scholarly journals Probabilistic reinforcement learning abnormalities and their correlates in adolescent bipolar disorders.

2018 ◽  
Vol 127 (8) ◽  
pp. 807-817 ◽  
Author(s):  
Snežana Urošević ◽  
Tate Halverson ◽  
Eric A. Youngstrom ◽  
Monica Luciana
Keyword(s):  
Reinforcement Learning ◽  
Bipolar Disorders ◽  
Probabilistic Reinforcement

scholarly journals Probabilistic Reinforcement Learning in Patients With Schizophrenia: Relationships to Anhedonia and Avolition

2016 ◽  
Vol 1 (5) ◽  
pp. 460-473 ◽  
Author(s):  
Erin C. Dowd ◽  
Michael J. Frank ◽  
Anne Collins ◽  
James M. Gold ◽  
Deanna M. Barch
Keyword(s):  
Reinforcement Learning ◽  
Probabilistic Reinforcement

scholarly journals Modeling changes in probabilistic reinforcement learning during adolescence

2021 ◽  
Vol 17 (7) ◽  
pp. e1008524
Author(s):  
Liyu Xia ◽  
Sarah L. Master ◽  
Maria K. Eckstein ◽  
Beth Baribault ◽  
Ronald E. Dahl ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Task ◽  
Learning Rate ◽  
Integration Time ◽  
Daily Experience ◽  
Salivary Testosterone ◽  
Probabilistic Uncertainty ◽  
Probabilistic Reinforcement ◽  
Hierarchical Bayesian Methods ◽  
Reinforcement Learning Models

In the real world, many relationships between events are uncertain and probabilistic. Uncertainty is also likely to be a more common feature of daily experience for youth because they have less experience to draw from than adults. Some studies suggest probabilistic learning may be inefficient in youths compared to adults, while others suggest it may be more efficient in youths in mid adolescence. Here we used a probabilistic reinforcement learning task to test how youth age 8-17 (N = 187) and adults age 18-30 (N = 110) learn about stable probabilistic contingencies. Performance increased with age through early-twenties, then stabilized. Using hierarchical Bayesian methods to fit computational reinforcement learning models, we show that all participants’ performance was better explained by models in which negative outcomes had minimal to no impact on learning. The performance increase over age was driven by 1) an increase in learning rate (i.e. decrease in integration time scale); 2) a decrease in noisy/exploratory choices. In mid-adolescence age 13-15, salivary testosterone and learning rate were positively related. We discuss our findings in the context of other studies and hypotheses about adolescent brain development.

scholarly journals Hippocampal Contribution to Probabilistic Feedback Learning: Modeling Observation- and Reinforcement-based Processes

2021 ◽  
Author(s):  
Virginie Patt ◽  
Daniela Palombo ◽  
Michael Esterman ◽  
Mieke Verfaellie
Keyword(s):  
Reinforcement Learning ◽  
Monitoring Network ◽  
Learning Processes ◽  
Learning System ◽  
Prediction Errors ◽  
Feedback Learning ◽  
Probabilistic Reinforcement ◽  
Additional Mode ◽  
Brain Correlates ◽  
The Brain

Simple probabilistic reinforcement learning is recognized as a striatum-based learning system, but in recent years, has also been associated with hippocampal involvement. The present study examined whether such involvement may be attributed to observation-based learning processes, running in parallel to striatum-based reinforcement learning. A computational model of observation-based learning (OL), mirroring classic models of reinforcement-based learning (RL), was constructed and applied to the neuroimaging dataset of Palombo, Hayes, Reid, & Verfaellie (2019). Hippocampal contributions to value-based learning: Converging evidence from fMRI and amnesia. Cognitive, Affective & Behavioral Neuroscience, 19(3), 523–536. Results suggested that observation-based learning processes may indeed take place concomitantly to reinforcement learning and involve activation of the hippocampus and central orbitofrontal cortex (cOFC). However, rather than independent mechanisms running in parallel, the brain correlates of the OL and RL prediction errors indicated collaboration between systems, with direct implication of the hippocampus in computations of the discrepancy between the expected and actual reinforcing values of actions. These findings are consistent with previous accounts of a role for the hippocampus in encoding the strength of observed stimulus-outcome associations, with updating of such associations through striatal reinforcement-based computations. Additionally, enhanced negative prediction error signaling was found in the anterior insula with greater use of OL over RL processes. This result may suggest an additional mode of collaboration between OL and RL systems, implicating the error monitoring network.

scholarly journals Monopolistic Models for Resource Allocation: A Probabilistic Reinforcement Learning Approach

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 49721-49731 ◽  
Author(s):  
Yue Zhang ◽  
Bin Song ◽  
Su Gao ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Learning Approach ◽  
Probabilistic Reinforcement

scholarly journals The Role of Dopaminergic Genes in Probabilistic Reinforcement Learning in Schizophrenia Spectrum Disorders

2021 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Dorota Frydecka ◽  
Błażej Misiak ◽  
Patryk Piotrowski ◽  
Tomasz Bielawski ◽  
Edyta Pawlak ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Genetic Polymorphisms ◽  
Schizophrenia Spectrum Disorders ◽  
Schizophrenia Spectrum ◽  
Probabilistic Reinforcement ◽  
Healthy Control ◽  
Spectrum Disorders ◽  
The Impact ◽  
Dopaminergic Genes ◽  
Allele Repeat

