Research in our lab focuses on the neural and computational processes underlying reinforcement learning and decision-making. We study the ongoing day-to-day processes by which we learn from trial and error, without explicit instructions, to predict future events and to act upon the environment so as to maximize reward and minimize punishment.
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LAB NEWS

May 2013: Congratulations to Yuan Chang for being awarded one of two Brinster '43 Neuroscience Prizes recognizing outstanding senior thesis work!

May 2013: E Eldar, JD Cohen and Y Niv - The Effects of Neural Gain on Attention and Learning, accepted to Nature Neuroscience

Mar 2013: CG Diuk, K Tsai, J Wallis, MM Botvinick and Y Niv - Hierarchical Learning Induces Two Simultaneous, But Separable Prediction Errors in Human Basal Ganglia, accepted to the Journal of Neuroscience

Dec 2012: Congratulations to Reka Daniel-Weiner for being awarded a competitive WSE postdoctoral fellowship!

Sept 2012: NIMH R01 grant awarded to study neural and computational mechanisms of selective attention in experience-based decision making

Sept 2012: NSF CRCNS grant awarded to study the neural correlates of hierarchical reinforcement learning together with Matt Botvinick and Andy Barto