Institute of Neuroscience
National Yang-Ming University
Probability estimation and its neurocomputational substrates
Many decisions we make depend on how we evaluate potential outcomes and estimate their probabilities of occurrence. Outcome valuation is subjective – it requires consulting the decision maker’s internal preferences and is sensitive to context. Probability estimation is also subjective – but requires the decision maker to first extract statistics from the environment before using them to estimate probability. Currently, it is unclear whether the two computations share similar algorithms and neural-algorithmic implementations.
I will present our recent work on context-dependent probability estimation, which we identified both similarities and differences in computational mechanisms between valuation and probability estimation. I will also talk about work on modeling probability estimation as Bayesian inference, which focuses on examining how and how well people estimate probability of reward in the presence of prior and likelihood information. Here we found suboptimal performance similar to base-rate neglect, which surprisingly is robust across a wide variety of setups that try to eliminate this behavior. Together, these results suggest many interesting aspects of probability estimation that have yet to be fully understood at the behavioral, computational, and neural algorithmic levels.
Bio: I obtained my PhD (2008) from New York University working on representations and use of probability information in decision making under risk with Larry Maloney. As a postdoc (Caltech, 2008-2010), with Antonio Rangel, we investigated neural mechanisms for sequential information integration and context-dependent valuation. I am currently an Associate Professor in the Institute of Neuroscience at National Yang-Ming University, Taipei, Taiwan where my lab studies the neural and computational mechanisms of decision making.
A pizza lunch will be served.