M. Frank

发表

Banishing the homunculus: Making working memory work

Thomas E. Hazy, R. O’Reilly, T. E. Hazy, 2006, Neuroscience.

Anatomy of a decision: striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal.

M. Frank, E. Claus, 2006, Psychological review.

Cognitive control over learning: creating, clustering, and generalizing task-set structure.

Anne G E Collins, M. Frank, A. Collins, 2013, Psychological review.

Selective Reinforcement Learning Deficits in Schizophrenia Support Predictions from Computational Models of Striatal-Cortical Dysfunction

Benjamin M. Robinson, M. Frank, J. Waltz, 2007, Biological Psychiatry.

Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis.

M. Frank, David Badre, 2012, Cerebral cortex.

Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants

Denise M Werchan, Anne G E Collins, M. Frank, 2016, The Journal of Neuroscience.

8-Month-Old Infants Spontaneously Learn and Generalize Hierarchical Rules

Denise M Werchan, Anne G E Collins, M. Frank, 2015, Psychological science.

From reinforcement learning models to psychiatric and neurological disorders

M. Frank, T. Maia, 2011, Nature Neuroscience.

Frontal theta as a mechanism for cognitive control

M. Frank, J. Cavanagh, 2014, Trends in Cognitive Sciences.

Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation.

M. Frank, B. B. Doll, Jen Oas-Terpstra, 2009, Nature neuroscience.

Computational psychiatry as a bridge from neuroscience to clinical applications

M. Frank, Q. Huys, T. Maia, 2016, Nature Neuroscience.

Neurogenetics and Pharmacology of Learning, Motivation, and Cognition

M. Frank, J. Fossella, 2011, Neuropsychopharmacology.

Approach and avoidance learning in patients with major depression and healthy controls: relation to anhedonia

T. Robbins, E. Bullmore, M. Frank, 2009, Psychological Medicine.

Genetic contributions to avoidance-based decisions: striatal D2 receptor polymorphisms

M. Frank, K. Hutchison, 2009, Neuroscience.

A Reinforcement Learning Mechanism Responsible for the Valuation of Free Choice

Anne G E Collins, M. Frank, Jeffrey Cockburn, 2014, Neuron.

Striatal D1 and D2 signaling differentially predict learning from positive and negative outcomes

Sylvia M. L. Cox, M. Frank, A. Dagher, 2015, NeuroImage.

Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive.

Anne G E Collins, M. Frank, A. Collins, 2014, Psychological review.

Working Memory Contributions to Reinforcement Learning Impairments in Schizophrenia

Anne G E Collins, M. Frank, J. Waltz, 2014, The Journal of Neuroscience.

How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis

Anne G E Collins, M. Frank, A. Collins, 2012, The European journal of neuroscience.

Mechanisms of hierarchical reinforcement learning in cortico-striatal circuits 2: evidence from fMRI.

M. Frank, David Badre, 2012, Cerebral cortex.

Reward-predictive representations generalize across tasks in reinforcement learning

Michael L. Littman, Michael J. Frank, Lucas Lehnert, 2019, bioRxiv.

Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning

M. Frank, A. Collins, 2016, Cognition.

fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning

Thomas V. Wiecki, M. Frank, David Badre, 2015, The Journal of Neuroscience.

The drift diffusion model as the choice rule in reinforcement learning

M. Frank, Mads L Pedersen, G. Biele, 2017, Psychonomic bulletin & review.

Eye tracking and pupillometry are indicators of dissociable latent decision processes.

Thomas V. Wiecki, M. Frank, J. Cavanagh, 2014, Journal of experimental psychology. General.

Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning

N. Daw, M. Frank, B. B. Doll, 2016, The Journal of Neuroscience.

Frontal Theta Overrides Pavlovian Learning Biases

Ian W. Eisenberg, M. Frank, Q. Huys, 2013, The Journal of Neuroscience.

Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis.

Karl J. Friston, P. Dayan, M. Owen, 2016, The lancet. Psychiatry.

Dopaminergic Genes Predict Individual Differences in Susceptibility to Confirmation Bias

M. Frank, B. B. Doll, K. Hutchison, 2011, The Journal of Neuroscience.

Seeing is believing: Trustworthiness as a dynamic belief

Luke J. Chang, M. Frank, A. Sanfey, 2010, Cognitive Psychology.

