To see a list of our publications sorted by year, you may go here.
To see a list of our publications including abstracts, you may go here.

(Some papers apply to multiple themes; below listings generated automatically via computer script -- Please let me know if a link is broken!)

Modeling

  • [PDF] [Abstract] Scott, D.N. & Frank, M.J. (2022). Adaptive conttol of synaptic plasticity integrates micro- and macroscopic network function. Neuropsychopharmacology Reviews.

  • [PDF] [Abstract] Liu, R.G. & Frank, M.J. (2022). Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning. Artificial Intelligence, 312,.

  • [PDF] [Abstract] Fengler, A., Bera, K., Pedersen, M.L. & Frank, M.J. (2022). Beyond Drift Diffusion Models: Fitting a broad class of decision and RL models with HDDM. Journal of Cognitive Neuroscience.

  • [PDF] [Abstract] Moolchand, P., Jones, S.R. & Frank, M.J. (2022). Biophysical and Architectural Mechanisms of Subthalamic Theta under Response Conflict. Journal of Neuroscience, 42, 4470-4487.

  • [PDF] [Abstract] Calderon, C.B., Verguts, T. & Frank, M.J. (2022). Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits. PLoS Computational Biology e1009854.

  • [PDF] [Abstract] Hamid, A.A., Frank, M.J.*. & Moore, C.I.*. (2021). Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. *co-senior authors; alphabetical order. Cell, 184, 2733-2749.

  • [PDF] [Abstract] Fengler, A., Govindarajan, L., Chen, T. & Frank, M.J. (2021). Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience. eLife, 10, e65074.

  • [PDF] [Abstract] Nassar, M.N., Waltz, J.A., Albrecht, M.A., Gold, J.M. & Frank, M.J. (2021). All or nothing belief updating in patients with schizophrenia reduces precision and flexibility of beliefs. Brain, 144, 1013-1029.

  • [PDF] [Abstract] Lehnert, L., Littman, M.L. & Frank, M.J. (2020). Reward-predictive representations generalize across tasks in reinforcement learning. PLOS Computational Biology, 16(10), e1008317.

  • [Main PDF] [Supp PDF] [Abstract] Westbrook, J., van den Bosch, R., Maatta, J.I., Hofmans, L., Papadopetraki, D., Cools, R.*. & Frank, M.J.*. (2020). Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. *co-senior authors. Science, 367, 1362-1366. [Journal Homepage]

  • [PDF] [Abstract] Franklin, N.T. & Frank, M.J. (2020). Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning. PLOS Computational Biology, 16, e1007720.

  • [PDF] [Abstract] Pedersen, M.L. & Frank, M.J. (2020). Simultaneous hierarchical Bayesian parameter estimation for reinforcement learning and drift diffusion models: a tutorial and links to neural data. Computational Brain & Behavior, 3, 458-471.

  • [PDF] [Abstract] Piray, P., Dezfouli, A., Heskes, T., Frank, M.J. & Daw, N.D. (2019). Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Computational Biology.

  • [PDF] [Abstract] Nassar, M.R., Bruckner, R. & Frank, M.J. (2019). Statistical context dictates the relationship between feedback-related EEG signals and learning. eLife, 8, e46975.

  • [PDF] [Abstract] Jang, A.I., Nassar, M.N., Dillon, D.G. & Frank, M.J. (2019). Positive reward prediction errors during decision making strengthen memory encoding. Nature Human Behaviour, 3,.

  • [PDF] [Abstract] Franklin, N.T. & Frank, M.J. (2018). Compositional clustering in task structure learning. PLOS Computational Biology, 14(4), e1006116.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2018). Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory. Proceedings of the National Academy of Sciences, 115, 2502-2507.

  • [PDF] [Abstract] Nassar, M.R., Helmers, J. & Frank, M.J. (2018). Chunking as a rational strategy for lossy data compression in visual working memory. Psychological Review, 125, 486-511.

  • [PDF] [Abstract] Swart, J.C., Frank, M.J., Maatta, J.I., Jensen, O., Cools, R. & den Ouden, H.E.M. (2018). Frontal network dynamics reflect neurocomputational mechanisms for reducing maladaptive biases in motivated action. PLoS Biology, 16, e2005959.

  • [PDF] [Abstract] Ritz, H., Nassar, M.R., Frank, M.J. & Shenhav, A. (2018). A control theoretic model of adaptive learning in dynamic environments. Journal of Cognitive Neuroscience, 30, 1405-1421.

  • [PDF] [Abstract] Swart, J.C., Frobose, M.I., Cook, J.L., Geurts, D.E.M., Frank, M.J., Cools, R. & den Ouden, H.E.M. (2017). Catecholaminergic challenge uncovers distinct Pavlovian and instrumental mechanisms of motivated (in)action. eLife, 6:e22169,.

