Mechanisms underlying dopamine-mediated reward bias in compulsive behaviors.
Pathological behaviors such as problem gambling or shopping are characterized by compulsive choice despite alternative options and negative costs. Reinforcement learning algorithms allow a computation of prediction error, a comparison of actual and expected outcomes, which updates our predictions and influences our subsequent choices. Using a reinforcement learning model, we show data consistent with the idea that dopamine agonists in susceptible individuals with Parkinson's disease increase the rate of learning from gain outcomes. Dopamine agonists also increase striatal prediction error activity, thus signifying a "better than expected" outcome. Thus, our findings are consistent with a model whereby a distorted estimation of the gain cue underpins a choice bias toward gains.
National Institutes of Health, Bethesda, MD 20892-1428, USA. voonval@gmail.com <voonval@gmail.com>
Valerie
V
Voon
Mathias
M
Pessiglione
Christina
C
Brezing
Cecile
C
Gallea
Hubert H
HH
Fernandez
Raymond J
RJ
Dolan
Mark
M
Hallett
D015982:Bias; D002755:Choice Behavior / Q000502:physiology; D003192:Compulsive Behavior; D003342:Corpus Striatum / Q000378:metabolism; D004298:Dopamine / Q000378:metabolism* / Q000627:therapeutic use; D005715:Gambling; D006801:Humans; D007858:Learning / Q000502:physiology; D008279:Magnetic Resonance Imaging; D010300:Parkinson Disease / Q000188:drug therapy / Q000378:metabolism* / Q000503:physiopathology; D012054:Reinforcement, Psychology; D012201:Reward
D004298:Dopamine