We learn to choose better options and avoid worse ones through trial and error, but exactly how this happens is still unclear. One idea is that we learn 'values' for options: whenever we choose an option and get more reward than originally expected (for example, if an unappetizing-looking food turns out to be very tasty), the value of that option increases. Likewise, if we get less reward than expected, the chosen option’s value decreases.

This learning process is hypothesized to work via the strengthening and weakening of connections between neurons in two parts of the brain: the cortex and the striatum. In this model, the activity of the neurons in the cortex represents the options, and the value of these options is represented by the activity of neurons in the striatum. Strengthening the connections is thought to increase the value of the stimulus, but this theory has been difficult to test.

In humans, a single genetic mutation causes a movement disorder called DYT1 dystonia, in which muscles contract involuntarily. In rodents, the same mutation causes the connections between the neurons in the cortex and the striatum to become too strong. If the theory about value learning is true, this strengthening should affect the decisions of patients that have DYT1 dystonia.

Arkadir et al. got healthy people and people with DYT1 dystonia to play a game where they had to choose between a 'sure' option and a 'risky' option. Picking the sure option guaranteed the player would receive a small amount of money, whereas the risky option gave either double this amount or nothing. The theory predicts that the double rewards should cause the patients to learn abnormally high values, which would lure them into making risky choices. Indeed, Arkadir et al. found that players with DYT1 dystonia were more likely to choose the risky option, with the people who had more severe symptoms of dystonia having a greater tendency towards taking risks.

Arkadir et al. showed that these results correspond with a model that suggests that people with DYT1 dystonia learn excessively from unexpected wins but show weakened learning after losses, causing them to over-estimate the value of risky choices. This imbalance mirrors the previous results that showed an inappropriate strengthening of the connections between neurons in rodents, and so suggests that similar changes occur in the brains of humans. Thus it appears that the changes in the strength of the connections between neurons translate into changes in behavior.

This pattern of results might also mean that the movement problems seen in people with DYT1 dystonia may be because they over-learn movements that previously led to a desired outcome and cannot sufficiently suppress movements that are no longer useful. Testing this idea will require further experiments.

DYT1 dystonia is a rare, dominantly inherited form of dystonia, caused almost exclusively by a specific deletion of three base pairs in the TOR1A gene (Ozelius et al., 1997). Clinically, DYT1 dystonia is characterized by variable severity of sustained or intermittent muscle contractions that produce abnormal movements. DYT1 dystonia patients have normal intelligence, and post-mortem examination of their brains does not reveal obvious abnormalities or evidence of neurodegeneration (Paudel et al., 2012). Nevertheless, research in two different rodent models of DYT1 dystonia points to the existence of a fundamental deficit in synaptic plasticity. Specifically, brain slices of transgenic rodents expressing the human mutant TOR1A gene show abnormally strong long-term potentiation (LTP; Martella et al., 2009) and weak, or even absent, long-term depression (LTD; Grundmann et al., 2012; Martella et al., 2009) in corticostriatal synapses, as compared to wild-type controls.

Reinforcement learning theory (Sutton and Barto, 1998) hypothesizes that dopamine-dependent synaptic plasticity in corticostriatal networks is the neuronal substrate for learning through trial and error (Barnes et al., 2005; Barto, 1995; Schultz et al., 1997). The core assumptions of this theory are that (1) dopamine release in the striatum signals errors in the prediction of reward, with dopamine levels increasing following successful actions (to signal a positive prediction error) and decreasing when actions fail to achieve the expected outcome (a negative prediction error), (2) fluctuations in dopamine modulate downstream plasticity in recently active corticostriatal synapses such that synapses responsible for positive prediction errors are strengthened through long-term potentiation (LTP), and those that led to disappointment are weakened through long-term depression (LTD) (Reynolds et al., 2001), and (3) the efficacy of corticostriatal transmission affects voluntary action selection.