Biases in the Explore–Exploit Tradeoff in Addictions: The Role of Avoidance of Uncertainty

M. Frank, V. Voon, N. Harrison, 2015, Neuropsychopharmacology.

Supplemental Figure

Nicole M. Long, M. Frank, David Badre, 2013 .

Acute stress selectively reduces reward sensitivity

M. Frank, D. Pizzagalli, R. Bogdan, 2013, Front. Hum. Neurosci..

Neural mechanisms of acquired phasic dopamine responses in learning

Thomas E. Hazy, R. O’Reilly, T. E. Hazy, 2010, Neuroscience & Biobehavioral Reviews.

Instructional control of reinforcement learning: A behavioral and neurocomputational investigation

M. Frank, A. Sanfey, B. B. Doll, 2009, Brain Research.

Neurocomputational models of motor and cognitive deficits in Parkinson's disease.

Thomas V. Wiecki, M. Frank, T. Wiecki, 2010, Progress in brain research.

Striatal Dopamine Predicts Outcome-Specific Reversal Learning and Its Sensitivity to Dopaminergic Drug Administration

M. Frank, M. D’Esposito, R. Cools, 2009, The Journal of Neuroscience.

Reduction of Pavlovian Bias in Schizophrenia: Enhanced Effects in Clozapine-Administered Patients

Michael J Frank, James M Gold, James F Cavanagh, 2016, PloS one.

Common medial frontal mechanisms of adaptive control in humans and rodents

James F. Cavanagh, Michael J. Frank, Mark Laubach, 2013, Nature Neuroscience.

Hypothetical decision making in schizophrenia: The role of expected value computation and “irrational” biases

M. Frank, R. McMahon, J. Waltz, 2013, Psychiatry Research.

A computational model of inhibitory control in frontal cortex and basal ganglia.

Thomas V. Wiecki, M. Frank, T. Wiecki, 2011, Psychological review.

Spontaneous eye blink rate predicts learning from negative, but not positive, outcomes

M. Frank, H. Slagter, K. Georgopoulou, 2015, Neuropsychologia.

Stress modulates reinforcement learning in younger and older adults.

Marissa A. Gorlick, M. Frank, M. Mather, 2013, Psychology and aging.

Running head : STRESS EFFECTS ON LEARNING IN AGING 1 Stress Modulates Reinforcement Learning in Younger and Older Adults

Marissa A. Gorlick, M. Frank, M. Mather, 2012 .

Pleasurable music affects reinforcement learning according to the listener

M. Frank, E. Brattico, B. Bogert, 2013, Front. Psychol..

Differential Effects of Psychotic Illness on Directed and Random Exploration

Robert C. Wilson, M. Frank, J. Waltz, 2020, Computational Psychiatry.

Cross-task contributions of fronto-basal ganglia circuitry in response inhibition and conflict-induced slowing

K. R. Ridderinkhof, Anne G E Collins, M. Frank, 2017, bioRxiv.

John J. B. Allen, M. Frank, J. Cavanagh, 2009, Neuropsychologia.

Frontal theta reflects uncertainty and unexpectedness during exploration and exploitation.

Michael X. Cohen, M. Frank, J. Cavanagh, 2012, Cerebral cortex.

A Computational Cognitive Biomarker for Early-Stage Huntington’s Disease

Thomas V. Wiecki, C. Kennard, M. Frank, 2016, PloS one.

Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry

Thomas V. Wiecki, M. Frank, T. Wiecki, 2015 .

Optimizing vs. Matching: Response Strategy in a Probabilistic Learning Task is associated with Negative Symptoms of Schizophrenia

M. Frank, J. Waltz, J. Gold, 2011, Schizophrenia Research.

Deficits in Positive Reinforcement Learning and Uncertainty-Driven Exploration Are Associated with Distinct Aspects of Negative Symptoms in Schizophrenia

M. Frank, J. Waltz, J. Gold, 2011, Biological Psychiatry.

Differential, but not opponent, effects of l-DOPA and citalopram on action learning with reward and punishment

P. Dayan, R. Dolan, M. Frank, 2013, Psychopharmacology.

Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work

M. Frank, R. Cools, A. Westbrook, 2020, Science.

An Integrative Perspective on the Role of Dopamine in Schizophrenia

M. Frank, T. Maia, 2017, Biological Psychiatry.

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

M. Frank, Rachel Rac-Lubashevsky, 2020, bioRxiv.

Corticostriatal Output Gating during Selection from Working Memory

Christopher H. Chatham, M. Frank, David Badre, 2014, Neuron.

