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Mind matters: placebo enhances reward learning in Parkinson's disease

Nature Neuroscience volume 17, pages 1793–1797 (2014)Cite this article

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Abstract

Expectations have a powerful influence on how we experience the world. Neurobiological and computational models of learning suggest that dopamine is crucial for shaping expectations of reward and that expectations alone may influence dopamine levels. However, because expectations and reinforcers are typically manipulated together, the role of expectations per se has remained unclear. We separated these two factors using a placebo dopaminergic manipulation in individuals with Parkinson's disease. We combined a reward learning task with functional magnetic resonance imaging to test how expectations of dopamine release modulate learning-related activity in the brain. We found that the mere expectation of dopamine release enhanced reward learning and modulated learning-related signals in the striatum and the ventromedial prefrontal cortex. These effects were selective to learning from reward: neither medication nor placebo had an effect on learning to avoid monetary loss. These findings suggest a neurobiological mechanism by which expectations shape learning and affect.

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Figure 1: Instrumental learning task.
Figure 2: Behavioral results for the gain and loss conditions (n = 18).
Figure 3: vmPFC value activity (n = 15).
Figure 4: Striatal prediction error activity (n = 15).

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Acknowledgements

We thank neurologists P. Greene, R. Alcalay, L. Coté and the nursing staff of the Center for Parkinson's Disease and Other Movement Disorders at Columbia University Presbyterian Hospital for help with patient recruitment and discussion of the findings, N. Johnston and B. Vail for help with data collection, M. Sharp, K. Duncan, D. Sulzer, J. Weber and B. Doll for insightful discussion, and M. Pessiglione and G.E. Wimmer for helpful comments on an earlier version of the manuscript. This study was supported by the Michael J. Fox Foundation and the US National Institutes of Health (R01MH076136).

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Author notes

  1. Tor D Wager and Daphna Shohamy: These authors contributed equally to this work.

Authors and Affiliations

  1. Psychology Department, Columbia University, New York, New York, USA

    Liane Schmidt, Erin Kendall Braun & Daphna Shohamy

  2. Psychology Department, University of Colorado at Boulder, Boulder, Colorado, USA

    Tor D Wager

  3. Kavli Center for Brain Science, Columbia University, New York, New York, USA

    Daphna Shohamy

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  1. Liane Schmidt
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Contributions

D.S. and T.D.W. planned the experiment. L.S., T.D.W. and D.S. developed the experimental design. L.S. and E.K.B. collected data. L.S. analyzed data. D.S. and T.D.W. supervised and assisted in data analysis. L.S., E.K.B., T.D.W. and D.S. wrote the manuscript.

Corresponding authors

Correspondence to Tor D Wager or Daphna Shohamy.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Experimental and task design.

(a) Experimental design. Each patient was scanned 3 times: off drug, on placebo, and on drug. Off drug and placebo scan sessions were counterbalanced across patients (order 1: 8 patients; order 2: 10 patients). Scan sessions lasted for 1 h and were separated by a 1 h break. Placebo and dopaminergic medication were crushed into orange juice and administered 30 min before the respective scan session. (b) Task structure. The outcome of a trial could be a gain of $10, nothing ($0), or a loss of $10. Two cue pairs, a gain and a loss cue pair, were randomly intermixed. Within the gain cue pair, optimal choices led to a gain of $10 with a probability of.75 and to nothing ($0) with a probability of.25. Within the loss cue pair, optimal choices led to nothing ($0) with a probability of.75 and to a loss of $10 with a probability of.25.

Supplementary Figure 2 Partial correlations between on drug and placebo controlling for the effects of off drug (n = 18).

Partial correlations between on drug and placebo controlling for effects of off drug for reward learning (left) (r = 0.34, p = 0.08) and motor symptoms (right) (r = 0.59, p = 0.01). On drug and placebo expressed as residuals after confounds due to off drug effects were regressed out.

Supplementary Figure 3 BOLD responses to choices and feedback in the vmPFC and the ventral striatum (n = 15).

(a) Parameter estimates for choices (correct vs. incorrect) from the vmPFC region of interest in the gain and loss condition for off drug (gray), placebo (blue), and on drug (black) treatments. (b) Parameter estimates for feedback (correct vs. incorrect) from the ventral striatum region of interest in the gain and loss condition for the off drug (gray), placebo (blue), and on drug (black) treatments. Error bars represent within subject standard errors.

Supplementary Figure 4 Ventral striatum responses to components of the prediction error (n = 15).

Parameter estimates (betas) from the ventral striatum at time of outcome for the two components of prediction error – expected value and reward – in the off drug (gray), placebo (blue) and on drug (black) treatments. Error bars represent within subject standard errors.

Supplementary Figure 5 Reaction times during learning across conditions and treatment.

Average reaction time in seconds for the gain (left) and loss condition (right) in the off drug (gray), placebo (blue), and on drug (black) treatments. The reaction time curves depict how fast patients chose the optimal choice cue. Error bars represent within-subject standard errors.

Supplementary Figure 6 Behavioral results for the subgroup of patients scanned with fMRI (n = 15).

Percentage of observed optimal choices binned across bocks of 8 trials (left), smoothed (middle), and modeled optimal choices (right) in the gain (top) and loss (bottom) conditions for the off drug (gray), placebo (blue), and on drug (black) treatments. The learning curves depict how often patients chose the 75% rewarding cue (t11 = 5.2, p < 0.001) during the gain condition and the 75% nothing cue (t11 = 4.1, p < 0.01) during the loss condition. Error bars represent within-subject standard errors.

Supplementary Figure 7 Observed and modeled behavioral results for the scanned patients (n = 15).

Percentage of observed behavioral choices (dots) and modeled optimal choices (solid lines) across trials for off drug (gray), placebo (blue), and on drug (black) treatments. The learning curves depict how often patients chose the 75% rewarding cue (t11 = 5.2, p < 0.001) during the gain condition and the 75% loss cue (t11 = −4.1, p < 0.01) during the loss condition. The modeled learning curves represent the probabilities of choice on each trial, as predicted by the RL model.

Supplementary Figure 8 Direct comparisons of value and prediction error responses across treatments.

Statistical parametric maps (SPMs) are superimposed on the average structural scan. (a) SPMs are masked for the vmPFC ROI, defined a priori by MNI = [−1, 27, −8]) from Hare et al. 2008, at p < 0.05 uncorrected. (b) Whole brain activations for value at p < 0.05 uncorrected. (c) SPMs are masked for the ventral striatum ROI, defined a priori by MNI = [−10, 12, −8]) from Pessiglione et al. 2006, at p < 0.05 uncorrected. (d) Whole brain activations for prediction error at p < 0.05 uncorrected.

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Schmidt, L., Braun, E., Wager, T. et al. Mind matters: placebo enhances reward learning in Parkinson's disease. Nat Neurosci 17, 1793–1797 (2014). https://doi.org/10.1038/nn.3842

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