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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

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Primary Papers: A causal link between prediction errors, dopamine neurons and learning.

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" (Crick, 1979). If we can recreate a pattern of firing that is thought to be necessary and sufficient for a particular cognitive process, we can be more certain that that particular signal is causing that process.

One increasingly influential model of delusion formation—the aberrant prediction error account (Corlett et al., 2007; Gray et al., 1991; Hemsley, 1994)—recently received a preclinical boost in this regard. Using optogenetic techniques (see below), Steinberg, Janak, and colleagues demonstrated that a specific pattern of neural firing, prediction error, at a particular time (coincident with irrelevant cues), was causally related to behavioral learning (about those irrelevant, predictively redundant cues) (Steinberg et al., 2013). This is exactly the pattern of neural signaling and behavior that portends delusions, according to the theory (Corlett et al., 2007; Gray et al., 1991; Hemsley, 1994). Prediction errors and delusions have been associated previously with correlative studies (Corlett et al., 2007; Romaniuk et al., 2010; Schlagenhauf et al., 2009). These new data show that, if one engenders prediction errors artificially, aberrant learning results.

Steinberg, Janak, and their colleagues achieved this feat by first identifying a cell population of interest—dopamine cells in the midbrain previously implicated in the pathophysiology of schizophrenia (Carlsson and Carlsson, 1990). Next, they chose a firing pattern—phasic firing—which has been correlated with Pavlovian and instrumental learning in now classical, albeit correlative, work from Wolfram Schultz (Schultz and Dickinson, 2000; Tobler et al., 2006; Waelti et al., 2001). Finally, they homed in on an informative behavioral paradigm—blocking.

Blocking calls into question Hebb’s adage that in learning, things that fire together wire together (Hebb, 1949). We do, of course, learn from contiguity and statistical regularity, but blocking teaches us that there is more to learning than mere correlation (McLaren and Dickinson, 1990). In a blocking paradigm, we first train a cue (such as a light) as a predictor of a salient outcome (such as a food reward). Next, we add a novel cue (such as a tone) to the first cue. This compound of cues (tone and light together) also predicts the outcome. Prior learning about the first cue blocks learning about the second cue (Kamin, 1969). That is, despite being contiguous with the outcome, an association between the second, blocked cue is not learned. This is because the outcome was already predicted.

Blocking led to the invention of learning theories that focus on prediction error—the mismatch between what we expect (based on what we have already learned) and what we experience (Rescorla and Wagner, 1972). We learn most when prediction errors are largest, and we don’t learn when there is no prediction error, as in the blocking case. Prediction errors have been implicated in the formation of causal beliefs—associations between cause and effect (Dickinson, 2001; Fletcher and Henson, 2001)—in social inferences, such as attributions of worker productivity (Cramer et al., 2002) and in the formation of trusting relationships (Behrens et al., 2008). Furthermore, aberrant prediction errors—signaled independent of cue and context—have been associated with delusion formation in psychotic illnesses (Corlett et al., 2007; Romaniuk et al., 2010; Schlagenhauf et al., 2009) and model psychoses such as amphetamine and ketamine administration in human subjects (Bernacer et al., 2013; Corlett et al., 2006).

However, all of these important observations lack a definitive demonstration of causal association between prediction error, learning, belief, reputation, trust, or delusion. Janak, Steinberg, and their colleagues provide some of the first evidence for a causal association among prediction error, dopamine firing, and learning.

They made an elegant genetic manipulation in rats. First, they targeted dopamine neurons specifically by driving Cre recombinase expression with a tyrosine hydroxylase promoter. Tyrosine hydroxylase is an enzyme that is crucial in the production of dopamine and is present in dopamine cells. Cre recombinase subsequently triggered the expression of an ion channel that is sensitive to light—when this channel is stimulated with light of a particular wavelength, the channel opens and the cells expressing the channel depolarize and fire action potentials. The genetic technique colocalizes the channel and the dopamine. The targeted application of light (hence, optogenetic) ensures specificity to cells that have previously shown prediction error-like signaling. Steinberg et al. applied the light following blocking trials (when there should be no prediction error—confirmed with neural recordings in monkeys and with fMRI in humans; see Tobler et al., 2006; Waelti et al., 2001). The causal proof comes if the rats learned about the blocked cue. They did. They formed a predictive association between the blocked cue and the reward.

Intriguingly, this is exactly what we think happens in patients with psychotic illness. Their prediction error signaling neurons are inappropriately activated, driving them to attend to and learn about things they ought not to, things that non-psychotic people would ignore. This is the kernel for delusion formation. Paul Fletcher and I recently showed that in non-psychotic individuals with odd beliefs (such as telekinesis or alien abduction), there are prediction error brain signals during blocking, and the magnitude of these signals correlates with weaker blocking and with the severity of odd beliefs (Corlett and Fletcher, 2012).

Whilst it is heartening to have our theories supported by preclinical data, what might be the clinical application of this new result? We cannot make optogenetic manipulations in human subjects. However, we can make manipulations of cortical neural activity using transcranial magnetic stimulation. Such studies are underway in my lab at Yale. We are using stimulation protocols that engender neural inhibition and long-term depression (Hoffman and Cavus, 2002) in an attempt to cancel aberrant prediction errors in psychotic patients. The data of Steinberg et al. suggest that this approach might ultimately help us to control prediction error signaling and curtail psychotic symptoms.


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Primary Papers: A causal link between prediction errors, dopamine neurons and learning.

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 understanding of the precise neural mechanisms of reinforcement learning in health and disease.

View all comments by Anna Ermakova