This center leverages the strengths of Carnegie Mellon in cognitive and computational neuroscience and those of the University of Pittsburgh in basic and clinical neuroscience to support a coordinated cross-university research and educational program of international stature.


Simon Initiative Distinguished Lecture: Chi
Nov 12 @ 4:30 PM – 6:30 PM

Michelene (Micki) T.H. Chi
Foundation Professor and Dorothy Bray Endowed Professor of Science and Teaching, Mary Lou Fulton Teachers College; Director of the Learning and Cognition Lab, Arizona State University

Tuesday, November 12
4:30 — 6:30 p.m.
Simmons Auditorium A, Tepper Buidling
A reception will immediately follow the lecture.
ICAP: How to Promote Deeper Learning by Engaging Students Cognitively

CNBC Colloquium – Jennifer Raymond
Nov 14 @ 4:00 PM – 5:00 PM
CNBC Colloquium – Matthew Botvinick @ MI 328
Nov 21 @ 4:00 PM – 5:00 PM

“A distributional code for value in dopamine-based reinforcement learning”
Matthew Botvinick, MD, PhD
Director of Neuroscience, DeepMind

Thursday, November 21, 2019
328 Mellon Institute
Twenty years ago, a link was discovered between the neurotransmitter dopamine and the computational framework of reinforcement learning. Since then, it has become well established that dopamine release reflects a reward prediction error, a surprise signal that drives learning of reward predictions and shapes future behavior. According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. I’ll present recent work in which we have proposed a novel account of dopamine-based reinforcement learning, and adduced experimental results which point to a significant modification of the standard reward prediction error theory. Inspired by recent artificial intelligence research on distributional reinforcement learning, we hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea leads immediately to a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.

CNBC Connect

CNBC Connect is our annual newsletter with award, research, and more news. View editions.

Research Roundup

Research Roundup lists recent publications by CNBC members. View publications.