Frontal network dynamics reflect neurocomputational mechanisms for reducing maladaptive biases in motivated action

Jessica I. Määttä, M. Frank, R. Cools, 2018, PLoS biology.

CNTRICS Long-Term Memory Tasks 1 Abstract Word Count : 163 Text Body Word Count : 8 , 910 CNTRICS Final Task Selection : Long-term Memory

M. Frank, J. Ragland, A. Wagner, 2010 .

Reinforcement learning and higher level cognition: Introduction to special issue

N. Daw, M. Frank, 2009, Cognition.

CNTRICS final task selection: long-term memory.

M. Frank, J. Ragland, A. Wagner, 2009, Schizophrenia bulletin.

Anxiety Impedes Adaptive Social Learning Under Uncertainty

M. Frank, Oriel FeldmanHall, A. Lamba, 2020, Psychological science.

A control theoretic model of adaptive behavior in dynamic environments

M. Frank, M. Nassar, A. Shenhav, 2017, bioRxiv.

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

Anne G E Collins, M. Frank, A. Collins, 2017 .

Computational models of motivated action selection in corticostriatal circuits

M. Frank, 2011, Current Opinion in Neurobiology.

Probabilistic reinforcement learning in adults with autism spectrum disorders

M. Frank, C. Carter, Anne C. Smith, 2011, Autism research : official journal of the International Society for Autism Research.

Transitive inference in adults with autism spectrum disorders

M. Frank, C. Carter, Anne C. Smith, 2011, Cognitive, affective & behavioral neuroscience.

Impaired expected value computations in schizophrenia are associated with a reduced ability to integrate reward probability and magnitude of recent outcomes

M. Frank, J. Waltz, J. Gold, 2018, bioRxiv.

Impaired Expected Value Computations Coupled With Overreliance on Stimulus-Response Learning in Schizophrenia.

M. Frank, J. Waltz, J. Gold, 2018, Biological psychiatry. Cognitive neuroscience and neuroimaging.

Impaired expected value computations in schizophrenia are associated with a reduced ability to integrate reward probability and magnitude of recent outcomes

M. Frank, J. Waltz, J. Gold, 2018, bioRxiv.

Surprise! Dopamine signals mix action, value and error

Anne G E Collins, M. Frank, A. Collins, 2015, Nature Neuroscience.

Negative Symptoms of Schizophrenia Are Associated with Abnormal Effort-Cost Computations

Benjamin M. Robinson, M. Frank, J. Waltz, 2013, Biological Psychiatry.

Effort Cost Computation in Schizophrenia: A Commentary on the Recent Literature

M. Frank, J. Waltz, J. Gold, 2015, Biological Psychiatry.

Conflict acts as an implicit cost in reinforcement learning

M. Frank, J. Cavanagh, Sean E Masters, 2014, Nature Communications.

UvA-DARE ( Digital Academic Repository ) Stimulus discriminability may bias value-based probabilistic learning

Anne G E Collins, M. Frank, H. Slagter, 2017 .

Charting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology.

Karl J. Friston, P. Dayan, M. Owen, 2016, The lancet. Psychiatry.

How cognitive theory guides neuroscience

M. Frank, David Badre, 2015, Cognition.

Multiple Dissociations Between Comorbid Depression and Anxiety on Reward and Punishment Processing: Evidence From Computationally Informed EEG

John J. B. Allen, M. Frank, J. Cavanagh, 2019, Computational Psychiatry.

How Preparation Changes the Need for Top–Down Control of the Basal Ganglia When Inhibiting Premature Actions

K. R. Ridderinkhof, B. Forstmann, M. Frank, 2012, The Journal of Neuroscience.

Working Memory Load Strengthens Reward Prediction Errors

David Badre, Michael J Frank, M. Frank, 2017, The Journal of Neuroscience.

Linking Across Levels of Computation in Model-Based Cognitive Neuroscience

Michael J. Frank, M. Frank, 2015 .

Anxiety Impedes Adaptive Social Learning Under Uncertainty

M. Frank, Oriel FeldmanHall, A. Lamba, 2020, Psychological science.

Dissecting the impact of depression on decision-making

Thomas V. Wiecki, M. Frank, M. Trivedi, 2019, Psychological Medicine.

