay and Zoubin Ghahramani, both previous NIPS tutorial speakers. He obtained his PhD in 2007 from the Gatsby Computational Neuroscience Unit at UCL. His thesis on Monte Carlo methods received an honourable mention for the ISBA Savage Award. He was a commonwealth fellow in Machine Learning at the University of Toronto, before moving to Edinburgh in 2010. Iain's research interests include building flexible probabilistic models of data, and probabilistic inference from indirect and uncertain observations. Iain is passionate about teaching. He has lectured at several Summer schools, is listed in the top 15 authors on videolectures.net, and was awarded the EUSA Van Heyningen Award for Teaching in Science and Engineering in 2015.

Ladan Shams (UCLA, Caltech)
Richard Zemel (Vector Institute/University of Toronto)

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