  • [PDF] [Abstract] Frank, M.J. (2016). Computational Cognitive Neuroscience Approaches to Deconstructing Mental Function and Dysfunction. A.D. Redish & J.D. Gordon (Eds) Computational Psychiatry: New Perspectives on Mental Illness. Strüngmann Forum Reports, vol. 20, J. Lupp, series editor, 101-120, Cambridge, MA: MIT Press.[Publisher] [Strüngmann Forum Reports]

  • [PDF] [Abstract] Kurth-Nelson, Z., O'Doherty, J.P., M., B.D., Denève, S., Durstewitz, D., Frank, M., Gordon, J., Mathew, S.J., Y., N., Ressler, K. & Tost, H. (2016). Computational Approaches for Studying Mechanisms of Psychiatric Disorders. A.D. Redish & J.D. Gordon (Eds) Computational Psychiatry: New Perspectives on Mental Illness. Strüngmann Forum Reports, vol. 20, J. Lupp, series editor, Cambridge, MA: MIT Press.[Publisher] [Strüngmann Forum Reports]

  • [PDF] [Abstract] Pedersen, M.L., Frank, M.J. & Biele, G. (2017). The drift diffusion model as the choice rule in reinforcement learning. Psychonomic Bulletin & Review, 24, 1234-1251.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2016). Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning. Cognition, 152, 160-169.

  • [PDF] [Abstract] Nassar, M.R. & Frank, M.J. (2016). Taming the beast: extracting generalizable knowledge from computational models of cognition. Current Opinion in Behavioral Sciences, 11, 49-54.

  • [PDF] [Abstract] Franklin, N.T. & Frank, M.J. (2015). A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning. eLife, 4:e12029,.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2016). Motor demands constrain cognitive rule structures. PLoS Computational Biology, 12:e1004785,.

  • [PDF] [Abstract] Wiecki, T.V., Antoniades, C.A., Stevenson, A., Kennard, C., Borowsky, B., Owen, G., Leavitt, B., Roos, R., Durr, A., Tabrizi, S.J. & Frank, M.J. (2016). A computational cognitive biomarker for early-stage Huntington's disease. PLoS ONE, 11:e0148409,.

  • [PDF] [Abstract] Frank, M.J., Gagne, C., Nyhus, E., Masters, S., Wiecki, T.V., Cavanagh, J.F. & Badre, D. (2015). fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning. Journal of Neuroscience, 35, 484-494.

  • [PDF] [Abstract] Frank, M.J. (2015). Linking across levels of computation in model-based cognitive neuroscience. B.U. Forstmann & E. Wagenmakers (Eds) An Introduction to Model-Based Cognitive Neuroscience, 163-181, New York: Springer.

  • [Main PDF] [Supp PDF] [Abstract] Wiecki, T.V., Poland, J.S. & Frank, M.J. (2015). Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification. Clinical Psychological Science, 3, 378-399.

  • [PDF] [Abstract] Collins, A.G.E., Brown., J., Gold, J., Waltz, J. & Frank, M.J. (2014). Working memory contributions to reinforcement learning in schizophrenia. Journal of Neuroscience, 34, 13747-13756.

  • [Main PDF] [Supp PDF] [Abstract] Cockburn, J., Collins, A.G.E. & Frank, M.J. (2014). A reinforcement learning mechanism responsible for the valuation of free choice. Neuron, 83, 551-557.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2014). Opponent Actor Learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. Psychological Review, 121, 337-366.

  • [PDF] [Abstract] Cavanagh, J.F., Wiecki, T.V., Kochar, A. & Frank, M.J. (2014). Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology: General, 143, 1476-1488.

  • [Main PDF] [Supp PDF] [Abstract] Wiecki, T.V., Sofer, I. & Frank, M.J. (2013). HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Frontiers of Neuroinformatics, 7, 1-10.

  • [PDF] [Abstract] Cavanagh, J.F., Eisenberg, I., Guitart-Masip, M., Huys, Q. & Frank, M.J. (2013). Frontal theta overrides Pavlovian learning biases. Journal of Neuroscience, 33, 8541-8548.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2013). Cognitive control over learning: Creating, clustering and generalizing task-set structure. Psychological Review, 120, 190-229.

  • [PDF] [Abstract] Wiecki, T.V. & Frank, M.J. (2013). A computational model of inhibitory control in frontal cortex and basal ganglia. Psychological Review, 120, 329-355.

  • [PDF] [Abstract] Beeler, J.A., Frank, M.J., McDaid, J., Alexander, E., Turkson, S., Sol Bernandez, M., McGehee, D. & Zhuang, X. (2012). A role for dopamine-mediated learning in the pathophysiology and treatment of Parkinson's Disease. Cell Reports, 2, 1747-1761.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2012). How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. European Journal of Neuroscience, 35, 1024-1035.

