Model-based predictions for dopamine.
TLDR
A number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value.About:
This article is published in Current Opinion in Neurobiology.The article was published on 2018-04-01 and is currently open access. It has received 112 citations till now.read more
Citations
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The Neuroscience of Drug Reward and Addiction.
TL;DR: Treatment interventions intended to reverse neuroadaptations that result in an impaired prefrontal top-down self-regulation that favors compulsive drug-taking against the backdrop of negative emotionality and an enhanced interoceptive awareness of "drug hunger" show promise as therapeutic approaches for addiction.
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Hallucinations and Strong Priors
Philip R. Corlett,Guillermo Horga,Paul C. Fletcher,Paul C. Fletcher,Ben Alderson-Day,Katharina Schmack,Albert R. Powers +6 more
TL;DR: This work underlines the continuum from normal to aberrant perception, encouraging a more empathic approach to clinical hallucinations, and highlights the role of prior beliefs as a critical elicitor of hallucinations.
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Learning task-state representations
TL;DR: Recent research into the computational and neural underpinnings of ‘representation learning’—how humans (and other animals) construct task representations that allow efficient learning and decision-making are summarized.
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Adaptive learning under expected and unexpected uncertainty
Alireza Soltani,Alicia Izquierdo +1 more
TL;DR: Computational models and experimental findings are examined to distil computational principles and neural mechanisms for adaptive learning under uncertainty and define these concepts, Soltani and Izquierdo and proposed models of how they may be computed and discuss their neural substrates.
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Habits without values.
TL;DR: It is suggested that mapping habitual behaviors onto value-free mechanisms provides a parsimonious account of existing behavioral and neural data and may provide a new foundation for building robust and comprehensive models of the interaction of habits with other, more goal-directed types of behaviors.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
A Neural Substrate of Prediction and Reward
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
Planning and Acting in Partially Observable Stochastic Domains
TL;DR: A novel algorithm for solving pomdps off line and how, in some cases, a finite-memory controller can be extracted from the solution to a POMDP is outlined.
Journal ArticleDOI
Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control
TL;DR: This work considers dual-action choice systems from a normative perspective, and suggests a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate.
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A framework for mesencephalic dopamine systems based on predictive Hebbian learning
TL;DR: A theoretical framework is developed that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations and shows that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.