The cortico-basal-ganglia circuit plays a critical role in decision making on the basis of probabilistic information. Computational models have suggested how this circuit could compute the probabilities of actions being appropriate according to Bayes' theorem. These models predict that the subthalamic nucleus (STN) provides feedback that normalizes the neural representation of probabilities, such that if the probability of one action increases, the probabilities of all other available actions decrease. Here we report the results of an experiment testing a prediction of this theory that disrupting information processing in the STN with deep brain stimulation should abolish the normalization of the neural representation of probabilities. In our experiment, we asked patients with Parkinson's disease to saccade to a target that could appear in one of two locations, and the probability of the target appearing in each location was periodically changed. When the stimulator was switched off, the target probability affected the reaction times (RT) of patients in a similar way to healthy participants. Specifically, the RTs were shorter for more probable targets and, importantly, they were longer for the unlikely targets. When the stimulator was switched on, the patients were still faster for more probable targets, but critically they did not increase RTs as the target was becoming less likely. This pattern of results is consistent with the prediction of the model that the patients on DBS no longer normalized their neural representation of prior probabilities. We discuss alternative explanations for the data in the context of other published results.
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Parkinson's, deep brain stimulation, eye movements, prior probabilities, reaction times, Aged, Bayes Theorem, Deep Brain Stimulation, Humans, Male, Middle Aged, Parkinson Disease, Psychomotor Performance, Reaction Time, Saccades, Subthalamic Nucleus