Probabilistic Brains: The Neural Computation of Uncertainty

Friday, November 3, 2017 -
2:00pm to 3:00pm
The FUNG Auditorium
Megan Peters

Assistant Professor of Bioengineering

University of California, Riverside

 

Probabilistic Brains: The Neural Computation of Uncertainty

Abstract: 
How is uncertain information represented in the brain, and what can we learn from how the brain computes and represents uncertainty?  My research aims to characterize the neural computations that underlie the ability to evaluate uncertainty in the environment and in the decisions that we make.  I use a combination of neuroimaging, brain stimulation, Bayesian computational modeling, electrophysiology, and behavioral approaches to reveal how our brains are able to compute the reliability of incoming sensory signals and the decisions we make about them, and represent and use the result of these computations in driving adaptive behavior.  Through examining not only human neuroscience and behavior but also that of model organisms such as non-human primates and rodents, I aim to reveal the universal substrates of these abilities across species, as well as those aspects of uncertainty monitoring that may be uniquely human.
Bio: 

I am an Assistant Professor in the Department of Bioengineering at the University of California, Riverside, and a Visiting Researcher at Advanced Telecommunications Research Institute International in Kyoto, Japan.  I received my Ph.D. in Computational Cognitive Neuroscience (Psychology) from UCLA, following my B.A. in Cognitive Science from Brown University.  During my Ph.D. I studied visuohaptic perception and sensorimotor integration, and during my postdoctoral work at UCLA I pivoted to study neural representations of uncertainty within perceptual decision-making.  My research combines neuroscience, bioengineering, and psychology to understand how the brain performs probabilistic computations that drive perception, learning, and awareness.