Dopamine’s role as a reinforcing signal for trial-and-error learning is supported by numerous findings (Pessiglione et al., 2006; Schultz et al., 1997; Steinberg et al., 2013), including in humans, where Parkinson’s disease serves as a human model for altered dopaminergic transmission (Frank et al., 2004). However, the contribution of (dopamine modulated) corticostriatal plasticity to shaping action has remained unconfirmed in the behaving organism, as it is not clear that the behavioral effects of altered dopamine signaling in Parkinson’s disease (and other conditions in which dopamine transmission is compromised) indeed stem from the role of dopamine in modulating plasticity. Towards this end, here we test whether DYT1 dystonia, where corticostriatal plasticity is suggested to be altered despite preserved dopaminergic signaling, leads to the behavioral effects predicted by reinforcement learning with imbalanced plasticity. In particular, our predictions stem from considering the effects of intact prediction errors on an altered plasticity mechanism that amplifies the effect of positive prediction errors (i.e., responds to positive prediction errors with more LTP than would otherwise occur in controls) and mutes the effects of negative prediction errors (that is, responds with weakened LTD as compared to controls).

We compared the behavior of DYT1 dystonia patients and healthy controls on an operant-learning paradigm with probabilistic rewards (Niv et al., 2012). Participants learned from trial and error to associate four different visual cues with monetary rewards (Figure 1a), optimizing their gain by selecting one of two cues in choice trials, and choosing the single available cue in forced trials. Three visual cues were associated with a payoff of 0¢, 5¢ and 10¢, respectively, while the fourth cue was associated with an unpredictable payoff of either 0¢ or 10¢ with equal probabilities (henceforth the ‘risky 0/10¢’ cue). Based on the findings in rodents with the DYT1 mutation, we predicted that dystonia patients would learn preferentially from positive prediction errors (putatively due to abnormally strong LTP) and to a much lesser extent from negative prediction errors (due to weak LTD) (Figure 1b). As a result, they should show a stronger tendency to choose the risky cue as compared to healthy controls.

Figure 1

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We tested 13 patients with DYT1 dystonia (8 women, 5 men, age 20–47, mean 28.6 years, henceforth DYT) and 13 healthy controls (CTL; 8 women, 5 men, age 19–46, mean 28.8 years), matched on an individual basis for sex and age (Mann-Whitney U test for age differences, z = −0.59, df = 24, P = 0.55), all with at least 13 years of education. Patients had no previous neurosurgical interventions for dystonia (including deep brain stimulation) and were tested before their scheduled dose of medication when possible (see Materials and methods). The number of aborted trials was similarly low in both groups (DYT 2.3 ± 2.5, CTL 1.1 ± 1.2, Mann-Whitney z = −1.61, df = 24, P = 0.11) and reactions times were well below the 1.5s response deadline (DYT 0.78s ± 0.11, CTL 0.71s ± 0.10, Mann-Whitney z = −1.49, df = 24, P = 0.14), confirming that motor symptoms of dystonia did not interfere with the minimal motor demands of the task.

Both groups quickly learned the task, and showed similarly high probabilities of choosing the best cue in trials in which a pair of sure cues (sure 0¢ vs. sure 5¢ or sure ¢5 vs. sure 10¢) appeared together (mean probability correct choice: DYT1 0.92 ± 0.08, CTL 0.93 ± 0.05, Mann-Whitney z = 0.08, df = 24, P = 0.94; Figure 2a), as well as in trials in which the risky cue appeared together with either the sure 0¢ or sure 10¢ cues (mean probability correct: DYT1 0.84 ± 0.09, CTL 0.89 ± 0.04, Mann-Whitney z = −1.39, df = 24, P = 0.17; Figure 2b).

Figure 2

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On trials in which the risky 0/10¢ cue appeared together with the equal-mean 5¢ sure cue, control participants showed risk-averse behavior, as is typically observed in such tasks (Kahneman and Tversky, 1979; Niv et al., 2012). In contrast, patients with DYT1 dystonia displayed significantly less risk aversion, choosing the risky stimulus more often than controls throughout the experiment (Figure 3a, Mann Whitney one-sided test for each block separately, all z > 1.68, df = 24, P < 0.05; Friedman’s test for effect of group after correcting for the effect of time χ² = 16.2, df = 1, P < 0.0001). Overall, the probability of choosing the risky cue was significantly higher among patients with dystonia than among healthy controls (Figure 3b, probability of choosing the risky cue over the sure cue DYT 0.44 ± 0.18, CTL 0.25 ± 0.20, Mann-Whitney z = 2.33, df = 24, P < 0.05).