  • [PDF] [Abstract] Cavanagh, J.F., Figueroa, C.M., Cohen, M.X. & Frank, M.J. (2012). Frontal theta reflects uncertainty and unexpectedness during exploration and exploitation. Cerebral Cortex 2575-86.

  • [Main PDF] [Supp PDF] [Abstract] Badre, D., Doll, B.B., Long, N.M. & Frank, M.J. (2012). Rostrolateral prefrontal cortex and individual differences in uncertainty-driven exploration. Neuron, 73, 595-607.

  • [Main PDF] [Supp PDF] [Abstract] Gold, J.M., Waltz, J.A., Matveeva, T.M., Kasanova, Z., Strauss, G.P., Herbener, E.S., Collins, A.G.E. & Frank, M.J. (2012). Negative symptoms and the failure to represent the expected reward value of actions: Behavioral and computational modeling evidence. Archives of General Psychiatry, 69, 129-138.

  • [Main PDF] [Supp PDF] [Abstract] Cavanagh, J.F., Wiecki, T.V., Cohen, M.X., Figueroa, C.M., Samanta, J., Sherman, S.J. & Frank, M.J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14, 1462-1467.

  • [PDF] [Abstract] Ratcliff, R. & Frank, M.J. (2012). Reinforcement-based decision making in corticostriatal circuits: Mutual constraints by neurocomputational and diffusion models. Neural Computation, 24, 1186-1229.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J. & Badre, D. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: Computational analysis. Cerebral Cortex, 22, 509-526.

  • [PDF] [Abstract] Cockburn, J. & Frank, M.J. (2011). Reinforcement learning, conflict monitoring, and cognitive control: An integrative model of cingulate-striatal interactions and the ERN. R. Mars, J. Sallet, M. Rushworth & N. Yeung (Eds) Neural Basis of Motivational and Cognitive Control, 311-331, Cambridge: The MIT Press.

  • [PDF] [Abstract] Wiecki, T.V. & Frank, M.J. (2010). Neurocomputational models of motor and cognitive deficits in Parkinson's disease. Progress in Brain Research, 183, 275-297.

  • [PDF] [Abstract] Samson, R.D., Frank, M.J. & Fellous, J. (2010). Computational models of reinforcement learning: the role of dopamine as a reward signal. Cognitive Neurodynamics, 4, 91-105.

  • [PDF] [Abstract] Hazy, T.E., Frank, M.J. & O'Reilly, R.C. (2010). Neural mechanisms of acquired phasic dopamine responses in learning. Neuroscience & Biobehavioral Reviews, 34, 701-720.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Doll, B.B., Oas-Terpstra, J. & Moreno, F. (2009). Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation. Nature Neuroscience, 12, 1062-1068.

  • [email me for PDF] [Abstract] Doll, B.B. & Frank, M.J. (2009). The basal ganglia in reward and decision making: computational models and empirical studies. J. Dreher & L. Tremblay (Eds) Handbook of Reward and Decision Making, 399-425, Oxford: Academic Press.

  • [Main PDF] [Supp PDF] [Abstract] Wiecki, T.V., Riedinger, K., Meyerhofer, A., Schmidt, W. & Frank, M.J. (2009). A neurocomputational account of catalepsy sensitization induced by D2-receptor-blockade in rats: Context-dependency, extinction, and renewal. Psychopharmacology, 204, 265--277.

  • [PDF] [Abstract] Doll, B.B., Jacobs, W.J., Sanfey, A.G. & Frank , M.J. (2009). Instructional control of reinforcement learning: A behavioral and neurocomputational investigation. Brain Research, 1299, 74-94.

  • [PDF] [Abstract] Cohen, M.X. & Frank, M.J. (2009). Neurocomputational models of basal ganglia function in learning, memory and choice. Behavioural Brain Research, 199, 141-156.

  • [PDF] [Abstract] Moustafa, A.A., Cohen, M.X., Sherman, S.J. & Frank, M.J. (2008). A role for dopamine in temporal decision making and reward maximization in Parkinsonism. Journal of Neuroscience, 28, 12294-12304.