Motivational deficits in schizophrenia relate to abnormalities in cortical learning rate signals

D. Hernaus, M. Frank, J. Waltz, 2018, Cognitive, Affective, & Behavioral Neuroscience.

Altered probabilistic learning and response biases in schizophrenia: behavioral evidence and neurocomputational modeling.

Thomas V. Wiecki, Michael J Frank, James M Gold, 2011, Neuropsychology.

Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence.

Michael J Frank, Anne G E Collins, James M Gold, 2012, Archives of general psychiatry.

A role for dopamine-mediated learning in the pathophysiology and treatment of Parkinson's disease.

Michael J Frank, M. Frank, X. Zhuang, 2012, Cell reports.

Cognitive Frontal Theta Overrides Pavlovian Learning Biases

Ian W. Eisenberg, M. Frank, Q. Huys, 2014 .

Freezing of gait in Parkinson's disease is associated with functional decoupling between the cognitive control network and the basal ganglia.

Michael J Frank, Elie Matar, James M Shine, 2013, Brain : a journal of neurology.

Identifying the neural correlates of doorway freezing in Parkinson's disease

M. Frank, A. Moustafa, J. Shine, 2019, Human brain mapping.

025 The neural correlates of doorway freezing in parkinson’s disease

M. Frank, A. Moustafa, J. Shine, 2018, Journal of Neurology, Neurosurgery, and Psychiatry.

Interrelations between cognitive dysfunction and motor symptoms of Parkinson’s disease: behavioral and neural studies

Jacob J. Crouse, Julie M. Hall, M. Frank, 2016, Reviews in the neurosciences.

Intermittent subthalamic nucleus deep brain stimulation induces risk-aversive behavior in human subjects

Shaun R. Patel, M. Frank, E. Eskandar, 2018, eLife.

Larger Error Signals in Major Depression are Associated with Better Avoidance Learning

James F. Cavanagh, Michael J. Frank, John J. B. Allen, 2011, Front. Psychology.

Atypical Learning in Autism Spectrum Disorders: A Functional Magnetic Resonance Imaging Study of Transitive Inference.

J. C. Matter, M. Frank, C. Carter, 2015, Journal of the American Academy of Child and Adolescent Psychiatry.

CNTRICS imaging biomarkers final task selection: Long-term memory and reinforcement learning.

Deborah E. Hannula, M. Frank, N. Cohen, 2012, Schizophrenia bulletin.

Daily-life stress differentially impacts ventral striatal dopaminergic modulation of reward processing in first-degree relatives of individuals with psychosis

M. Frank, T. van Amelsvoort, F. Mottaghy, 2018, European Neuropsychopharmacology.

Striatal dopaminergic modulation of reinforcement learning predicts reward—oriented behavior in daily life

M. Frank, F. Mottaghy, A. Heinzel, 2017, Biological Psychology.

Intact striatal dopaminergic modulation of reward learning and daily-life reward-oriented behavior in first-degree relatives of individuals with psychotic disorder

M. Frank, T. van Amelsvoort, F. Mottaghy, 2017, Psychological Medicine.

Feedback-driven trial-by-trial learning in autism spectrum disorders.

J. C. Matter, M. Frank, J. Ragland, 2015, The American journal of psychiatry.

Cognitive correlates of psychosis in patients with Parkinson's disease

M. Frank, A. Moustafa, A. Eissa, 2014, Cognitive neuropsychiatry.

Beyond Drift Diffusion Models: Fitting a broad class of decision and RL models with HDDM

M. Frank, Alexander Fengler, Krishn Bera, 2022, bioRxiv.

Dopamine D2 agonist affects visuospatial working memory distractor interference depending on individual differences in baseline working memory span

M. Frank, J. Cavanagh, J. Broadway, 2018, Cognitive, Affective, & Behavioral Neuroscience.

Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work

Jessica I. Määttä, M. Frank, R. Cools, 2019, Science.

A Neural Correlate of Strategic Exploration at the Onset of Adolescence

Michael J. Frank, Ronald E. Dahl, Andrew S. Kayser, 2016, Journal of Cognitive Neuroscience.