Figure 3 with 4 supplements see all

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To rule out the possibility that DYT1 patients were simply making choices randomly, causing their behavior to seem indifferent to risk, we divided all 0/10¢ versus 5¢ choice trials according to the outcome of the previous trial in which the risky 0/10¢ cue was chosen. As shown in Figure 3c (see also Figure 3—figure supplement 1), both groups chose the risky 0/10¢ cue significantly more often after a 10¢ ‘win’ than after a 0¢ ‘loss’ outcome (DYT P < 0.005, CTL P < 0.05, Wilcoxon signed-rank test), attesting to intact reinforcement learning in the DYT group (see Figure 3—figure supplement 2, for a reinforcement learning simulation of the same result). If anything, DYT1 dystonia patients showed a greater difference between trials following a win and those following a loss. We next tested for a correlation between risk-taking behavior and the clinical severity of dystonia, as rated on the day of the experiment (see Materials and methods). The results showed that patients with more severe dystonia were more risk taking in our task (Figure 3d, Pearson’s r = 0.62, df = 11, P < 0.05). Risky behavior was not significantly affected by sex (Figure 3—figure supplement 3) or the patient's regime of regular medication (Figure 3—figure supplement 4), and the relationship between risk taking and symptom severity held even when controlling for these factors (p < 0.05 for symptom severity when regressing risk taking on symptom severity, age and either of the two medications; including both medications in the model lost significance for symptom severity, likely due to the large number of degrees of freedom for such a small sample size; age and medication did not achieve significance in any of the regressions).

To test whether increased risk-taking in DYT1 dystonia could be explained by asymmetry in the effects of positive and negative prediction errors on corticostriatal plasticity, we modeled participants’ choice data using an asymmetric reinforcement-learning model (see methods) where the learning rate ($\eta$) is modulated by $(1+\kappa$) when learning from positive prediction errors and by $(1-\kappa$) when the prediction error is negative (also called a 'risk-sensitive' reinforcement learning model; Mihatsch and Neuneier, 2002; Niv et al., 2012). Our model also included an inverse-temperature parameter ($\beta$) controlling the randomness of choices. This approach exploits fluctuations in each individual’s propensity for risk taking (see Figure 3a) as they update their policy based on the outcomes they experience, to recover the learning rate $\eta$ and learning asymmetry $\kappa$ that provide the best fit to each participant’s observed behavior.

First, we tested whether the asymmetric-learning model is justified, that is, whether it explains participants’ data significantly better than the classical reinforcement-learning model with only learning-rate and inverse-temperature parameters. The results showed that the more complex model was justified for the majority of participants (16 out of 26 participants; DYT 6, CTL 19), and in particular, for participants who were risk seeking or risk taking (but not risk-neutral; Figure 4a).

Figure 4

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We then compared the individually fit parameters of the asymmetric model across the two groups. We found significant differences between the groups in the learning asymmetry parameter (DYT −0.05 ± 0.27, CTL −0.34 ± 0.27, Mann-Whitney z = −2.51, df = 24, P < 0.05), but no differences in the other two parameters (learning rate DYT1 0.25 ± 0.19, CTL 0.14 ± 0.11, Mann-Whitney z = 1.33, df = 24, P = 0.18; inverse temperature DYT 0.68 ± 0.37, CTL 0.93 ± 0.47, Mann-Whitney z = −1.18, df = 24, P = 0.23). Thus patients’ behavior was consistent with enhanced learning from positive prediction errors and reduced learning from negative prediction errors as compared to healthy controls, despite the overall rate of learning and the degree of noise in choices (modeled by the inverse temperature parameter) being similar across groups. A significant correlation was also observed between the learning asymmetry parameter and the severity of dystonia (Pearson’s r = 0.64, df = 11, P < 0.05).