  • [PDF] [Abstract] Santesso, D., Evins, A., Frank, M., Cowman, E. & Pizzagalli, D. (2009). Single dose of a dopamine agonist impairs reinforcement learning in humans: Converging evidence from electrophysiology and computational modeling of striatal-cortical function. Human Brain Mapping, 30, 1963--1976.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Samanta, J., Moustafa, A.A. & Sherman, S.J. (2007). Hold your horses: Impulsivity, deep brain stimulation and medication in Parkinsonism. Science, 318, 1309-1312. [Journal Homepage]

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Moustafa, A.A., Haughey, H., Curran, T. & Hutchison, K. (2007). Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning. Proceedings of the National Academy of Sciences, 104, 16311-16316. [Journal Homepage]

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Scheres, A. & Sherman, S.J. (2007). Understanding decision making deficits in neurological conditions: Insights from models of natural action selection. Philosophical Transactions of the Royal Society - B, 362, 1641-1654. [Journal Homepage]

  • [Main PDF] [Supp PDF] [Abstract] Hazy, T.E., Frank, M.J. & O'Reilly, R.C. (2007). Toward an executive without a homunculus: Computational models of the prefrontal cortex/basal ganglia system. Philosophical Transactions of the Royal Society - B, 362, 1601-1613. [Journal Homepage]

  • [PDF] [Abstract] O'Reilly, R.C., Frank, M.J., Hazy, T.E. & Watz, B. (2007). PVLV: The Primary Value and Learned Value Pavlovian learning algorithm. Behavioral Neuroscience, 121, 31-49.

  • [PDF] [Abstract] Frank, M.J. (2006). Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making. Neural Networks, 19, 1120-1136.

  • [PDF] [Abstract] Frank, M.J. & Claus, E.D. (2006). Anatomy of a decision: Striato-orbitofrontal interactions in reinforcement learning, decision making and reversal. Psychological Review, 113, 300-326.

  • [PDF] [Abstract] O'Reilly, R.C. & Frank, M.J. (2006). Making working memory work: A computational model of learning in the frontal cortex and basal ganglia. Neural Computation, 18, 283-328.

  • [PDF] [Abstract] Hazy, T.E., Frank, M.J. & O'Reilly, R.C. (2006). Banishing the homunculus: Making working memory work. Neuroscience, 139, 105--118.

  • [PDF] [Abstract] Frank, M.J. (2005). Dynamic dopamine modulation in the basal ganglia: A neurocomputational account of cognitive deficits in medicated and non-medicated Parkinsonism. Journal of Cognitive Neuroscience, 17, 51-72.

  • [PDF] [Abstract] Atallah, H.E., Frank, M.J. & O'Reilly, R.C. (2004). Hippocampus, cortex and basal ganglia: Insights from computational models of complementary learning systems. Neurobiology of Learning and Memory, 82/3, 253-67.

  • [PDF] [Abstract] Frank, M.J., Rudy, J.W. & O'Reilly, R.C. (2003). Transitivity, flexibility, conjunctive representations and the hippocampus: II. A computational analysis. Hippocampus, 13, 341-354.

  • [PDF] [Abstract] Frank, M.J., Loughry, B. & O'Reilly, R.C. (2001). Interactions between the frontal cortex and basal ganglia in working memory: A computational model. Cognitive, Affective, and Behavioral Neuroscience, 1, 137-160.

Empirical

  • [PDF] [Abstract] Brown, V.M., Hallquist, M.N., Frank, M.J. & Dombrovski, A.Y. (2022). Humans adaptively resolve the explore-exploit dilemma under cognitive constraints: Evidence from a multi-armed bandit task. Cognition, 229,.

  • [PDF] [Abstract] Hamid, A.A., Frank, M.J.*. & Moore, C.I.*. (2021). Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. *co-senior authors; alphabetical order. Cell, 184, 2733-2749.

  • [PDF] [Abstract] Rac-Lubashevsky, R. & Frank, M.J. (2021). Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence. PLoS Computational Biology, 17, e1008971.

  • [Main PDF] [Supp PDF] [Abstract] Westbrook, J., van den Bosch, R., Maatta, J.I., Hofmans, L., Papadopetraki, D., Cools, R.*. & Frank, M.J.*. (2020). Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. *co-senior authors. Science, 367, 1362-1366. [Journal Homepage]

  • [PDF] [Abstract] Franklin, N.T. & Frank, M.J. (2020). Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning. PLOS Computational Biology, 16, e1007720.

  • [PDF] [Abstract] Lamba, A., Frank, M.J. & FeldmanHall, O. (2020). Anxiety impedes adaptive social learning under uncertainty. Psychological Science, 31, 592-603.

  • [PDF] [Abstract] Nassar, M.R., Bruckner, R. & Frank, M.J. (2019). Statistical context dictates the relationship between feedback-related EEG signals and learning. eLife, 8, e46975.

  • [PDF] [Abstract] Frey, A., Frank, M.J. & McCabe, C. (2019). Social reinforcement learning as a predictor of real-life experiences in individuals with high and low depressive symptomatology. Psychological Medicine.

  • [PDF] [Abstract] Ironside, M., Amemori, K., McGrath, C.L., Pedersen, M.L., Su Kang, M., Amemori, S., Frank, M.J., Graybiel, A.M. & Pizzagalli, D.A. (2020). Approach-avoidance conflict in major depression: Congruent neural findings in human and non-human primates. Biological Psychiatry, 87, 399-408.

