The ability to rapidly and flexibly adapt decisions to available rewards is crucial for survival in dynamic environments. Rewardbased decisions are guided by reward expectations that are updated based on prediction errors, and processing of these errors involves dopaminergic neuromodulation in the striatum. To test the hypothesis that the COMT gene Val 158 Met polymorphism leads to interindividual differences in reward-based learning, we used the neuromodulatory role of dopamine in signaling prediction errors. We show a behavioral advantage for the phylogenetically ancestral Val/Val genotype in an instrumental reversal learning task that requires rapid and flexible adaptation of decisions to changing reward contingencies in a dynamic environment. Implementing a reinforcement learning model with a dynamic learning rate to estimate prediction error and learning rate for each trial, we discovered that a higher and more flexible learning rate underlies the advantage of the Val/Val genotype. Model-based fMRI analysis revealed that greater and more differentiated striatal fMRI responses to prediction errors reflect this advantage on the neurobiological level. Learning rate-dependent changes in effective connectivity between the striatum and prefrontal cortex were greater in the Val/Val than Met/Met genotype, suggesting that the advantage results from a downstream effect of the prefrontal cortex that is presumably mediated by differences in dopamine metabolism. These results show a critical role of dopamine in processing the weight a particular prediction error has on the expectation updating for the next decision, thereby providing important insights into neurobiological mechanisms underlying the ability to rapidly and flexibly adapt decisions to changing reward contingencies.COMT ͉ dopamine ͉ functional MRI ͉ learning rate ͉ reinforcement learning H uman learning spans a wide range of functions from ancient evolutionary accomplishments [e.g., fast and intuitive learning from rewards (1, 2)], to complex executive processes that require high capacities of attention and working memory (3, 4). These functions are supported by interacting brain regions that comprise phylogenetically ancient structures (e.g., the striatum) as well as more recently evolved neocortical structures [e.g., the prefrontal cortex (PFC)]. Still, different learning processes rely on the same neuromodulators. Dopamine (DA) modulates synaptic efficacy in both the striatum and PFC (5) and is thus involved in different kinds of learning. Accordingly, interindividual differences in dopaminergic neuromodulatory mechanisms contribute to performance differences in both simple instrumental learning tasks and complex cognitive tasks (6-10).One interindividual difference in dopaminergic neuromodulation that has been linked to PFC function is the uniquely human Val 158 Met polymorphism in the enzyme catechol-O-methyltransferase (COMT), which regulates extrasynaptical DA degradation (8-10). COMT activity is lower in individuals homozygous for the mutated allele...

Learning by following explicit advice is fundamental for human cultural evolution, yet the neurobiology of adaptive social learning is largely unknown. Here, we used simulations to analyze the adaptive value of social learning mechanisms, computational modeling of behavioral data to describe cognitive mechanisms involved in social learning, and model-based functional magnetic resonance imaging (fMRI) to identify the neurobiological basis of following advice. One-time advice received before learning had a sustained influence on people's learning processes. This was best explained by social learning mechanisms implementing a more positive evaluation of the outcomes from recommended options. Computer simulations showed that this “outcome-bonus” accumulates more rewards than an alternative mechanism implementing higher initial reward expectation for recommended options. fMRI results revealed a neural outcome-bonus signal in the septal area and the left caudate. This neural signal coded rewards in the absence of advice, and crucially, it signaled greater positive rewards for positive and negative feedback after recommended rather than after non-recommended choices. Hence, our results indicate that following advice is intrinsically rewarding. A positive correlation between the model's outcome-bonus parameter and amygdala activity after positive feedback directly relates the computational model to brain activity. These results advance the understanding of social learning by providing a neurobiological account for adaptive learning from advice.

Early biological concepts of language were predominantly corticocentric, but over the last decades biolinguistic research, equipped with new technical possibilities, has drastically changed this view. To date, connectionist models, conceiving linguistic skills as corticobasal network activities, dominate our understanding of the neural basis of language. However, beyond the notion of an involvement of the thalamus and, in most cases also, the basal ganglia (BG) in linguistic operations, specific functions of the respective depth structures mostly remain rather controversial. In this review, some of these issues shall be discussed, particularly the functional configuration of basal network components and the language specificity of subcortical supporting activity. Arguments will be provided for a primarily cortico-thalamic language network. In this view, the thalamus does not engage in proper linguistic operations, but rather acts as a central monitor for language-specific cortical activities, supported by the BG in both perceptual and productive language execution.

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The mesocorticolimbic dopamine (DA) system linking the dopaminergic midbrain to the prefrontal cortex and subcortical striatum has been shown to be sensitive to reinforcement in animals and humans. Within this system, coexistent segregated striato-frontal circuits have been linked to different functions. In the present study, we tested patients with Parkinson's disease (PD), a neurodegenerative disorder characterized by dopaminergic cell loss, on two reward-based learning tasks assumed to differentially involve dorsal and ventral striato-frontal circuits. 15 non-depressed and non-demented PD patients on levodopa monotherapy were tested both on and off medication. Levodopa had beneficial effects on the performance on an instrumental learning task with constant stimulus-reward associations, hypothesized to rely on dorsal striato-frontal circuits. In contrast, performance on a reversal learning task with changing reward contingencies, relying on ventral striato-frontal structures, was better in the unmedicated state. These results are in line with the “overdose hypothesis” which assumes detrimental effects of dopaminergic medication on functions relying upon less affected regions in PD. This study demonstrates, in a within-subject design, a double dissociation of dopaminergic medication and performance on two reward-based learning tasks differing in regard to whether reward contingencies are constant or dynamic. There was no evidence for a dose effect of levodopa on reward-based behavior with the patients’ actual levodopa dose being uncorrelated to their performance on the reward-based learning tasks.

BackgroundVerbal Fluency is reduced in patients with Parkinson’s disease, particularly if treated with deep brain stimulation. This deficit could arise from general factors, such as reduced working speed or from dysfunctions in specific lexical domains.ObjectiveTo test whether DBS-associated Verbal Fluency deficits are accompanied by changed dynamics of word processing.Methods21 Parkinson’s disease patients with and 26 without deep brain stimulation of the subthalamic nucleus as well as 19 healthy controls participated in the study. They engaged in Verbal Fluency and (primed) Lexical Decision Tasks, testing phonemic and semantic word production and processing time. Most patients performed the experiments twice, ON and OFF stimulation or, respectively, dopaminergic drugs.ResultsPatients generally produced abnormally few words in the Verbal Fluency Task. This deficit was more severe in patients with deep brain stimulation who additionally showed prolonged response latencies in the Lexical Decision Task. Slowing was independent of semantic and phonemic word priming. No significant changes of performance accuracy were obtained. The results were independent from the treatment ON or OFF conditions.ConclusionLow word production in patients with deep brain stimulation was accompanied by prolonged latencies for lexical decisions. No indication was found that the latter slowing was due to specific lexical dysfunctions, so that it probably reflects a general reduction of cognitive working speed, also evident on the level of Verbal Fluency. The described abnormalities seem to reflect subtle sequelae of the surgical procedure for deep brain stimulation rather than of the proper neurostimulation.

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