One alternative explanation for our results is that the nonlinearity of subjective utility functions (Kahneman and Tversky, 1979) for small amounts of money is different between DYT1 dystonia patients and controls. However, replicating previous results from a healthy cohort (Niv et al., 2012), formal model comparison suggested that choice behavior in our task is significantly better explained by the asymmetric-learning model above (Figure 4b). Moreover, the impetus for our experiment was an a priori hypothesis regarding risk sensitivity as a consequence of asymmetric learning, based on findings from the mouse model of DYT1 dystonia, which has no straightforward equivalent interpretation in terms of nonlinear utilities. We note also that strongly nonlinear utilities in the domain of small payoffs such as those we used here are generally unlikely (Rabin and Thaler, 2001), again suggesting that risk sensitivity is more likely to arise in our experiment from asymmetric learning. Another alternative explanation for behavior in our task, is a win-stay lose-shift strategy that is perhaps utilized to different extent by DYT1 patients and controls. However, this model, equivalent to the classical reinforcement-learning model with a learning rate of 1 and only an inverse temperature parameter, fit 25 out of 26 participants’ data considerably worse than the asymmetric learning model, and therefore was not investigated further.

We demonstrated that DYT1 dystonia patients and healthy controls have different profiles of risk sensitivity in a trial-and-error learning task. Our results support the dominant model of reinforcement learning in the basal ganglia, according to which prediction-error modulated LTP and LTD in corticostriatal synapses are responsible for changing the propensity to repeat actions that previously led to positive or negative prediction errors, respectively. Similar to Parkinson’s disease, at first considered a motor disorder but now recognized to also cause cognitive and learning abnormalities, it appears that DYT1 dystonia is not limited to motor symptoms (Fiorio et al., 2007; Heiman et al., 2004; Molloy et al., 2003; Stamelou et al., 2012), and specifically, that the suspected altered balance between LTP and LTD in this disorder has overt, readily measurable effects on behavior.

DYT1 dystonia and Parkinson's disease can be viewed as complementary models for understanding the mechanisms of reinforcement learning in the human brain. In unmedicated Parkinson’s disease patients, learning from positive prediction errors is impaired due to reduced levels of striatal dopamine that presumably signal the prediction errors themselves, whereas learning from negative prediction errors is intact (Frank et al., 2004; Rutledge et al., 2009). This impairment, and the resulting asymmetry that favors learning from negative prediction errors, can be alleviated using dopaminergic medication (Frank et al., 2004; Shohamy et al., 2004). DYT1 dystonia patients, on the other hand, seem to have intact striatal dopamine signaling (Balcioglu et al., 2007; Dang et al., 2006; Grundmann et al., 2007; Zhao et al., 2008), but altered corticostriatal LTP/LTD that favors learning from positive prediction errors.

Our a priori predictions were based on a simplified model of the role of corticostriatal LTP and LTD in reinforcement learning, and the entire picture is undoubtedly more complex. Controversies regarding the functional relationship between the direct and indirect pathways of the basal ganglia (Calabresi et al., 2014; Cui et al., 2013; Kravitz et al., 2012) and the large number of players taking part in shaping synaptic plasticity (Calabresi et al., 2014; Shen et al., 2008) make it hard to pin down the precise mechanism behind reinforcement learning. Indeed, the DYT1 mouse model has also been linked to impaired plasticity in the indirect pathway due to D2 receptor dysfunction (Beeler et al., 2012; Napolitano et al., 2010; Wiecki et al., 2009), which can lead to abnormal reinforcement (Kravitz et al., 2012).

In any case, our finding are compatible with the prominent 'Go'/'NoGo' model of learning and action selection in the basal ganglia (Frank et al., 2004) that incorporates opposing directions of plasticity in the direct and indirect pathways (Collins and Frank, 2014). In particular, current evidence suggests that corticostriatal LTP following positive prediction errors and LTD following negative prediction errors occur in D1 striatal neurons (direct pathway), whereas plasticity in D2-expressing neurons (indirect pathway) is in the opposite direction (Kravitz et al., 2012; Shen et al., 2008). As the direct pathway supports choice (‘Go’) while the indirect pathway supports avoidance (‘NoGo’), under this implementation of reinforcement learning both types of learning eventually lead to the same behavioral outcome: a positive prediction error increases the probability that the action/choice that led to the prediction error would be repeated in the future, and vice versa for negative prediction errors. As such, at the algorithmic level in which our asymmetric learning model was cast, the differences we have shown between dystonia patients and controls would still be expected to manifest behaviorally through diminished risk-aversion in dystonia patients.

