Cargando…

Neural computations underlying inverse reinforcement learning in the human brain

In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while...

Descripción completa

Detalles Bibliográficos
Autores principales:	Collette, Sven, Pauli, Wolfgang M, Bossaerts, Peter, O'Doherty, John
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	eLife Sciences Publications, Ltd 2017
Materias:	Neuroscience
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662289/ https://www.ncbi.nlm.nih.gov/pubmed/29083301 http://dx.doi.org/10.7554/eLife.29718

Ejemplares similares

Evidence for model-based encoding of Pavlovian contingencies in the human brain
por: Pauli, Wolfgang M., et al.
Publicado: (2019)

Neural Computations Mediating One-Shot Learning in the Human Brain
por: Lee, Sang Wan, et al.
Publicado: (2015)

In the Mind of the Market: Theory of Mind Biases Value Computation during Financial Bubbles
por: De Martino, Benedetto, et al.
Publicado: (2013)

In the Mind of the Market: Theory of Mind Biases Value Computation during Financial Bubbles
por: De Martino, Benedetto, et al.
Publicado: (2013)

Evidence for Model-based Computations in the Human Amygdala during Pavlovian Conditioning
por: Prévost, Charlotte, et al.
Publicado: (2013)

Is Avoiding an Aversive Outcome Rewarding? Neural Substrates of Avoidance Learning in the Human Brain
por: Kim, Hackjin, et al.
Publicado: (2006)

Computing Generalized Matrix Inverse on Spiking Neural Substrate
por: Shukla, Rohit, et al.
Publicado: (2018)

The human prefrontal cortex mediates integration of potential causes behind observed outcomes
por: Wunderlich, Klaus, et al.
Publicado: (2011)

Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism
por: Marković, Dimitrije, et al.
Publicado: (2015)

The Cerebellum: A Neural System for the Study of Reinforcement Learning
por: Swain, Rodney A., et al.
Publicado: (2011)

Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation
por: Bauer, Robert, et al.
Publicado: (2015)

Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI
por: Colas, Jaron T., et al.
Publicado: (2017)

The Behavioral and Neural Mechanisms Underlying the Tracking of Expertise
por: Boorman, Erie D., et al.
Publicado: (2013)

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review
por: Girdler, Benton, et al.
Publicado: (2022)

Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks
por: Weidel, Philipp, et al.
Publicado: (2021)

Reinforcement Learning With Parsimonious Computation and a Forgetting Process
por: Toyama, Asako, et al.
Publicado: (2019)

Neural Systems Underlying Aversive Conditioning in Humans with Primary and Secondary Reinforcers
por: Delgado, Mauricio R., et al.
Publicado: (2011)

Behavioural evidence for parallel outcome-sensitive and outcome-insensitive Pavlovian learning systems in humans
por: Pool, Eva R., et al.
Publicado: (2019)

The Path Planning of Mobile Robot by Neural Networks and Hierarchical Reinforcement Learning
por: Yu, Jinglun, et al.
Publicado: (2020)

Reinforcement Learning for Central Pattern Generation in Dynamical Recurrent Neural Networks
por: Yoder, Jason A., et al.
Publicado: (2022)

Formalizing the Function of Anterior Insula in Rapid Adaptation
por: Bossaerts, Peter
Publicado: (2018)

Dimensional Reduction for the Inverse Problem of Neural Field Theory
por: Potthast, Roland, et al.
Publicado: (2009)

Training Spiking Neural Networks for Reinforcement Learning Tasks With Temporal Coding Method
por: Wu, Guanlin, et al.
Publicado: (2022)

Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite
por: Uchida, Yuuki, et al.
Publicado: (2022)

Reappraisal of incentives ameliorates choking under pressure and is correlated with changes in the neural representations of incentives
por: Dunne, Simon, et al.
Publicado: (2018)

Deep Reinforcement Learning Based Trajectory Planning Under Uncertain Constraints
por: Chen, Lienhung, et al.
Publicado: (2022)

Neural Mechanisms Underlying Paradoxical Performance for Monetary Incentives Are Driven by Loss Aversion
por: Chib, Vikram S., et al.
Publicado: (2012)

Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
por: Detorakis, Georgios, et al.
Publicado: (2018)

Neural computations underlying social risk sensitivity
por: Lauharatanahirun, Nina, et al.
Publicado: (2012)

A Brain-Machine Interface Instructed by Direct Intracortical Microstimulation
por: O'Doherty, Joseph E., et al.
Publicado: (2009)

The Functional Role of Striatal Cholinergic Interneurons in Reinforcement Learning From Computational Perspective
por: Kim, Taegyo, et al.
Publicado: (2019)

Computational Characteristics of the Striatal Dopamine System Described by Reinforcement Learning With Fast Generalization
por: Fujita, Yoshihisa, et al.
Publicado: (2020)

Positive Temporal Dependence of the Biological Clock Implies Hyperbolic Discounting
por: Ray, Debajyoti, et al.
Publicado: (2011)

Human Neural Learning Depends on Reward Prediction Errors in the Blocking Paradigm
por: Tobler, Philippe N., et al.
Publicado: (2006)

InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling
por: Nasr, Ali, et al.
Publicado: (2021)

Machine Teaching for Human Inverse Reinforcement Learning
por: Lee, Michael S., et al.
Publicado: (2021)

Neural mechanisms underlying the hierarchical construction of perceived aesthetic value
por: Iigaya, Kiyohito, et al.
Publicado: (2023)

Embodied Synaptic Plasticity With Online Reinforcement Learning
por: Kaiser, Jacques, et al.
Publicado: (2019)

The Face Inversion Effect in Deep Convolutional Neural Networks
por: Tian, Fang, et al.
Publicado: (2022)

Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance
por: Yamamori, Yumeya, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_88093roegqh2rvs7a4en6d42bp