CiteULike: nelmor human

Perceptual accuracy and conflicting effects of certainty on risk-taking behaviour

Sharoni Shafir — 2008-06-12T04:36:15-00:00

Nature, Vol. 453, No. 7197., pp. 917-920.

Basal Ganglia Play a Unique Role in Task Switching within the Frontal-Subcortical Circuits: Evidence from Patients with Focal Lesions

Einat Yehene — 2008-05-19T09:09:26-00:00

J. Cogn. Neurosci., Vol. 20, No. 6. (1 June 2008), pp. 1079-1093.

The performance of patients with lesions involving the basal ganglia (BG) was compared to that of patients with prefrontal (PFC) lesions, thalamic (TH) lesions, and age-matched controls in order to examine the specific role of the BG within the frontal-subcortical circuits (FSCC) in task switching. All the BG patients and none of the other participants showed a marked increase in error rate in incongruent trials where correct responses depended upon the choice of the correct task rule. Some BG patients erred in failing to switch tasks and others failed despite their attempt to switch tasks. Additionally, reaction time results indicate abnormal response repetition effects among the BG patients; failure in benefiting from advance task information among all the patients; and increased task mixing costs following PFC lesions. The authors conclude that although the frontal-subcortical circuits jointly determine some behaviors (such as benefiting from preparation), the BG play a unique role within the FSCC in action selection and/or the inhibition of irrelevant information.

Low-Serotonin Levels Increase Delayed Reward Discounting in Humans

Nicolas Schweighofer — 2008-04-24T13:04:57-00:00

J. Neurosci., Vol. 28, No. 17. (23 April 2008), pp. 4528-4532.

Previous animal experiments have shown that serotonin is involved in the control of impulsive choice, as characterized by high preference for small immediate rewards over larger delayed rewards. Previous human studies under serotonin manipulation, however, have been either inconclusive on the effect on impulsivity or have shown an effect in the speed of action-reward learning or the optimality of action choice. Here, we manipulated central serotonergic levels of healthy volunteers by dietary tryptophan depletion and loading. Subjects performed a "dynamic" delayed reward choice task that required a continuous update of the reward value estimates to maximize total gain. By using a computational model of delayed reward choice learning, we estimated the parameters governing the subjects' reward choices in low-, normal, and high-serotonin conditions. We found an increase of proportion in small reward choices, together with an increase in the rate of discounting of delayed rewards in the low-serotonin condition compared with the control and high-serotonin conditions. There were no significant differences between conditions in the speed of learning of the estimated delayed reward values or in the variability of reward choice. Therefore, in line with previous animal experiments, our results show that low-serotonin levels steepen delayed reward discounting in humans. The combined results of our previous and current studies suggest that serotonin may adjust the rate of delayed reward discounting via the modulation of specific loops in parallel corticobasal ganglia circuits. 10.1523/JNEUROSCI.4982-07.2008

Hold Your Horses: Impulsivity, Deep Brain Stimulation, and Medication in Parkinsonism

Michael Frank — 2007-11-20T16:59:08-00:00

Science (25 October 2007), 1146157.

Deep brain stimulation (DBS) of the subthalamic nucleus dramatically improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. Here we show that DBS selectively interferes with the normal ability to slow down when faced with decision conflict. While on DBS, patients actually sped up under high conflict conditions. This form of impulsivity was not affected by dopaminergic medication status. Instead, medication impaired patients' ability to learn from negative decision outcomes. These findings implicate independent mechanisms leading to impulsivity in treated Parkinson's patients, and were predicted by a single neurocomputational model of the basal ganglia. 10.1126/science.1146157

Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning

Michael Frank — 2007-10-23T09:47:07-00:00

Proceedings of the National Academy of Sciences, Vol. 104, No. 41. (9 October 2007), pp. 16311-16316.

What are the genetic and neural components that support adaptive learning from positive and negative outcomes? Here, we show with genetic analyses that three independent dopaminergic mechanisms contribute to reward and avoidance learning in humans. A polymorphism in the DARPP-32 gene, associated with striatal dopamine function, predicted relatively better probabilistic reward learning. Conversely, the C957T polymorphism of the DRD2 gene, associated with striatal D2 receptor function, predicted the degree to which participants learned to avoid choices that had been probabilistically associated with negative outcomes. The Val/Met polymorphism of the COMT gene, associated with prefrontal cortical dopamine function, predicted participants' ability to rapidly adapt behavior on a trial-to-trial basis. These findings support a neurocomputational dissociation between striatal and prefrontal dopaminergic mechanisms in reinforcement learning. Computational maximum likelihood analyses reveal independent gene effects on three reinforcement learning parameters that can explain the observed dissociations. 10.1073/pnas.0706111104

Neural Antecedents of Financial Decisions

Brian Knutson — 2007-08-03T09:19:47-00:00

J. Neurosci., Vol. 27, No. 31. (1 August 2007), pp. 8174-8177.

To explain investing decisions, financial theorists invoke two opposing metrics: expected reward and risk. Recent advances in the spatial and temporal resolution of brain imaging techniques enable investigators to visualize changes in neural activation before financial decisions. Research using these methods indicates that although the ventral striatum plays a role in representation of expected reward, the insula may play a more prominent role in the representation of expected risk. Accumulating evidence also suggests that antecedent neural activation in these regions can be used to predict upcoming financial decisions. These findings have implications for predicting choices and for building a physiologically constrained theory of decision-making. 10.1523/JNEUROSCI.1564-07.2007

Reinforcement learning signals predict future decisions.

MX Cohen — 2007-02-21T01:41:58-00:00

J Neurosci, Vol. 27, No. 2. (10 January 2007), pp. 371-378.

Optimal behavior in a competitive world requires the flexibility to adapt decision strategies based on recent outcomes. In the present study, we tested the hypothesis that this flexibility emerges through a reinforcement learning process, in which reward prediction errors are used dynamically to adjust representations of decision options. We recorded event-related brain potentials (ERPs) while subjects played a strategic economic game against a computer opponent to evaluate how neural responses to outcomes related to subsequent decision-making. Analyses of ERP data focused on the feedback-related negativity (FRN), an outcome-locked potential thought to reflect a neural prediction error signal. Consistent with predictions of a computational reinforcement learning model, we found that the magnitude of ERPs after losing to the computer opponent predicted whether subjects would change decision behavior on the subsequent trial. Furthermore, FRNs to decision outcomes were disproportionately larger over the motor cortex contralateral to the response hand that was used to make the decision. These findings provide novel evidence that humans engage a reinforcement learning process to adjust representations of competing decision options.