[Intelligence Seminar] March 16: Yael Niv, GHC 4303, 3:30, "Better Safe Than Sorry? Neural Prediction Errors Reveal a Risk-Sensitive Reinforcement Learning Process"

[Noah A Smith]

Addendum:  please contact Sharon Cavlovich (sharonw at cs.cmu.edu) for
meetings with Yael.


On Tue, Mar 9, 2010 at 7:12 AM, Noah A Smith <nasmith at cs.cmu.edu> wrote:
> Intelligence Seminar
>
> March 16, 2010
> 3:30 pm
> GHC 4303
>
> Title:  Better Safe Than Sorry? Neural Prediction Errors Reveal a
> Risk-Sensitive Reinforcement Learning Process
> Yael Niv, Princeton University
>
> Which of these would you prefer: getting $10 with certainty or tossing
> a coin for a 50% chance to win $20? Whatever your answer, you probably
> were not indifferent between these two options. In general, human
> choice behavior is influenced not only by the expected reward value of
> options, but also by their variance, with people differing in the
> degree to which they are risk-averse or risk-seeking. Economic,
> psychological and neural aspects of this are well studied when
> information about risk is provided explicitly. However, we must
> normally learn about outcomes from experience, through trial and
> error. Traditional reinforcement learning (RL) models of action
> selection, however, rely on temporal difference methods that learn the
> mean value of an option, ignoring risk.  We used fMRI to test this
> assumption by examining the neural correlates of reinforcement
> learning and asking whether they are indeed indifferent to risk. Our
> results show that reinforcement learning is modulated by experienced
> risk, and reveal a close coupling between the fluctuating,
> experience-based, evaluations of risky options measured neurally, and
> fluctuations in behavioral choice. This suggests that risk sensitivity
> is integral to human learning, illuminating economic models of choice
> and neuroscientific models of learning.
>
> Joint work with: Jeffrey A. Edlund, Peter Dayan, John P. O'Doherty
>
> Bio:
>
> Yael is an assistant professor at the Princeton Neuroscience Institute
> (PNI) and the Psychology Department at Princeton University since
> September 2008. She was also a postdoc at Princeton, and earned her
> PhD at The Hebrew University of Jerusalem (Israel) while conducting
> most of her research at the Gatsby Computational Neuroscience Unit
> (UCL, London). Her research focuses on normative computational models
> of learning and decision making, and in understanding the neural basis
> for simple day to day trial and error learning.
>