scholarly journals Pleasure, reward value and prediction error in anhedonia

2021 ◽  
Author(s):  
Karel Kieslich ◽  
Vincent Valton ◽  
Jonathan Paul Roiser

In order to develop effective treatments for anhedonia we need to understand its underlying neurobiological mechanisms. Anhedonia is conceptually strongly linked to reward processing, which involves a variety of cognitive and neural operations. This article reviews the evidence for impairments in experiencing hedonic response (pleasure), reward valuation, and reward learning based on outcomes (commonly conceptualised in terms of “reward prediction error”). Synthesizing behavioural and neuroimaging findings, we examine case-control studies of patients with depression and schizophrenia, including those focusing specifically on anhedonia. Overall, there is reliable evidence that depression and schizophrenia are associated with disrupted reward processing. In contrast to the historical definition of anhedonia, there is surprisingly limited evidence for impairment in the ability to experience pleasure in depression and schizophrenia. There is some evidence that learning about reward and reward prediction error signals are impaired in depression and schizophrenia, but the literature is inconsistent. The strongest evidence is for impairments in the representation of reward value and how this is used to guide action. Future studies would benefit from focusing on impairments in reward processing specifically in anhedonic samples, including transdiagnostically, and from using designs separating different components of reward processing, formulating them in computational terms, and moving beyond cross-sectional designs to provide an assessment of causality.

2016 ◽  
Vol 18 (1) ◽  
pp. 23-32 ◽  

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards—an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.


2019 ◽  
Vol 146 ◽  
pp. 107735 ◽  
Author(s):  
Luke D. Smillie ◽  
Hayley K. Jach ◽  
David M. Hughes ◽  
Jan Wacker ◽  
Andrew J. Cooper ◽  
...  

2020 ◽  
Author(s):  
Pramod Kaushik ◽  
Jérémie Naudé ◽  
Surampudi Bapi Raju ◽  
Frédéric Alexandre

AbstractClassical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.


2018 ◽  
Vol 83 (9) ◽  
pp. S164
Author(s):  
Hanna Keren ◽  
Nathan Fox ◽  
Ellen Leibenluft ◽  
Daniel S. Pine ◽  
Argyris Stringaris

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S11-S11
Author(s):  
Teresa Katthagen ◽  
Jakob Kaminski ◽  
Andreas Heinz ◽  
Ralph Buchert ◽  
Florian Schlagenhauf

Abstract Background Increased striatal dopamine synthesis capacity (DSC) has consistently been reported in patients with schizophrenia (Sz). However, the functional mechanism translating this into behavior and symptoms remains unclear. It has been proposed that heightened striatal dopamine may blunt dopaminergic reward prediction error (RPE) signaling during reinforcement learning. Methods In this study, we investigated striatal DSC and RPEs and their association in unmedicated Sz and healthy controls. 23 healthy controls (HC) and 20 unmedicated Sz took part in an FDOPA-PET scan measuring DSC and underwent fMRI scanning, where they performed a reversal learning paradigm. We compared groups regarding DSC und neural RPE signals and probed the respective correlation (23 HC and 16 Sz for both measures). Results There was no significant difference between HC and Sz in DSC. Taking into account comorbid alcohol abuse revealed that only patients without such abuse showed elevated DSC in the associative and sensorimotor striatum, while those with abuse did not differ from HC. Patients performed worse during learning, accompanied by a reduced RPE signal in the ventral striatum. In HC, the DSC in the limbic striatum correlated with higher RPE signaling, while there was no significant association in patients. DSC in the associative striatum correlated with higher positive symptoms, and blunted RPE signaling was associated with negative symptoms. Discussion Our results suggest that dopamine modulation of RPE is impaired in schizophrenia. Furthermore, we observed a dissociation with elevated DSC in the associative and sensorimotor striatum contributing to positive symptoms and blunted RPE in the ventral striatum to negative symptoms.


2019 ◽  
Vol 39 (25) ◽  
pp. 5010-5017 ◽  
Author(s):  
Ehsan Sedaghat-Nejad ◽  
David J. Herzfeld ◽  
Reza Shadmehr

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