Adaptive control of synaptic plasticity integrates micro- and macroscopic network function

M. Frank, Daniel N. Scott, 2022, Neuropsychopharmacology.

Beyond Drift Diffusion Models: Fitting a Broad Class of Decision and Reinforcement Learning Models with HDDM

M. Frank, Alexander Fengler, Krishn Bera, 2022, Journal of Cognitive Neuroscience.

Beyond Drift Diffusion Models: Fitting a broad class of decision and RL models with HDDM

M. Frank, Alexander Fengler, Krishn Bera, 2022, bioRxiv.

Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms

M. Frank, Willoughby B. Britton, Peter F Hitchcock, 2022, Cognitive, Affective, & Behavioral Neuroscience.

Computational Approaches for Studying Mechanisms of Psychiatric Disorders

Z. Kurth-Nelson, J. O'Doherty, Y. Niv, 2016 .

All or nothing belief updating in patients with schizophrenia reduces precision and flexibility of beliefs.

M. Frank, M. Nassar, J. Waltz, 2021, Brain : a journal of neurology.

Cognitive [Computational] Neuroscience Test Reliability and Clinical Applications for Serious Mental Illness (CNTRaCS) Consortium: Progress and Future Directions.

Megan A. Boudewyn, Molly A. Erickson, S. Luck, 2022, Current topics in behavioral neurosciences.

Motivational Deficits in Schizophrenia Are Associated With Reduced Differentiation Between Gain and Loss-Avoidance Feedback in the Striatum.

M. Frank, J. Waltz, J. Gold, 2017, Biological psychiatry. Cognitive neuroscience and neuroimaging.

Acute Stress Selectively Reduces Reward Sensitivity how this access benefits you. Your story matters

M. Frank, D. Pizzagalli, R. Bogdan, 2013 .

Adaptive control of synaptic plasticity integrates micro- and macroscopic network function

M. Frank, Daniel N. Scott, 2022, Neuropsychopharmacology.

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

M. Frank, Alana Jaskir, 2022, bioRxiv.

Differential, but not opponent, effects of l-DOPA and citalopram on action learning with reward and punishment

P. Dayan, R. Dolan, M. Frank, 2013, Psychopharmacology.

Cross-task contributions of fronto-basal ganglia circuitry in response inhibition and conflict-induced slowing

Tomas Knapen, Michael J Frank, K Richard Ridderinkhof, 2017, bioRxiv.

Computational Cognitive Neuroscience Approaches to Deconstructing Mental Function and Dysfunction

M. Frank, 2016 .

Probabilistic Reinforcement Learning in Patients With Schizophrenia: Relationships to Anhedonia and Avolition.

Deanna M Barch, Michael J Frank, Anne G E Collins, 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.

Altered cingulate sub-region activation accounts for task-related dissociation in ERN amplitude as a function of obsessive-compulsive symptoms

James F. Cavanagh, Michael J. Frank, Theo O. J. Gründler, 2010, Neuropsychologia.

Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia

James A. Waltz, Michael J. Frank, Anne G. E. Collins, 2017, Biological Psychiatry.

Frontal theta links prediction errors to behavioral adaptation in reinforcement learning

James F. Cavanagh, Michael J. Frank, John J. B. Allen, 2010, NeuroImage.

Retention of Value Representations Across Time in People With Schizophrenia and Healthy Control Subjects.

M. Frank, J. Waltz, J. Gold, 2020, Biological psychiatry. Cognitive neuroscience and neuroimaging.

Beyond Gradients: Noise Correlations Control Hebbian Plasticity to Shape Credit Assignment

M. Frank, Daniel N. Scott, 2021, bioRxiv.

Dissociable responses to punishment in distinct striatal regions during reversal learning

Roshan Cools, Barbara J. Sahakian, Michael J. Frank, 2010, NeuroImage.

Increased conflict-induced slowing, but no differences in conflict-induced positive or negative prediction error learning in patients with schizophrenia

M. Frank, J. Waltz, J. Gold, 2019, Neuropsychologia.

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

Michael J. Frank, Anne G. E. Collins, M. Frank, 2017, bioRxiv.

Biophysical and Architectural Mechanisms of Subthalamic Theta under Response Conflict

M. Frank, S. Jones, Prannath Moolchand, 2021, The Journal of Neuroscience.

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

Thomas V. Wiecki, Michael J. Frank, Imri Sofer, 2013, Front. Neuroinform..

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

Matthew R Nassar, Michael J Frank, M. Frank, 2016, Current Opinion in Behavioral Sciences.

Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning

Michael J. Frank, Nicholas T. Franklin, Nicholas T. Franklin, 2019, bioRxiv.

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

Roger Ratcliff, Michael J. Frank, R. Ratcliff, 2012, Neural Computation.

Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience

Alexander Fengler, Michael J. Frank, Lakshmi N. Govindarajan, 2020, bioRxiv.

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

Michael J Frank, Anne G E Collins, M. Frank, 2017, Proceedings of the National Academy of Sciences.

Honeycomb: a template for reproducible psychophysiological tasks for clinic, laboratory, and home use

M. Frank, D. Borton, M.I. Restrepo, 2021, Revista brasileira de psiquiatria.

Advances in the computational understanding of mental illness

Martin Paulus, Michael J. Frank, Quentin J.M. Huys, 2020, Neuropsychopharmacology.

Motor symptoms in Parkinson’s disease: A unified framework

Marjan Jahanshahi, Michael J. Frank, Srinivasa Chakravarthy, 2016, Neuroscience & Biobehavioral Reviews.

Dopaminergic basis for impairments in functional connectivity across subdivisions of the striatum in Parkinson's disease

Moran Gilat, Michael J Frank, James M Shine, 2015, Human brain mapping.

The neurogenetics of exploration and exploitation: Prefrontal and striatal dopaminergic components

M. Frank, B. B. Doll, Jen Oas-Terpstra, 2009, Nature Neuroscience.

Impulse control disorders and levodopa-induced dyskinesias in Parkinson's disease: an update

Michael J Frank, Erwan Bezard, Valerie Voon, 2017, The Lancet Neurology.

Faculty Opinions recommendation of States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.

Michael J. Frank, Bradley B. Doll, M. Frank, 2010 .

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

M. Frank, T. Verguts, C. Calderon, 2022, bioRxiv.

A neurocomputational account of catalepsy sensitization induced by D2 receptor blockade in rats: context dependency, extinction, and renewal

Thomas V. Wiecki, Michael J. Frank, Werner J. Schmidt, 2009, Psychopharmacology.

Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data

Michael J. Frank, Mads L. Pedersen, M. Frank, 2020, Computational brain & behavior.

Toolbox for the Reinforcement Learning Drift Diffusion Model

Michael J. Frank, Mads Lund Pedersen, M. Frank, 2019, 2019 Conference on Cognitive Computational Neuroscience.

The basal ganglia in reward and decision making: computational models and empirical studies

Bradley B. Doll, Michael J. Frank, M. Frank, 2009 .

University of Birmingham Catecholaminergic challenge uncovers distinct Pavlovian and instrumental mechanisms of motivated (in)action

M. Frank, R. Cools, H. D. den Ouden, 2017 .

Learning Cognitive Control

M. Frank, David Badre, A. Bhandari, 2017 .

Chunking as a rational strategy for lossy data compression in visual working memory

Julie C. Helmers, Matthew R Nassar, Michael J Frank, 2017, bioRxiv.

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

Tom Heskes, Nathaniel D. Daw, Michael J. Frank, 2018, bioRxiv.

Reduced susceptibility to confirmation bias in schizophrenia

James A. Waltz, Bradley B. Doll, Michael J. Frank, 2014, Cognitive, Affective, & Behavioral Neuroscience.

Reinforcement Learning, Conflict Monitoring, and Cognitive Control: An Integrative Model of Cingulate-Striatal Interactions and the ERN

M. Frank, Jeffrey Cockburn, 2011 .

Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment

Arif A. Hamid, M. Frank, C. Moore, 2021, Cell.

Dopamine waves as a mechanism for spatiotemporal credit assignment

Christopher I. Moore, Michael J. Frank, Arif A. Hamid, 2019, bioRxiv.

Positive reward prediction errors during decision making strengthen memory encoding

Anthony I. Jang, M. Frank, M. Nassar, 2019, Nature Human Behaviour.

Probability and magnitude evaluation in schizophrenia

M. Frank, J. Waltz, J. Gold, 2016, Schizophrenia Research: Cognition.

The Subthalamic Nucleus Contributes to Post-error Slowing

James F. Cavanagh, Michael J. Frank, Joseph L. Sanguinetti, 2014, Journal of Cognitive Neuroscience.

Stop! Stay tuned for more information

James F. Cavanagh, Michael J. Frank, M. Frank, 2013, Experimental Neurology.

