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Steinberg EE, Keiflin R, Boivin JR, Witten IB, Deisseroth K, Janak PH. A causal link between prediction errors, dopamine neurons and learning. Nat Neurosci . 2013 May 26 ; PubMed Abstract

Comments on Paper and Primary News
Comment by:  Phil Corlett
Submitted 8 July 2013 Posted 8 July 2013

Researchers across our field (even those relatively less interested in the brain) are deeply concerned with causality—from those geneticists or epidemiologists assessing the relationships between genes or cannabis exposure and illness onset to those phenomenologists concerned with how patients describe their thoughts and actions as lacking causal agency. For the most part, all of our observations are correlational. Anything more causal, with a few exceptions (Corlett et al., 2009), would entail ethical concerns. Causality is particularly problematic for those of us concerned with the neuronal mechanisms of symptom generation. Are the neural signals we observe with functional neuroimaging of patients with psychotic symptoms, for example, a cause of those symptoms or a consequence of having distressing and distracting experiences in the scanner?

In a 1979 issue of Scientific American, Francis Crick (of DNA fame) wished for a method to gain control over some neurons whilst "leaving the others more or less unaltered" (  Read more

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Comment by:  Anna Ermakova
Submitted 22 July 2013 Posted 22 July 2013

Highly replicated correlational studies, beginning with electrophysiological recordings in primates and rodents and followed up with similar studies using PET and MRI in humans, established a strong correlation between dopamine neuronal firing and learning about rewards. This process appeared driven by the mismatch between expected and actual outcome, called prediction error. Steinberg in their recent article take a crucial next step into the interactions among prediction errors, dopamine, and reinforcement learning: They demonstrate a causal link between phasic dopamine prediction error signaling in the midbrain and learning stimulus-reward associations. In their elegant experiments they used two classical learning paradigms: associative blocking and extinction. They mimicked prediction error signaling by inducing precisely timed dopamine firing with optogenetics to slow down extinction and to drive learning. This is an important first step for moving away from correlational studies to direct manipulation, and I am sure it will be followed by many others to advance...  Read more

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