Literature DB >> 32601244

Reinforcement Learning during Adolescence in Rats.

Neema Moin Afshar1, Alex J Keip1, Jane R Taylor1,2, Daeyeol Lee3,4,5,6, Stephanie M Groman7.   

Abstract

The most dynamic period of postnatal brain development occurs during adolescence, the period between childhood and adulthood. Neuroimaging studies have observed morphologic and functional changes during adolescence, and it is believed that these changes serve to improve the functions of circuits that underlie decision-making. Direct evidence in support of this hypothesis, however, has been limited because most preclinical decision-making paradigms are not readily translated to humans. Here, we developed a reversal-learning protocol for the rapid assessment of adaptive choice behavior in dynamic environments in rats as young as postnatal day 30. A computational framework was used to elucidate the reinforcement-learning mechanisms that change in adolescence and into adulthood. Using a cross-sectional and longitudinal design, we provide the first evidence that value-based choice behavior in a reversal-learning task improves during adolescence in male and female Long-Evans rats and demonstrate that the increase in reversal performance is due to alterations in value updating for positive outcomes. Furthermore, we report that reversal-learning trajectories in adolescence reliably predicted reversal performance in adulthood. This novel behavioral protocol provides a unique platform for conducting biological and systems-level analyses of the neurodevelopmental mechanisms of decision-making.SIGNIFICANCE STATEMENT The neurodevelopmental adaptations that occur during adolescence are hypothesized to underlie age-related improvements in decision-making, but evidence to support this hypothesis has been limited. Here, we describe a novel behavioral protocol for rapidly assessing adaptive choice behavior in adolescent rats with a reversal-learning paradigm. Using a computational approach, we demonstrate that age-related changes in reversal-learning performance in male and female Long-Evans rats are linked to specific reinforcement-learning mechanisms and are predictive of reversal-learning performance in adulthood. Our behavioral protocol provides a unique platform for elucidating key components of adolescent brain function.
Copyright © 2020 the authors.

Entities:  

Keywords:  computational psychiatry; meta-learning; neurodevelopment; reversal learning; reward

Year:  2020        PMID: 32601244      PMCID: PMC7380962          DOI: 10.1523/JNEUROSCI.0910-20.2020

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


  79 in total

1.  Dopamine receptor pruning in prefrontal cortex during the periadolescent period in rats.

Authors:  S L Andersen; A T Thompson; M Rutstein; J C Hostetter; M H Teicher
Journal:  Synapse       Date:  2000-08       Impact factor: 2.562

2.  Striatum-medial prefrontal cortex connectivity predicts developmental changes in reinforcement learning.

Authors:  Wouter van den Bos; Michael X Cohen; Thorsten Kahnt; Eveline A Crone
Journal:  Cereb Cortex       Date:  2011-08-04       Impact factor: 5.357

3.  An Upside to Reward Sensitivity: The Hippocampus Supports Enhanced Reinforcement Learning in Adolescence.

Authors:  Juliet Y Davidow; Karin Foerde; Adriana Galván; Daphna Shohamy
Journal:  Neuron       Date:  2016-10-05       Impact factor: 17.173

Review 4.  Adolescent neurodevelopment.

Authors:  Linda Patia Spear
Journal:  J Adolesc Health       Date:  2013-02       Impact factor: 5.012

5.  From Creatures of Habit to Goal-Directed Learners: Tracking the Developmental Emergence of Model-Based Reinforcement Learning.

Authors:  Johannes H Decker; A Ross Otto; Nathaniel D Daw; Catherine A Hartley
Journal:  Psychol Sci       Date:  2016-04-15

6.  By carrot or by stick: cognitive reinforcement learning in parkinsonism.

Authors:  Michael J Frank; Lauren C Seeberger; Randall C O'reilly
Journal:  Science       Date:  2004-11-04       Impact factor: 47.728

7.  Volatility Facilitates Value Updating in the Prefrontal Cortex.

Authors:  Bart Massi; Christopher H Donahue; Daeyeol Lee
Journal:  Neuron       Date:  2018-07-19       Impact factor: 17.173

8.  Adolescents adapt more slowly than adults to varying reward contingencies.

Authors:  Amir Homayoun Javadi; Dirk H K Schmidt; Michael N Smolka
Journal:  J Cogn Neurosci       Date:  2014-06-24       Impact factor: 3.225

Review 9.  Pubertal development and behavior: hormonal activation of social and motivational tendencies.

Authors:  Erika E Forbes; Ronald E Dahl
Journal:  Brain Cogn       Date:  2009-11-25       Impact factor: 2.310

10.  Chronic cocaine but not chronic amphetamine use is associated with perseverative responding in humans.

Authors:  Karen D Ersche; Jonathan P Roiser; Trevor W Robbins; Barbara J Sahakian
Journal:  Psychopharmacology (Berl)       Date:  2008-01-24       Impact factor: 4.530

View more
  4 in total

Review 1.  Reinforcement learning detuned in addiction: integrative and translational approaches.

Authors:  Stephanie M Groman; Summer L Thompson; Daeyeol Lee; Jane R Taylor
Journal:  Trends Neurosci       Date:  2021-12-15       Impact factor: 13.837

2.  Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning.

Authors:  Bruno B Averbeck
Journal:  Proc Natl Acad Sci U S A       Date:  2022-05-27       Impact factor: 12.779

Review 3.  Persistent behavioral and neurobiological consequences of social isolation during adolescence.

Authors:  Dan C Li; Elizabeth A Hinton; Shannon L Gourley
Journal:  Semin Cell Dev Biol       Date:  2021-06-08       Impact factor: 7.499

Review 4.  Linking mPFC circuit maturation to the developmental regulation of emotional memory and cognitive flexibility.

Authors:  Cassandra B Klune; Benita Jin; Laura A DeNardo
Journal:  Elife       Date:  2021-05-05       Impact factor: 8.713

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.