  • [PDF] [Abstract] Jang, A.I., Nassar, M.N., Dillon, D.G. & Frank, M.J. (2019). Positive reward prediction errors during decision making strengthen memory encoding. Nature Human Behaviour, 3,.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2018). Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory. Proceedings of the National Academy of Sciences, 115, 2502-2507.

  • [PDF] [Abstract] Nassar, M.R., Helmers, J. & Frank, M.J. (2018). Chunking as a rational strategy for lossy data compression in visual working memory. Psychological Review, 125, 486-511.

  • [PDF] [Abstract] Cavanagh, J.F., Bismark, A., Frank, M.J. & Allen, J.J.B. (2018). Multiple Dissociations between Comorbid Depression and Anxiety on Reward and Punishment Processing: Evidence from Computationally Informed EEG. Computational Psychiatry, 1, 1-17.

  • [PDF] [Abstract] Swart, J.C., Frank, M.J., Maatta, J.I., Jensen, O., Cools, R. & den Ouden, H.E.M. (2018). Frontal network dynamics reflect neurocomputational mechanisms for reducing maladaptive biases in motivated action. PLoS Biology, 16, e2005959.

  • [PDF] [Abstract] Patel, S.R., Herrington, T., Sheth, S.A., Mian, M., Bick, S.K., Yang, J.C., Flaherty, A.W., Frank, M.J., Widge, A.S., Dougherty, D. & Eskandar, E.N. (2018). Intermittent subthalamic nucleus deep brain stimulation induces risk-aversive behavior in human subjects. eLife, 7, e36460.

  • [PDF] [Abstract] Ritz, H., Nassar, M.R., Frank, M.J. & Shenhav, A. (2018). A control theoretic model of adaptive learning in dynamic environments. Journal of Cognitive Neuroscience, 30, 1405-1421.

  • [PDF] [Abstract] Jahfari, S., Ridderinkhof, K.R., Collins, A.G.E., Knapen, T., Waldorp, L. & Frank, M.J. (2019). Cross-task contributions of frontobasal ganglia circuitry in response inhibition and conflict-induced slowing. Cerebral Cortex, 29, 1969-1983.

  • [PDF] [Abstract] Hernaus, D., Gold, J.M., Waltz, J.A. & Frank, M.J. (2018). Impaired expected value computations coupled with overreliance on stimulus-response learning in schizophrenia. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.

  • [PDF] [Abstract] Waltz, J.A., Xu, Z., Brown, E.C., Ruiz, R.R., Frank, M.J. & Gold, J.M. (2018). Motivational deficits in schizophrenia are associated with reduced differentiation between gain and loss-avoidance feedback in the striatum. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3, 239-247.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E., Albrecht, M., Waltz, J.A., Gold, J.M. & Frank, M.J. (2017). Interactions between working memory, reinforcement learning and effort in value-based choice: A new paradigm and selective deficits in schizophrenia. Biological Psychiatry, 82, 431-439.

  • [PDF] [Abstract] Collins, A.G.E., Ciullo, B., Frank, M.J. & Badre, D. (2017). Working memory load strengthens reward prediction errors. Journal of Neuroscience, 37, 4332-4342.

  • [PDF] [Abstract] Swart, J.C., Frobose, M.I., Cook, J.L., Geurts, D.E.M., Frank, M.J., Cools, R. & den Ouden, H.E.M. (2017). Catecholaminergic challenge uncovers distinct Pavlovian and instrumental mechanisms of motivated (in)action. eLife, 6:e22169,.

  • [PDF] [Abstract] Kasanova, Z., Ceccarini, J., Frank, M.J., van Amelsvoort, T., Booij, J., Heinzel, A., Mottaghy, F. & Myin-Germeys, I. (2017). Striatal dopaminergic modulation of reinforcement learning predicts reward-oriented behavior in daily life. Biological Psychology, 127, 1-9.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2016). Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning. Cognition, 152, 160-169.

  • [PDF] [Abstract] Werchan, D.M., Collins, A.G.E., Frank, M.J. & Amso, D. (2016). Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants. Journal of Neuroscience, 36, 10314-10322.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2016). Motor demands constrain cognitive rule structures. PLoS Computational Biology, 12:e1004785,.

  • [PDF] [Abstract] Doll, B.B., Bath, K.G., Daw, N.D. & Frank, M.J. (2016). Variability in dopamine genes dissociates model-based and model-free reinforcement learning. Journal of Neuroscience, 36, 1211-1222.

  • [PDF] [Abstract] Gold, J.M., Waltz, J.W. & Frank, M.J. (2015). Effort cost computation in schizophrenia: A commentary on the recent literature. Biological Psychiatry, 78, 747-753.