Computational Psychiatry Needs Time and Context.

M. Frank, E. Fried, Peter F Hitchcock, 2021, Annual review of psychology.

The Case for Adaptive Neuromodulation to Treat Severe Intractable Mental Disorders

Eric A. Storch, Sameer A. Sheth, David A. Borton, 2019, Front. Neurosci..

The role of frontostriatal impairment in freezing of gait in Parkinson's disease

James M. Shine, Michael J. Frank, Elie Matar, 2013, Front. Syst. Neurosci..

Theory-Based Computational Psychiatry

M. Frank, Q. Huys, T. Maia, 2017, Biological Psychiatry.

Human EEG Uncovers Latent Generalizable Rule Structure during Learning

Michael J Frank, Anne G E Collins, James F Cavanagh, 2014, The Journal of Neuroscience.

Patients with schizophrenia demonstrate inconsistent preference judgments for affective and nonaffective stimuli.

Michael J Frank, Benjamin M. Robinson, James M Gold, 2011, Schizophrenia bulletin.

Running Head: Computational Psychiatry Needs Time and Context Computational Psychiatry Needs Time and Context

M. Frank, E. Fried, Peter F Hitchcock, 2021 .

Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and recommendations

Thomas V. Wiecki, Thomas J. Palmeri, Andrew Heathcote, 2018, Journal of Mathematical Psychology.

Statistical context dictates the relationship between feedback-related EEG signals and learning

Michael J. Frank, Matthew R. Nassar, Rasmus Bruckner, 2019, bioRxiv.

Social reinforcement learning as a predictor of real-life experiences in individuals with high and low depressive symptomatology

M. Frank, C. McCabe, A. Frey, 2019, Psychological Medicine.

Computational models of reinforcement learning: the role of dopamine as a reward signal

Jean-Marc Fellous, R. D. Samson, M. J. Frank, 2010, Cognitive Neurodynamics.

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning

Michael J Frank, Nicholas T. Franklin, Nicholas T Franklin, 2015, eLife.

Roles of D1-like dopamine receptors in the nucleus accumbens and dorsolateral striatum in conditioned avoidance responses

M. Frank, E. Miyoshi, C. Cunha, 2011, Psychopharmacology.

Reduced susceptibility to confirmation bias in schizophrenia

M. Frank, J. Waltz, J. Gold, 2014, Cognitive, affective & behavioral neuroscience.

The Importance of Standards for Sharing of Computational Models and Data

Brandon M. Turner, Christopher J. Markiewicz, B. Love, 2019, Computational Brain & Behavior.

Dopamine, Locus of Control, and the Exploration-Exploitation Tradeoff

Michael J Frank, Jennifer M. Mitchell, M. Frank, 2015, Neuropsychopharmacology.

Compositional clustering in task structure learning

Michael J. Frank, Nicholas T. Franklin, M. Frank, 2017, bioRxiv.

Motor Demands Constrain Cognitive Rule Structures

Anne Gabrielle Eva Collins, Michael Joshua Frank, M. Frank, 2016, PLoS Comput. Biol..

Modeling Negative Symptoms in Schizophrenia

M. Frank, J. Waltz, J. Gold, 2018 .

A computational neuroimaging study of reinforcement learning and goal-directed exploration in schizophrenia spectrum disorders.

M. Frank, J. Waltz, J. Gold, 2023, Psychological medicine.

Generalizing to generalize: when (and when not) to be compositional in task structure learning

Michael J. Frank, Nicholas T. Franklin, M. Frank, 2019 .

Sensitivity to reward and punishment in major depressive disorder: Effects of rumination and of single versus multiple experiences

Michael J Frank, M. Frank, I. Gotlib, 2012, Cognition & emotion.

A Control Theoretic Model of Adaptive Learning in Dynamic Environments

Michael J. Frank, Matthew R. Nassar, Amitai Shenhav, 2018, Journal of Cognitive Neuroscience.

Positive reward prediction errors strengthen incidental memory encoding

Michael J. Frank, Matthew R. Nassar, Anthony I. Jang, 2018, bioRxiv.

Do substantia nigra dopaminergic neurons differentiate between reward and punishment?

M. Frank, D. Surmeier, 2009, Journal of molecular cell biology.

Behavioral / Cognitive Frontal Theta Overrides Pavlovian Learning Biases

Ian W. Eisenberg, M. Frank, Q. Huys, 2017 .

