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Prediction Error in Reinforcement Learning : A Meta-analysis of Neuroimaging studies

Jane R. Garrison, +2 more
- 01 Aug 2013 - 
- Vol. 37, Iss: 7, pp 1297-1310
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TLDR
Activation likelihood estimation (ALE) meta-analyses were used to examine the neural correlates of prediction error in reinforcement learning, finding that reward prediction errors were observed primarily in the striatum while aversive predictionerrors were found more widely including insula and habenula.
About: 
This article is published in Neuroscience & Biobehavioral Reviews.The article was published on 2013-08-01 and is currently open access. It has received 362 citations till now. The article focuses on the topics: Reward system & Anterior cingulate cortex.

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The basal ganglia and the cerebellum: nodes in an integrated network

Andreea C. Bostan, +1 more
- 01 Jun 2018 - 
TL;DR: Findings indicating that these subcortical areas are in fact interconnected and, along with the cerebral cortex, form an integrated network are discussed.
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The Striatum: Where Skills and Habits Meet

Ann M. Graybiel, +1 more
- 01 Aug 2015 - 
TL;DR: Evidence supporting the role of the striatum in optimizing behavior by refining action selection and in shaping habits and skills as a modulator of motor repertoires is reviewed, challenging the notion that striatal learning processes are limited to the motor domain.
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A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages.

Thomas D. Sambrook, +1 more
- 01 Jan 2015 - 
TL;DR: A meta-analysis of the FRN's response to both reward magnitude and likelihood was conducted using a novel method in which published effect sizes were disregarded in favor of direct measurement of the published waveforms themselves, with these waveforms then averaged to produce "great grand averages."
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A simple solution for model comparison in bold imaging: the special case of reward prediction error and reward outcomes

Burak Erdeniz, +3 more
- 19 Jul 2013 - 
TL;DR: In this article, the authors discuss the special case of separating the reward prediction error signal from reward outcomes and discuss methodological approaches to analyzing such data by discussing the multicollinearity problem in statistical analysis.
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Anterior cingulate engagement in a foraging context reflects choice difficulty, not foraging value.

Amitai Shenhav, +3 more
- 01 Sep 2014 - 
TL;DR: Two neuroimaging experiments help to formalize a fundamental connection between choice difficulty and foraging-like decisions, while also prescribing a solution for a common pitfall in studies of reward-based decision making.
References
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Philosophical transactions of the Royal Society of London. B

HighWire Press
Journal ArticleDOI

A Neural Substrate of Prediction and Reward

Wolfram Schultz, +2 more
- 14 Mar 1997 - 
TL;DR: Findings in this work indicate that dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events can be understood through quantitative theories of adaptive optimizing control.
Journal ArticleDOI

Reinforcement learning: a survey

Leslie Pack Kaelbling, +2 more
- 01 Jan 1996 - 
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.

A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement

Robert A. Rescorla
Posted Content

Reinforcement Learning: A Survey

Leslie Pack Kaelbling, +2 more
- 01 May 1996 - 
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
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The reward circuit: linking primate anatomy and human imaging.

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Frequently Asked Questions (1)
Q1. What are the contributions in this paper?

S from the above 779 articles were reviewed. An intermediate shortlist of 109 articles of interest was then constructed, which were studied in detail. Inclusion criteria were as follows: ( 1 ) primary research studies using human adult participants ; ( 2 ) prediction error calculated using Rescorla Wagner or TD models, or from models derived from either of these ; ( 3 ) coordinates of prediction error provided in Montreal Neurological Institute ( MNI ) or Talairach standard stereotactic space ; ( 4 ) the study involved use of an experimental reinforcement learning task providing subject feedback ( n. b. studies were excluded where tasks involved the simple probabilistic allocation of reward or punishment such as monetary incentive delay tasks ).