Literature DB >> 16971537

Dissociable systems for gain- and loss-related value predictions and errors of prediction in the human brain.

Juliana Yacubian1, Jan Gläscher, Katrin Schroeder, Tobias Sommer, Dieter F Braus, Christian Büchel.   

Abstract

Midbrain dopaminergic neurons projecting to the ventral striatum code for reward magnitude and probability during reward anticipation and then indicate the difference between actual and predicted outcome. It has been questioned whether such a common system for the prediction and evaluation of reward exists in humans. Using functional magnetic resonance imaging and a guessing task in two large cohorts, we are able to confirm ventral striatal responses coding both reward probability and magnitude during anticipation, permitting the local computation of expected value (EV). However, the ventral striatum only represented the gain-related part of EV (EV+). At reward delivery, the same area shows a reward probability and magnitude-dependent prediction error signal, best modeled as the difference between actual outcome and EV+. In contrast, loss-related expected value (EV-) and the associated prediction error was represented in the amygdala. Thus, the ventral striatum and the amygdala distinctively process the value of a prediction and subsequently compute a prediction error for gains and losses, respectively. Therefore, a homeostatic balance of both systems might be important for generating adequate expectations under uncertainty. Prevalence of either part might render expectations more positive or negative, which could contribute to the pathophysiology of mood disorders like major depression.

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Year:  2006        PMID: 16971537      PMCID: PMC6674602          DOI: 10.1523/JNEUROSCI.2915-06.2006

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  201 in total

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Review 6.  Learning from experience: event-related potential correlates of reward processing, neural adaptation, and behavioral choice.

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Review 9.  All that glitters ... dissociating attention and outcome expectancy from prediction errors signals.

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