- Thursday, January 23rd 2020 at 16:00 - 17:00 UK (Other timezones)
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Modern neuropsychiatry research increasingly relies on computational modeling to deconstruct mechanisms of brain and behavior. The success of these efforts heavily depends on statistical methods for estimating parameters of these models and comparing candidate computational models. In this work, I present a novel method for concurrent model comparison and parameter estimation, called hierarchical Bayesian inference (HBI). Unlike previous methods, HBI is built based on a novel approach that estimating individual variation in parameters (parameter estimation) and inference about individual variation in the expressed model (model comparison) are interdependent problems. I will show that this approach has important advantages for both parameter estimation and model comparison theoretically and experimentally. First, the parameters estimated by the HBI show smaller errors compared to other methods. Second, this framework solves two enduring issues of model comparison: i) oversensitivity to outliers; ii) bias towards overly simplistic models (e.g. those with fewer parameters). This method has implications for computational modeling research in group studies across many areas of psychology, neuroscience, and psychiatry.
Payam Piray
Postdoctoral research associate
Princeton Neuroscience Institute
Princeton University