Wolfram Schultz

Rewards - dopamine reward prediction error graph

Prof Wolfram Schultz FRS

Professor of Neuroscience, Department of Physiology, Development & Neuroscience, University of Cambridge, UK

Fellow, Churchill College, University of Cambridge

ws42@pm.me, ws234@cam.ac.uk

Neuroeconomics of reward and decision-making

Our group is interested in identifying brain signals for reward and economic decisions. As information processing systems work with explicit signals, we like to identify and characterise such signals before investigating detailed neuronal mechanisms. We use concepts from animal learning theory and economic decision theory and combine behavioural, neurophysiological and neuroimaging (fMRI) methods. We search for neuronal responses that implement fundamental theoretical constructs underlying reward-seeking, learning and decision-making, such as reward prediction error, utility, probability, risk, object-action-chosen value, and revealed preference. Studied brain structures include dopamine neurons, striatum, frontal cortex and amygdala. Please find more information in a short general article or update on dopamine reward prediction error coding, and a brief overview or longer review on reward and economic decisions. Please find also my short CV, full CV, publication list and my 2022 autobiography written for the Society for Neuroscience (SfN).

I am sorry, I do not accept PhD students or postdocs.

Lecture videos

Nature & Nurture podcast 2024

Neuroinformatics Krakow Univ 2022

AV Hill Lecture Cambridge Univ 2021

Virtual Dopamine (ViDA) Princeton 2020

Chen Lecture Caltech 2017

Einstein Center Berlin 2016

Puerto Rico Univ 2014

Collaborations and Visitorships

Ralph Adolphs, Antonio Rangel, Charles R. Plott (Caltech)

Peter Bossaerts (Economy Univ Cambridge)

Fabian Grabenhorst (Univ Oxford)

Florian Mormann (Univ Bonn)

Ueli RĂ¼tishauser (Cedars-Sinai Los Angeles)

Masamichi Sakagami (Tamagawa Univ)

Masahiko Takada (Kyoto Univ Primate Ctr at Inuyama)

Ken-ichiro Tsutsui (Tohoku Univ Sendai)