In particular, our results are compatible with several alternative abnormalities in corticostriatal plasticity in DYT1 dystonia: (a) Abnormally strong LTP/weak LTD in D1-expressing striatal neurons only, with plasticity in the indirect pathway being intact; in this case, learning in the direct pathway would exhibit the abnormal asymmetries we argue for, whereas the indirect pathway would learn as normal. (b) Abnormally strong LTP/weak LTD in D1-expressing striatal neurons and the opposite pattern, abnormally strong LTD and/or weak LTP in D2-expressing striatal neurons of the indirect pathway in DYT1 dystonia. As a result, a positive prediction error would generate extra strong positive learning in the Go pathway, and a similarly large decrease in the propensity to avoid this stimulus due to activity in the 'NoGo' pathway. Conversely, learning from negative prediction errors would generate relatively little decrease in the propensity to 'Go' to the stimulus and little increase in the propensity to 'NoGo'. In both cases, the effect on both pathways would be in the same direction as is seen in the behavioral asymmetry. (c) Finally, abnormalities may exist in both pathways in the same direction (stronger LTP and weaker LTD), but with a larger effect on LTP as compared to LTD. In this case, a positive prediction error would increase 'Go' activity considerably, but not decrease 'NoGo' activity to the same extent. Negative prediction errors, on the other hand, would increase 'NoGo' propensities while decreasing 'Go' propensities to a lesser extent. This type of asymmetry can explain why the rodent studies suggested almost absent (not only weaker) LTD, but nevertheless, patients did not behave as if they did not learn at all from negative prediction errors. Unfortunately, our model and behavioral results cannot differentiate between these three options. We hope that future data, especially from transgenic DYT1 rodents, will clarify this issue.

Relative weighting of positive and negative outcomes shapes risk-sensitivity in tasks that involve learning from experience. Humans with preserved function of the basal ganglia have been shown to be risk-averse in such tasks. We showed that patients with DYT1 dystonia are more risk-neutral, a rational pattern of behavior given our reward statistics, and in such tasks in general. While this type of behavior may offer advantages under certain conditions, it may also contribute to impaired reinforcement learning of motor repertoire and fixation on actions that were once rewarded. In any case, these reinforcement-learning manifestations of what has been considered predominantly a motor disease provide support for linking corticostriatal synaptic plasticity and overt trial-and-error learning behavior in humans.

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Author details

1. David Arkadir

   Department of Neurology, Hadassah Medical Center and the Hebrew University, Jerusalem, Israel

   Contribution

   DA, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   arkadir@gmail.com

   Competing interests

   The authors declare that no competing interests exist.

2. Angela Radulescu

   1. Department of Psychology, Princeton University, Princeton, United States
   2. Princeton Neuroscience Institute, Princeton University, Princeton, United States

   Contribution

   AR, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Deborah Raymond

   Department of Neurology, Beth Israel Medical Center, New York, United States

   Contribution

   DR, Facilitated access to patient population, Assistance with patient recruitment, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Naomi Lubarr

   Department of Neurology, Beth Israel Medical Center, New York, United States

   Contribution

   NL, Facilitated access to patient population, Assistance with patient recruitment, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Susan B Bressman

   Department of Neurology, Beth Israel Medical Center, New York, United States

   Contribution

   SBB, Facilitated access to patient population, Assistance with patient recruitment, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Pietro Mazzoni

   The Neurological Institute, Columbia University, New York, United States

   Contribution

   PM, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Yael Niv

   1. Department of Psychology, Princeton University, Princeton, United States
   2. Princeton Neuroscience Institute, Princeton University, Princeton, United States

   Contribution

   YN, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   yael@princeton.edu

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-0259-8371

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