  • [PDF] [Abstract] Morris, L., Baek, K., Kundu, P., Harrison, N.A., Frank, M.J. & Voon, V. (2016). Biases in the Explore-Exploit Tradeoff in Addictions: the Role of Avoidance of Uncertainty. Neuropsychopharmacology, 41, 940-948.

  • [PDF] [Abstract] Werchan, D.M., Collins, A.G.E., Frank, M.J. & Amso, D. (2015). 8-Month-Old Infants Spontaneously Learn and Generalize Hierarchical Rules. Psychological Science, 26, 805-815.

  • [PDF] [Abstract] Slagter, H.A., Georgopoulou, K. & Frank, M.J. (2015). Spontaneous eye blink rate predicts learning from negative, but not positive, outcomes. Neuropsychologia, 71, 126-132.

  • [PDF] [Abstract] Frank, M.J., Gagne, C., Nyhus, E., Masters, S., Wiecki, T.V., Cavanagh, J.F. & Badre, D. (2015). fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning. Journal of Neuroscience, 35, 484-494.

  • [PDF] [Abstract] Cox, S.M.L., Frank, M.J., Larcher, K., Fellows, L.K., Clark, C.A., Leyton, M. & Dagher, A. (2015). Striatal D1 and D2 signaling differentially predict learning from positive and negative outcomes. NeuroImage, 109, 95-101.

  • [Main PDF] [Supp PDF] [Abstract] Wiecki, T.V., Poland, J.S. & Frank, M.J. (2015). Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification. Clinical Psychological Science, 3, 378-399.

  • [PDF] [Abstract] Cavanagh, J.F., Masters, S.E., Bath, K. & Frank, M.J. (2014). Conflict acts as an implicit cost in reinforcement learning. Nature Communications, 5:5394,.

  • [PDF] [Abstract] Collins, A.G.E., Brown., J., Gold, J., Waltz, J. & Frank, M.J. (2014). Working memory contributions to reinforcement learning in schizophrenia. Journal of Neuroscience, 34, 13747-13756.

  • [Main PDF] [Supp PDF] [Abstract] Cockburn, J., Collins, A.G.E. & Frank, M.J. (2014). A reinforcement learning mechanism responsible for the valuation of free choice. Neuron, 83, 551-557.

  • [PDF] [Abstract] Solomon, M., Frank, M.J., Ragland, J.D., Smith, A.C., Niendam, T.A., Lesh, T.A., Grayson, D.S., Beck, J.S., Matter, J. & Carter, C.S. (2015). Feedback-driven trial-by-trial learning in autism spectrum disorders. American Journal of Psychiatry, 172, 173-181.

  • [PDF] [Abstract] Kayser, A., Mitchell, J.M., Weinstein, D. & Frank, M.J. (2015). Dopamine, locus of control, and the exploration-exploitation tradeoff. Neuropsychopharmacology, 40, 454-462.

  • [PDF] [Abstract] Collins, A.G.E., Cavanagh, J.F. & Frank, M.J. (2014). Human EEG uncovers latent generalizable task-set structure during learning. Journal of Neuroscience, 34, 4677-4685.

  • [PDF] [Abstract] Cavanagh, J.F., Sanguinetti, J.L., Allen, J.J.B., Sherman, S.J. & Frank, M.J. (2014). The subthalamic nucleus contributes to post-error slowing. Journal of Cognitive Neuroscience, 26, 2637-2644.

  • [PDF] [Abstract] Cavanagh, J.F., Wiecki, T.V., Kochar, A. & Frank, M.J. (2014). Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology: General, 143, 1476-1488.

  • [PDF] [Abstract] Doll, B.B., Waltz, J.A., Cockburn, J., K., B.J., Frank, M.J. & Gold, J.M. (2014). Reduced susceptibility to confirmation bias in schizophrenia. Cognitive, Affective and Behavioral Neuroscience, 14, 715-728.

  • [PDF] [Abstract] Narayanan, S.N., Cavanagh, J.F., Frank, M.J. & Laubach, M. (2013). Common medial frontal mechanisms of adaptive control in humans and rodents. Nature Neuroscience, 16, 1888-95.

  • [PDF] [Abstract] Cavanagh, J.F., Eisenberg, I., Guitart-Masip, M., Huys, Q. & Frank, M.J. (2013). Frontal theta overrides Pavlovian learning biases. Journal of Neuroscience, 33, 8541-8548.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2013). Cognitive control over learning: Creating, clustering and generalizing task-set structure. Psychological Review, 120, 190-229.

  • [PDF] [Abstract] Beeler, J.A., Frank, M.J., McDaid, J., Alexander, E., Turkson, S., Sol Bernandez, M., McGehee, D. & Zhuang, X. (2012). A role for dopamine-mediated learning in the pathophysiology and treatment of Parkinson's Disease. Cell Reports, 2, 1747-1761.

