scholarly journals Prefrontal Cortex Fails to Learn from Reward Prediction Errors in Alcohol Dependence

2010 ◽  
Vol 30 (22) ◽  
pp. 7749-7753 ◽  
Author(s):  
S. Q. Park ◽  
T. Kahnt ◽  
A. Beck ◽  
M. X. Cohen ◽  
R. J. Dolan ◽  
...  
2020 ◽  
Vol 22 (8) ◽  
pp. 849-859
Author(s):  
Julian Macoveanu ◽  
Hanne L. Kjærstad ◽  
Henry W. Chase ◽  
Sophia Frangou ◽  
Gitte M. Knudsen ◽  
...  

Author(s):  
Benjamin Voloh ◽  
Mariann Oemisch ◽  
Thilo Womelsdorf

AbstractThe prefrontal cortex and striatum form a recurrent network whose spiking activity encodes multiple types of learning-relevant information. This spike-encoded information is evident in average firing rates, but finer temporal coding might allow multiplexing and enhanced readout across the connected the network. We tested this hypothesis in the fronto-striatal network of nonhuman primates during reversal learning of feature values. We found that neurons encoding current choice outcomes, outcome prediction errors, and outcome history in their firing rates also carried significant information in their phase-of-firing at a 10-25 Hz beta frequency at which they synchronized across lateral prefrontal cortex, anterior cingulate cortex and striatum. The phase-of-firing code exceeded information that could be obtained from firing rates alone, was strong for inter-areal connections, and multiplexed information at three different phases of the beta cycle that were offset from the preferred spiking phase of neurons. Taken together, these findings document the multiplexing of three different types of information in the phase-of-firing at an interareally shared beta oscillation frequency during goal-directed behavior.HighlightsLateral prefrontal cortex, anterior cingulate cortex and striatum show phase-of-firing encoding for outcome, outcome history and reward prediction errors.Neurons with phase-of-firing code synchronize long-range at 10-25 Hz.Spike phases encoding reward prediction errors deviate from preferred synchronization phases.Anterior cingulate cortex neurons show strongest long-range effects.


2017 ◽  
Author(s):  
Jeroen P.H. Verharen ◽  
Johannes W. de Jong ◽  
Theresia J.M. Roelofs ◽  
Christiaan F.M. Huffels ◽  
Ruud van Zessen ◽  
...  

AbstractHyperdopaminergic states in mental disorders are associated with disruptive deficits in decision-making. However, the precise contribution of topographically distinct mesencephalic dopamine pathways to decision-making processes remains elusive. Here we show, using a multidisciplinary approach, how hyperactivity of ascending projections from the ventral tegmental area (VTA) contributes to faulty decision-making in rats. Activation of the VTA-nucleus accumbens pathway leads to insensitivity to loss and punishment due to impaired processing of negative reward prediction errors. In contrast, activation of the VTA-prefrontal cortex pathway promotes risky decision-making without affecting the ability to choose the economically most beneficial option. Together, these findings show how malfunction of ascending VTA projections affects value-based decision-making, providing a mechanistic understanding of the reckless behaviors seen in substance abuse, mania, and after dopamine replacement therapy in Parkinson’s disease.


2021 ◽  
Author(s):  
Patrick Wiegel ◽  
Meaghan Elizabeth Spedden ◽  
Christina Ramsenthaler ◽  
Mikkel Malling Beck ◽  
Jesper Lundbye-Jensen

AbstractThe history of our actions and the outcomes of these represent important information, which can inform choices, and efficiently guide future behaviour. While unsuccessful (S-) outcomes are expected to lead to more explorative motor states and increased behavioural variability, successful (S+) outcomes lead to reinforcement of the previous action and thus exploitation. Here, we show that during reinforcement motor learning, humans attribute different values to previous actions when they experience S- vs. S+ outcomes. Behavioural variability after S- outcomes is influenced more by the previous outcomes compared to what is observed after S+ outcomes. Using electroencephalography, we show that neural oscillations of the prefrontal cortex encode the level of reinforcement (high beta frequencies) and reflect the detection of reward prediction errors (theta frequencies). The results suggest that S+ experiences ‘overwrite’ previous motor states to a greater extent than S- experiences and that modulations in neural oscillations in the prefrontal cortex play a potential role in encoding the (changes in) movement variability state during reinforcement motor learning.


Neuron ◽  
2018 ◽  
Vol 98 (3) ◽  
pp. 616-629.e6 ◽  
Author(s):  
Clara Kwon Starkweather ◽  
Samuel J. Gershman ◽  
Naoshige Uchida

2019 ◽  
Vol 31 (1) ◽  
pp. 8-23 ◽  
Author(s):  
José J. F. Ribas-Fernandes ◽  
Danesh Shahnazian ◽  
Clay B. Holroyd ◽  
Matthew M. Botvinick

A longstanding view of the organization of human and animal behavior holds that behavior is hierarchically organized—in other words, directed toward achieving superordinate goals through the achievement of subordinate goals or subgoals. However, most research in neuroscience has focused on tasks without hierarchical structure. In past work, we have shown that negative reward prediction error (RPE) signals in medial prefrontal cortex (mPFC) can be linked not only to superordinate goals but also to subgoals. This suggests that mPFC tracks impediments in the progression toward subgoals. Using fMRI of human participants engaged in a hierarchical navigation task, here we found that mPFC also processes positive prediction errors at the level of subgoals, indicating that this brain region is sensitive to advances in subgoal completion. However, when subgoal RPEs were elicited alongside with goal-related RPEs, mPFC responses reflected only the goal-related RPEs. These findings suggest that information from different levels of hierarchy is processed selectively, depending on the task context.


2020 ◽  
Author(s):  
Kate Ergo ◽  
Luna De Vilder ◽  
Esther De Loof ◽  
Tom Verguts

Recent years have witnessed a steady increase in the number of studies investigating the role of reward prediction errors (RPEs) in declarative learning. Specifically, in several experimental paradigms RPEs drive declarative learning; with larger and more positive RPEs enhancing declarative learning. However, it is unknown whether this RPE must derive from the participant’s own response, or whether instead any RPE is sufficient to obtain the learning effect. To test this, we generated RPEs in the same experimental paradigm where we combined an agency and a non-agency condition. We observed no interaction between RPE and agency, suggesting that any RPE (irrespective of its source) can drive declarative learning. This result holds implications for declarative learning theory.


Author(s):  
Leandro F. Vendruscolo ◽  
George F. Koob

Alcohol use disorder is a chronically relapsing disorder that involves (1) compulsivity to seek and take alcohol, (2) difficulty in limiting alcohol intake, and (3) emergence of a negative emotional state (e.g., dysphoria, anxiety, irritability) in the absence of alcohol. Alcohol addiction encompasses a three-stage cycle that becomes more intense as alcohol use progresses: binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation. These stages engage neuroadaptations in brain circuits that involve the basal ganglia (reward hypofunction), extended amygdala (stress sensitization), and prefrontal cortex (executive function disorder). This chapter discusses key neuroadaptations in the hypothalamic and extrahypothalamic stress systems and the critical role of glucocorticoid receptors. These neuroadaptations contribute to negative emotional states that powerfully drive compulsive alcohol drinking and seeking. These changes in association with a disruption of prefrontal cortex function that lead to cognitive deficits and poor decision making contribute to the chronic relapsing nature of alcohol dependence.


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