Literature DB >> 18678251

Axiomatic methods, dopamine and reward prediction error.

Andrew Caplin1, Mark Dean.   

Abstract

The phasic firing rate of midbrain dopamine neurons has been shown to respond both to the receipt of rewarding stimuli, and the degree to which such stimuli are anticipated by the recipient. This has led to the hypothesis that these neurons encode reward prediction error (RPE)-the difference between how rewarding an event is, and how rewarding it was expected to be. However, the RPE model is one of a number of competing explanations for dopamine activity that have proved hard to disentangle, mainly because they are couched in terms of latent, or unobservable, variables. This article describes techniques for dealing with latent variables common in economics and decision theory, and reviews work that uses these techniques to provide simple, non-parametric tests of the RPE hypothesis, allowing clear differentiation between competing explanations.

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Year:  2008        PMID: 18678251     DOI: 10.1016/j.conb.2008.07.007

Source DB:  PubMed          Journal:  Curr Opin Neurobiol        ISSN: 0959-4388            Impact factor:   6.627


  16 in total

1.  Testing the reward prediction error hypothesis with an axiomatic model.

Authors:  Robb B Rutledge; Mark Dean; Andrew Caplin; Paul W Glimcher
Journal:  J Neurosci       Date:  2010-10-06       Impact factor: 6.167

2.  Frontal theta reflects uncertainty and unexpectedness during exploration and exploitation.

Authors:  James F Cavanagh; Christina M Figueroa; Michael X Cohen; Michael J Frank
Journal:  Cereb Cortex       Date:  2011-11-25       Impact factor: 5.357

Review 3.  Motivational Deficits in Schizophrenia and the Representation of Expected Value.

Authors:  James A Waltz; James M Gold
Journal:  Curr Top Behav Neurosci       Date:  2016

4.  Cannabinoids and value-based decision making: implications for neurodegenerative disorders.

Authors:  Angela M Lee; Erik B Oleson; Leontien Diergaarde; Joseph F Cheer; Tommy Pattij
Journal:  Basal Ganglia       Date:  2012-07-28

5.  Abnormal approach-related motivation but spared reinforcement learning in MDD: Evidence from fronto-midline Theta oscillations and frontal Alpha asymmetry.

Authors:  Davide Gheza; Jasmina Bakic; Chris Baeken; Rudi De Raedt; Gilles Pourtois
Journal:  Cogn Affect Behav Neurosci       Date:  2019-06       Impact factor: 3.282

6.  Differential modulation of reinforcement learning by D2 dopamine and NMDA glutamate receptor antagonism.

Authors:  Gerhard Jocham; Tilmann A Klein; Markus Ullsperger
Journal:  J Neurosci       Date:  2014-09-24       Impact factor: 6.167

7.  Taming the beast: extracting generalizable knowledge from computational models of cognition.

Authors:  Matthew R Nassar; Michael J Frank
Journal:  Curr Opin Behav Sci       Date:  2016-10

8.  Frontal theta links prediction errors to behavioral adaptation in reinforcement learning.

Authors:  James F Cavanagh; Michael J Frank; Theresa J Klein; John J B Allen
Journal:  Neuroimage       Date:  2009-12-05       Impact factor: 6.556

Review 9.  Frontal theta as a mechanism for cognitive control.

Authors:  James F Cavanagh; Michael J Frank
Journal:  Trends Cogn Sci       Date:  2014-05-15       Impact factor: 20.229

10.  Humans primarily use model-based inference in the two-stage task.

Authors:  Carolina Feher da Silva; Todd A Hare
Journal:  Nat Hum Behav       Date:  2020-07-06
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