  • [PDF] [Abstract] Gold, J.M., Strauss, G.P., Waltz, J.A., Robinson, B.M., Brown, J.K. & Frank, M.J. (2013). Negative symptoms of schizophrenia are associated with abnormal effort-cost computations. Biological Psychiatry, 74, 130-136.

  • [Main PDF] [Supp PDF] [Abstract] Gold, J.M., Waltz, J.A., Matveeva, T.M., Kasanova, Z., Strauss, G.P., Herbener, E.S., Collins, A.G.E. & Frank, M.J. (2012). Negative symptoms and the failure to represent the expected reward value of actions: Behavioral and computational modeling evidence. Archives of General Psychiatry, 69, 129-138.

  • [PDF] [Abstract] Jahfari, S., Verbruggen, F., Frank, M., Waldorp, L., Colzato, L., Ridderinkhof, K. & Forstmann, B. (2012). How preparation changes the need for top-down control of the basal ganglia when inhibiting premature actions. Journal of Neuroscience, 32, 10870-8.

  • [PDF] [Abstract] Badre, D. & Frank, M.J. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 2: Evidence from fMRI. Cerebral Cortex, 22, 527-536.

  • [PDF] [Abstract] Doll, B.B., Hutchison, K.E. & Frank, M.J. (2011). Dopaminergic genes predict individual differences in susceptibility to confirmation bias. Journal of Neuroscience 6188-6198.

  • [PDF] [Abstract] Cavanagh, J.F., Bismark, A.J., Frank, M.J. & Allen, J.J.B. (2011). Larger error signals in major depression are associated with better avoidance learning. Frontiers in Psychology | Cognition, 2,.

  • [PDF] [Abstract] Strauss, G.P., Frank, M.J., Waltz, J.A., Kasanova, Z., Herbener, E.S. & Gold, J.M. (2011). Deficits in positive reinforcement learning and uncertainty-driven exploration are associated with distinct aspects of negative symptoms in schizophrenia. Biological Psychiatry, 69, 424-431.

  • [PDF] [Abstract] Cavanagh, J.F., Frank, M.J. & Allen, J.J.B. (2011). Social stress reactivity alters reward and punishment learning. Social Cognitive & Affective Neuroscience, 6, 311-320.

  • [PDF] [Abstract] Solomon, M., Frank, M.J., Smith, A.C., Ly, S. & Carter, C. (2011). Transitive inference in adults with autism spectrum disorders. Cognitive, Affective and Behavioral Neuroscience.

  • [PDF] [Abstract] Kasanova, Z., Waltz, J.A., Strauss, G.P., Frank, M.J. & Gold, J.M. (2011). Optimizing vs matching: Response strategy in a probabilistic learning task is associated with negative symptoms of schizophrenia. Schizophrenia Research, 127, 215-222.

  • [PDF] [Abstract] Waltz, J.A., Frank, M.J., Wiecki, T.V. & Gold, J.M. (2011). Altered probabilistic learning and response biases in schizophrenia: Behavioral evidence and neurocomputational modeling. Neuropsychology, 25,.

  • [PDF] [Abstract] Cavanagh, J.F., Frank, M.J., Klein, T.J. & Allen, J.J.B. (2010). Frontal theta links prediction errors to behavioral adaptation in reinforcement learning. NeuroImage, 49, 3198-3209.

  • [PDF] [Abstract] Cavanagh, J.F., Grundler, T.O.J., Frank, M.J. & Allen, J.J.B. (2010). Altered cingulate sub-region activation accounts for task-related dissociation in ERN amplitude as a function of obsessive-compulsive symptoms. Neuropsychologia, 48, 2098-2109.

  • [PDF] [Abstract] Robinson, O.J., Frank, M.J., Sahakian, B.J. & Cools, C. (2010). Dissociable responses to punishment in distinct striatal regions during reversal learning. NeuroImage 1459-1467.

  • [PDF] [Abstract] Strauss, G.P., Robinson, B.M., Waltz, J.A., Frank, M.J., Kasanova, Z., Herbener, E.S. & Gold, J.M. (2011). Patients with schizophrenia demonstrate inconsistent preference judgments for affective and nonaffective stimuli. Schizophrenia Bulletin, 37, 1295-1304.

  • [PDF] [Abstract] Chase, H.W., Frank, M.J., Michael, A., Bullmore, E.T., Sahakian, B. & Robbins, T.W. (2010). Approach and avoidance learning in patients with major depression and healthy controls: relation to anhedonia. Psychological Medicine, 40, 433-440.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Doll, B.B., Oas-Terpstra, J. & Moreno, F. (2009). Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation. Nature Neuroscience, 12, 1062-1068.

  • [PDF] [Abstract] Frank, M.J. & Hutchison, K.E. (2009). Genetic contributions to avoidance-based decisions: Striatal D2 receptor polymorphisms. Neuroscience, 164, 131-140.

