Literature DB >> 26511241

Hemispheric Asymmetries in Striatal Reward Responses Relate to Approach-Avoidance Learning and Encoding of Positive-Negative Prediction Errors in Dopaminergic Midbrain Regions.

Kristoffer Carl Aberg1, Kimberly C Doell2, Sophie Schwartz2.   

Abstract

Some individuals are better at learning about rewarding situations, whereas others are inclined to avoid punishments (i.e., enhanced approach or avoidance learning, respectively). In reinforcement learning, action values are increased when outcomes are better than predicted (positive prediction errors [PEs]) and decreased for worse than predicted outcomes (negative PEs). Because actions with high and low values are approached and avoided, respectively, individual differences in the neural encoding of PEs may influence the balance between approach-avoidance learning. Recent correlational approaches also indicate that biases in approach-avoidance learning involve hemispheric asymmetries in dopamine function. However, the computational and neural mechanisms underpinning such learning biases remain unknown. Here we assessed hemispheric reward asymmetry in striatal activity in 34 human participants who performed a task involving rewards and punishments. We show that the relative difference in reward response between hemispheres relates to individual biases in approach-avoidance learning. Moreover, using a computational modeling approach, we demonstrate that better encoding of positive (vs negative) PEs in dopaminergic midbrain regions is associated with better approach (vs avoidance) learning, specifically in participants with larger reward responses in the left (vs right) ventral striatum. Thus, individual dispositions or traits may be determined by neural processes acting to constrain learning about specific aspects of the world.
Copyright © 2015 the authors 0270-6474/15/3514491-10$15.00/0.

Entities:  

Keywords:  approach; asymmetry; avoidance; dopamine; reinforcement; reward

Mesh:

Year:  2015        PMID: 26511241      PMCID: PMC6605462          DOI: 10.1523/JNEUROSCI.1859-15.2015

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


  13 in total

1.  Left-shifting prism adaptation boosts reward-based learning.

Authors:  Selene Schintu; Michael Freedberg; Zaynah M Alam; Sarah Shomstein; Eric M Wassermann
Journal:  Cortex       Date:  2018-10-12       Impact factor: 4.027

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

Authors:  Snežana Urošević; Tate Halverson; Eric A Youngstrom; Monica Luciana
Journal:  J Abnorm Psychol       Date:  2018-11

Review 3.  Driven by Pain, Not Gain: Computational Approaches to Aversion-Related Decision Making in Psychiatry.

Authors:  Martin P Paulus
Journal:  Biol Psychiatry       Date:  2019-09-09       Impact factor: 13.382

4.  Reward and fictive prediction error signals in ventral striatum: asymmetry between factual and counterfactual processing.

Authors:  E Pomarol-Clotet; J Radua; A Santo-Angles; P Fuentes-Claramonte; I Argila-Plaza; M Guardiola-Ripoll; C Almodóvar-Payá; J Munuera; P J McKenna
Journal:  Brain Struct Funct       Date:  2021-04-11       Impact factor: 3.270

5.  Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning.

Authors:  Kristoffer Carl Aberg; Kimberly C Doell; Sophie Schwartz
Journal:  PLoS One       Date:  2016-11-16       Impact factor: 3.240

6.  Effects of dopamine on reinforcement learning and consolidation in Parkinson's disease.

Authors:  John P Grogan; Demitra Tsivos; Laura Smith; Brogan E Knight; Rafal Bogacz; Alan Whone; Elizabeth J Coulthard
Journal:  Elife       Date:  2017-07-10       Impact factor: 8.140

7.  Trial-by-Trial Modulation of Associative Memory Formation by Reward Prediction Error and Reward Anticipation as Revealed by a Biologically Plausible Computational Model.

Authors:  Kristoffer C Aberg; Julia Müller; Sophie Schwartz
Journal:  Front Hum Neurosci       Date:  2017-02-15       Impact factor: 3.169

8.  Financial gain- and loss-related BOLD signals in the human ventral tegmental area and substantia nigra pars compacta.

Authors:  Eve H Limbrick-Oldfield; Robert Leech; Richard J S Wise; Mark A Ungless
Journal:  Eur J Neurosci       Date:  2018-12-13       Impact factor: 3.386

9.  Distinct neural processes support post-success and post-error slowing in the stop signal task.

Authors:  Yihe Zhang; Jaime S Ide; Sheng Zhang; Sien Hu; Nikola S Valchev; Xiaoying Tang; Chiang-Shan R Li
Journal:  Neuroscience       Date:  2017-06-13       Impact factor: 3.590

10.  Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach.

Authors:  Tsutomu Harada
Journal:  PLoS One       Date:  2021-06-17       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.