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Frontal theta as a mechanism for cognitive control

Trends in cognitive sciences, 2014-08, Vol.18 (8), p.414-421

2014

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An Integrative Perspective on the Role of Dopamine in Schizophrenia

Biological psychiatry (1969), 2017-01, Vol.81 (1), p.52-66

2017

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Computational psychiatry as a bridge from neuroscience to clinical applications

Nature neuroscience, 2016-03, Vol.19 (3), p.404

2016

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Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making

Neural networks, 2006-10, Vol.19 (8), p.1120-1136

Frank, Michael J.

2006

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Computational models of motivated action selection in corticostriatal circuits

Current opinion in neurobiology, 2011-06, Vol.21 (3), p.381-386

Frank, Michael J

2011

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From reinforcement learning models to psychiatric and neurological disorders

Nature neuroscience, 2011-02, Vol.14 (2), p.154-162

2011

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Mechanisms of Hierarchical Reinforcement Learning in Corticostriatal Circuits 1: Computational Analysis

Cerebral cortex (New York, N.Y. 1991), 2012-03, Vol.22 (3), p.509-526

2012

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A Computational Model of Inhibitory Control in Frontal Cortex and Basal Ganglia

Psychological review, 2013-04, Vol.120 (2), p.329-355

2013

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Corticostriatal output gating during selection from working memory

Neuron (Cambridge, Mass.), 2014-02, Vol.81 (4), p.930-942

2014

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Advances in the computational understanding of mental illness

Neuropsychopharmacology (New York, N.Y.), 2021-01, Vol.46 (1), p.3-19

2021

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Rostrolateral Prefrontal Cortex and Individual Differences in Uncertainty-Driven Exploration

Neuron (Cambridge, Mass.), 2012-02, Vol.73 (3), p.595-607

2012

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HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python

Frontiers in neuroinformatics, 2013-01, Vol.7, p.14-14

2013

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Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies

PLoS computational biology, 2019-06, Vol.15 (6), p.e1007043-e1007043

2019

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Reinforcement-Based Decision Making in Corticostriatal Circuits: Mutual Constraints by Neurocomputational and Diffusion Models

Neural computation, 2012-05, Vol.24 (5), p.1186-1229

2012

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Reward-predictive representations generalize across tasks in reinforcement learning

PLoS computational biology, 2020-10, Vol.16 (10), p.e1008317-e1008317

2020

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Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory

Proceedings of the National Academy of Sciences - PNAS, 2018-03, Vol.115 (10), p.2502-2507

2018

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Cognitive Control Over Learning: Creating, Clustering, and Generalizing Task-Set Structure

Psychological review, 2013-01, Vol.120 (1), p.190-229

2013

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Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism

Journal of cognitive neuroscience, 2005-01, Vol.17 (1), p.51-72

Frank, Michael J.

2005

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Opponent Actor Learning (OpAL): Modeling Interactive Effects of Striatal Dopamine on Reinforcement Learning and Choice Incentive

Psychological review, 2014-07, Vol.121 (3), p.337-366

2014

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Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence

PLoS computational biology, 2021-06, Vol.17 (6), p.e1008971-e1008971

2021

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Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold

Nature neuroscience, 2011-11, Vol.14 (11), p.1462-1467

2011

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Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

PLoS computational biology, 2022-02, Vol.18 (2), p.e1009854-e1009854

2022

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Motor symptoms in Parkinson's disease: A unified framework

Neuroscience and biobehavioral reviews, 2016-09, Vol.68, p.727-740

2016

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Common medial frontal mechanisms of adaptive control in humans and rodents

Nature neuroscience, 2013-12, Vol.16 (12), p.1888-1895

2013

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On the normative advantages of dopamine and striatal opponency for learning and choice

eLife, 2023-03, Vol.12

2023

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Frontal theta as a mechanism for cognitive control

An Integrative Perspective on the Role of Dopamine in Schizophrenia

Computational psychiatry as a bridge from neuroscience to clinical applications

Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making

Computational models of motivated action selection in corticostriatal circuits

From reinforcement learning models to psychiatric and neurological disorders

Mechanisms of Hierarchical Reinforcement Learning in Corticostriatal Circuits 1: Computational Analysis

A Computational Model of Inhibitory Control in Frontal Cortex and Basal Ganglia

Corticostriatal output gating during selection from working memory

Advances in the computational understanding of mental illness

Rostrolateral Prefrontal Cortex and Individual Differences in Uncertainty-Driven Exploration

HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies

Reinforcement-Based Decision Making in Corticostriatal Circuits: Mutual Constraints by Neurocomputational and Diffusion Models

Reward-predictive representations generalize across tasks in reinforcement learning

Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory

Cognitive Control Over Learning: Creating, Clustering, and Generalizing Task-Set Structure

Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism

Opponent Actor Learning (OpAL): Modeling Interactive Effects of Striatal Dopamine on Reinforcement Learning and Choice Incentive

Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence

Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Motor symptoms in Parkinson's disease: A unified framework

Common medial frontal mechanisms of adaptive control in humans and rodents

On the normative advantages of dopamine and striatal opponency for learning and choice

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