Proof-of-Mechanism Study of the Phosphodiesterase 10 Inhibitor RG7203 in Patients With Schizophrenia and Negative Symptoms

Christopher H. Chatham, M. Frank, J. Dukart, 2021, Biological psychiatry global open science.

A Neurocomputational Account of Catalepsy Sensitization Induced by D2-receptor-blockade in Rats: Context-dependency, Extinction and Renewal

Thomas V. Wiecki, M. Frank, W. Schmidt, 2022 .

Positive reward prediction errors during decision making strengthen memory encoding

Anthony I. Jang, Matthew R Nassar, Michael J Frank, 2019, Nature Human Behaviour.

An evidence accumulation model of motivational and developmental influences over sustained attention

M. Frank, A. Shenhav, M. Esterman, 2020, CogSci.

Title : Biases in the explore-exploit tradeoff in addictions : the role of avoidance of uncertainty Running Title : Uncertainty Avoidance in Addictions

M. Frank, V. Voon, N. Harrison, 2017 .

Biases in the explore-exploit tradeoff in addictions: the role of avoidance of uncertainty

M. Frank, V. Voon, N. Harrison, 2017 .

Biases in the Explore-Exploit Tradeoff in Addictions: the Role of Avoidance of Uncertainty

M. Frank, V. Voon, N. Harrison, 2015 .

Computational Psychiatry Needs Time and Context.

M. Frank, E. Fried, Peter F Hitchcock, 2021, Annual review of psychology.

Working Memory Load Strengthens Reward Prediction Errors

M. Frank, David Badre, A. Collins, 2017, The Journal of Neuroscience.

A Control Theoretic Model of Adaptive Learning in Dynamic Environments

M. Frank, M. Nassar, A. Shenhav, 2018, Journal of Cognitive Neuroscience.

Biases in the Explore–Exploit Tradeoff in Addictions: The Role of Avoidance of Uncertainty

M. Frank, V. Voon, N. Harrison, 2015, Neuropsychopharmacology.

Methylphenidate Modifies the Motion of the Circadian Clock Lamotrigine in Mood Disorders and Cocaine Dependence Cortical Glutamate in Postpartum Depression Title: Biases in the Explore-exploit Tradeoff in Addictions: the Role of Avoidance of Uncertainty Running Title: Uncertainty Avoidance in Addict

M. Frank, V. Voon, N. Harrison, 2022 .

Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis.

M. Frank, David Badre, 2012, Cerebral cortex.

Mechanisms of hierarchical reinforcement learning in cortico-striatal circuits 2: evidence from fMRI.

M. Frank, David Badre, 2012, Cerebral cortex.

Dissecting the impact of depression on decision-making

Thomas V. Wiecki, M. Frank, M. Trivedi, 2019, Psychological Medicine.

Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and recommendations

Thomas V. Wiecki, Guy E. Hawkins, G. Logan, 2018, Journal of Mathematical Psychology.

Multiple Dissociations Between Comorbid Depression and Anxiety on Reward and Punishment Processing: Evidence From Computationally Informed EEG

John J. B. Allen, M. Frank, J. Cavanagh, 2019, Computational Psychiatry.

Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning

M. Frank, Thomas Serre, Drew Linsley, 2023, ArXiv.

Striatal Dopamine Predicts Outcome-Specific Reversal Learning and Its Sensitivity to Dopaminergic Drug Administration

M. Frank, M. D’Esposito, R. Cools, 2009, Journal of Neuroscience.

Impaired Expected Value Computations Coupled With Overreliance on Stimulus-Response Learning in Schizophrenia.

M. Frank, J. Waltz, J. Gold, 2018, Biological psychiatry. Cognitive neuroscience and neuroimaging.

Impaired expected value computations coupled with overreliance on prediction error learning in schizophrenia

J. Waltz, M. Frank, J. Waltz, 2017, bioRxiv.

Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning

M. Frank, A. Collins, 2016, Cognition.

Mechanisms of hierarchical reinforcement learning in cortico-striatal circuits 2: evidence from fMRI.

M. Frank, David Badre, 2012, Cerebral cortex.

Roles of D1-like dopamine receptors in the nucleus accumbens and dorsolateral striatum in conditioned avoidance responses

M. Frank, E. Miyoshi, C. Cunha, 2011, Psychopharmacology.