Subjective estimates of uncertainty during gambling and impulsivity after subthalamic deep brain stimulation for Parkinson’s disease

Abstract

Subthalamic deep brain stimulation (STN-DBS) for Parkinson's disease may modulate chronometric and instrumental aspects of choice behaviour, including motor inhibition, decisional slowing, and value sensitivity. However, it is not well known whether STN-DBS affects more complex aspects of decision-making, such as the influence of subjective estimates of uncertainty on choices. In this study, thirty-eight participants with Parkinson's disease played a virtual casino prior to subthalamic DBS (whilst 'on' medication) and again, threemonths postoperatively (whilst 'on' stimulation). At the group level, there was a small but statistically significant postoperative decrease in impulsivity, as quantified by the Barratt Impulsiveness Scale (BIS). The gambling behaviour of participants (bet increases, slot machine switches and double or nothing gambles) was associated with this self-reported measure of impulsivity. However, there was a large variance in outcome amongst participants, and we were interested in whether individual differences in subjective estimates of uncertainty (specifically, volatility) were related to differences in pre-and postoperative impulsivity. To examine these individual differences, we fit a computational model (the Hierarchical Gaussian Filter, HGF), to choices made during slot machine game play as well as a simpler reinforcement learning model based on the Rescorla-Wagner formalism. The HGF was superior in accounting for the behaviour of our participants, suggesting that participants incorporated beliefs about environmental uncertainty when updating their beliefs about gambling outcome and translating these beliefs into action. A specific aspect of subjective uncertainty, the participant's estimate of the tendency of the slot machine's winning probability to change (volatility), increased subsequent to DBS. Additionally, the decision temperature of the response model decreased post-operatively, implying greater stochasticity in the belief-to-choice mapping of participants.Model parameter estimates were significantly associated with impulsivity; specifically, increased uncertainty was related to increased postoperative impulsivity. Moreover, changes in these parameter estimates were significantly associated with the maximum post-operative change in impulsivity over a six month follow up period. Our findings suggest that impulsivity in persons with Parkinson's disease may be influenced by subjective estimates of uncertainty (environmental volatility) and implicate a role for the subthalamic nucleus in the modulation of outcome certainty. Furthermore, our work outlines a possible approach to characterising those persons who become more impulsive after subthalamic DBS, an intervention in which non-motor outcomes can be highly variable.3 2014). For example, consider deciding how to invest a sum of money in order to maximise profit. In a dependable economic setting with a reliable government, low unemployment, steadily rising house prices and a stable stock market, one might be confident that...

A model for learning based on the joint estimation of stochasticity and volatility

“…More generally, this modeling approach, which quantifies misattribution of stochasticity to volatility and vice versa, might be useful for understanding various other brain disorders that are thought to influence processing of uncertainty and have largely been studied in the context of volatility in the past decade 14,16,17,19,21,22,66,79 . As another example, positive symptoms in schizophrenia have been argued to result from some alterations in prior vs likelihood processing, perhaps driven by abnormal attribution of uncertainty (or precision) to top-down expectations 80 .…”

“…Abnormalities in uncertainty and inference, broadly, have been hypothesized to play a role in numerous disorders, including especially anxiety and schizophrenia. More specifically, abnormalities in volatility-related learning adjustments have been reported in patients or people reporting symptoms of several mental illnesses 13,14,[16][17][18][19][20][21][22][23]36 . The current model provides a more detailed framework for better dissecting these effects, though this will ideally require a new generation of experiments manipulating both factors.…”

“…All else equal, when volatility is higher, the organism is more uncertain about the cue's value (because the true value will on average have fluctuated more following each observation), and so the learning rate (the reliance on each new outcome) should be higher. A series of experiments have reported behavioral and neural signatures of these effects of volatility enhancing learning rate, and also their disruption in relation to psychiatric symptoms 6,8,10,[13][14][15][16][17][18][19][20][21][22][23] . Conversely, stochasticity also affects the learning rate, but in the opposite direction: all else equal, when individual outcomes are more stochastic (larger stochasticity), they are less informative about the cue's true value and the learning rate, in turn, should be smaller.…”