Human Dorsal Striatal Activity during Choice Discriminates Reinforcement Learning Behavior from the Gambler's Fallacy.
Item Type:Journal Article
Citation:Jessup RK, O'Doherty JP, Human Dorsal Striatal Activity during Choice Discriminates Reinforcement Learning Behavior from the Gambler's Fallacy., Journal of Neuroscience, 31, 17, 2011, 6296-304
Human dorsal striatal activity during choice discriminates reinforcement learning behavior from the gambler's fallacy.pdf (Published (publisher's copy) - Peer Reviewed) 833.5Kb
Reinforcement learning theory has generated substantial interest in neurobiology, particularly because of the resemblance between phasic dopamine and reward prediction errors. Actor-critic theories have been adapted to account for the functions of the striatum, with parts of the dorsal striatum equated to the actor. Here, we specifically test whether the human dorsal striatum-as predicted by an actor-critic instantiation-is used on a trial-to-trial basis at the time of choice to choose in accordance with reinforcement learning theory, as opposed to a competing strategy: the gambler's fallacy. Using a partial-brain functional magnetic resonance imaging scanning protocol focused on the striatum and other ventral brain areas, we found that the dorsal striatum is more active when choosing consistent with reinforcement learning compared with the competing strategy. Moreover, an overlapping area of dorsal striatum along with the ventral striatum was found to be correlated with reward prediction errors at the time of outcome, as predicted by the actor-critic framework. These findings suggest that the same region of dorsal striatum involved in learning stimulus-response associations may contribute to the control of behavior during choice, thereby using those learned associations. Intriguingly, neither reinforcement learning nor the gambler's fallacy conformed to the optimal choice strategy on the specific decision-making task we used. Thus, the dorsal striatum may contribute to the control of behavior according to reinforcement learning even when the prescriptions of such an algorithm are suboptimal in terms of maximizing future rewards.
Science Foundation Ireland (SFI)
Author: O'DOHERTY, JOHN
Type of material:Journal Article
Series/Report no:Journal of Neuroscience
Availability:Full text available