Schizophrenia spectrum disorders (SZ) are characterized by impairments in probabilistic reinforcement learning (RL), which is associated with dopaminergic circuitry encompassing the prefrontal cortex and basal ganglia. However, there are no studies examining dopaminergic genes with respect to probabilistic RL in SZ. Thus, the aim of our study was to examine the impact of dopaminergic genes on performance assessed by the Probabilistic Selection Task (PST) in patients with SZ in comparison to healthy control (HC) subjects. In our study, we included 138 SZ patients and 188 HC participants. Genetic analysis was performed with respect to the following genetic polymorphisms: rs4680 in COMT, rs907094 in DARP-32, rs2734839, rs936461, rs1800497, and rs6277 in DRD2, rs747302 and rs1800955 in DRD4 and rs28363170 and rs2975226 in DAT1 genes. The probabilistic RL task was completed by 59 SZ patients and 95 HC subjects. SZ patients performed significantly worse in acquiring reinforcement contingencies during the task in comparison to HCs. We found no significant association between genetic polymorphisms and RL among SZ patients; however, among HC participants with respect to the DAT1 rs28363170 polymorphism, individuals with 10-allele repeat genotypes performed better in comparison to 9-allele repeat carriers. The present study indicates the relevance of the DAT1 rs28363170 polymorphism in RL in HC participants.

scholarly journals Increased ventromedial prefrontal cortex activity in adolescence benefits prosocial reinforcement learning

2021 ◽  
Author(s):  
Bianca Westhoff ◽  
Neeltje E. Blankenstein ◽  
Elisabeth Schreuders ◽  
Eveline A. Crone ◽  
Anna C. K. van Duijvenvoorde
Keyword(s):  
Prefrontal Cortex ◽  
Reinforcement Learning ◽  
Functional Neuroimaging ◽  
Learning Task ◽  
Developmental Trajectory ◽  
Ventromedial Prefrontal Cortex ◽  
Prediction Errors ◽  
Age Related ◽  
Probabilistic Reinforcement ◽  
Neuroimaging Study

AbstractLearning which of our behaviors benefit others contributes to social bonding and being liked by others. An important period for the development of (pro)social behavior is adolescence, in which peers become more salient and relationships intensify. It is, however, unknown how learning to benefit others develops across adolescence and what the underlying cognitive and neural mechanisms are. In this functional neuroimaging study, we assessed learning for self and others (i.e., prosocial learning) and the concurring neural tracking of prediction errors across adolescence (ages 9-21, N=74). Participants performed a two-choice probabilistic reinforcement learning task in which outcomes resulted in monetary consequences for themselves, an unknown other, or no one. Participants from all ages were able to learn for themselves and others, but learning for others showed a more protracted developmental trajectory. Prediction errors for self were observed in the ventral striatum and showed no age-related differences. However, prediction error coding for others was specifically observed in the ventromedial prefrontal cortex and showed age-related increases. These results reveal insights into the computational mechanisms of learning for others across adolescence, and highlight that learning for self and others show different age-related patterns.

Aberrant probabilistic reinforcement learning in first-degree relatives of individuals with bipolar disorder

2020 ◽  
Vol 264 ◽  
pp. 400-406
Author(s):  
Julia O. Linke ◽  
Georgia Koppe ◽  
Vanessa Scholz ◽  
Philipp Kanske ◽  
Daniel Durstewitz ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Reinforcement Learning ◽  
First Degree Relatives ◽  
Probabilistic Reinforcement

scholarly journals Life Span Differences in Electrophysiological Correlates of Monitoring Gains and Losses during Probabilistic Reinforcement Learning

2011 ◽  
Vol 23 (3) ◽  
pp. 579-592 ◽  
Author(s):  
Dorothea Hämmerer ◽  
Shu-Chen Li ◽  
Viktor Müller ◽  
Ulman Lindenberger
Keyword(s):  
Older Adults ◽  
Reinforcement Learning ◽  
Life Span ◽  
Ontogenetic Changes ◽  
Younger Adults ◽  
Probabilistic Reinforcement ◽  
Outcome Monitoring ◽  
The Difference ◽  
Gains And Losses

By recording the feedback-related negativity (FRN) in response to gains and losses, we investigated the contribution of outcome monitoring mechanisms to age-associated differences in probabilistic reinforcement learning. Specifically, we assessed the difference of the monitoring reactions to gains and losses to investigate the monitoring of outcomes according to task-specific goals across the life span. The FRN and the behavioral indicators of learning were measured in a sample of 44 children, 45 adolescents, 46 younger adults, and 44 older adults. The amplitude of the FRN after gains and losses was found to decrease monotonically from childhood to old age. Furthermore, relative to adolescents and younger adults, both children and older adults (a) showed smaller differences between the FRN after losses and the FRN after gains, indicating a less differentiated classification of outcomes on the basis of task-specific goals; (b) needed more trials to learn from choice outcomes, particularly when differences in reward likelihood between the choices were small; and (c) learned less from gains than from losses. We suggest that the relatively greater loss sensitivity among children and older adults may reflect ontogenetic changes in dopaminergic neuromodulation.

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