  • [PDF] [Abstract] Gruendler, T.O.J., Cavanagh, J.F., Figueroa, C.M., Frank, M.J. & Allen, J.J.B. (2009). Task-related dissociation in ERN amplitude as a function of obsessive-compulsive symptoms. Neuropsychologia, 47, 1978-1987.

  • [Main PDF] [Supp PDF] [Abstract] Chatham, C.H., Frank, M.J. & Munakata, Y. (2009). Pupillometric and behavioral markers of a developmental shift in the temporal dynamics of cognitive control. Proceedings of the National Academy of Sciences, 106, 5529-5533.

  • [PDF] [Abstract] Doll, B.B., Jacobs, W.J., Sanfey, A.G. & Frank , M.J. (2009). Instructional control of reinforcement learning: A behavioral and neurocomputational investigation. Brain Research, 1299, 74-94.

  • [Main PDF] [Supp PDF] [Abstract] Cools, R., Frank, M.J., Gibbs, S.E., Miyakawa, A., Jagust, W. & D'Esposito, M. (2009). Striatal dopamine predicts outcome-specific reversal learning and its sensitivity to dopaminergic drug administration. Journal of Neuroscience, 29, 1538-1543.

  • [PDF] [Abstract] Moustafa, A.A., Cohen, M.X., Sherman, S.J. & Frank, M.J. (2008). A role for dopamine in temporal decision making and reward maximization in Parkinsonism. Journal of Neuroscience, 28, 12294-12304.

  • [PDF] [Abstract] Moustafa, A.A., Sherman, S.J. & Frank, M.J. (2008). A dopaminergic basis for working memory, learning and attentional shifting in Parkinsonism. Neuropsychologia, 46, 3144-3156.

  • [PDF] [Abstract] Santesso, D., Evins, A., Frank, M., Cowman, E. & Pizzagalli, D. (2009). Single dose of a dopamine agonist impairs reinforcement learning in humans: Converging evidence from electrophysiology and computational modeling of striatal-cortical function. Human Brain Mapping, 30, 1963--1976.

  • [PDF] [Abstract] Frank, M.J. & Kong, L. (2008). Learning to Avoid in Older Age. Psychology and Aging, 23, 392-398.

  • [PDF] [Abstract] Pizzagalli, D.A., Evins, A.E., Schetter, E.C., Frank, M.J., Pajtas, P.E., Santesso, D.L. & Culhane, M. (2008). Single dose of a dopamine agonist impairs reinforcement learning in humans: Behavioral evidence from a laboratory-based measure of reward responsiveness. Psychopharmacology, 196, 221--232.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Samanta, J., Moustafa, A.A. & Sherman, S.J. (2007). Hold your horses: Impulsivity, deep brain stimulation and medication in Parkinsonism. Science, 318, 1309-1312. [Journal Homepage]

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Moustafa, A.A., Haughey, H., Curran, T. & Hutchison, K. (2007). Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning. Proceedings of the National Academy of Sciences, 104, 16311-16316. [Journal Homepage]

  • [PDF] [Abstract] Frank, M.J., D'Lauro, C. & Curran, T. (2007). Cross-task individual differences in error processing: Neural, electrophysiological and genetic components. Cognitive, Affective and Behavioral Neuroscience, 7, 297-308.

  • [PDF] [Abstract] Frank, M.J., Santamaria, A., O'Reilly, R. & Willcutt, E. (2007). Testing computational models of dopamine and noradrenaline dysfunction in Attention Deficit/Hyperactivity Disorder. Neuropsychopharmacology, 32, 1583-99.

  • [Main PDF] [Commentary] [Abstract] Waltz, J.A., Frank, M.J., Robinson, B.M. & Gold, J.M. (2007). Selective reinforcement learning deficits in schizophrenia support predictions from computational models of striatal-cortical dysfunction. Biological Psychiatry, 62, 756-764.

  • [PDF] [Abstract] Aron, A.R., Behrens, T.E., Smith, S., Frank, M.J. & Poldrack, R.A. (2007). Triangulating a cognitive control network using diffusion-weighted MRI and functional MRI. Journal of Neuroscience, 27, 3743-52.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J. & O'Reilly, R.C. (2006). A mechanistic account of striatal dopamine function in human cognition: Psychopharmacological studies with cabergoline and haloperidol. Behavioral Neuroscience, 120, 497-517.

  • [PDF] [Abstract] Frank, M.J., OReilly, R.C. & Curran, T. (2006). When memory fails, intuition reigns: Midazolam enhances implicit inference in humans. Psychological Science, 17, 700-707.

  • [PDF] [Abstract] Frank, M.J., Rudy, J.W., Levy, W.B. & O'Reilly, R.C. (2005). When logic fails: Implicit transitive inference in humans. Memory and Cognition, 33, 742--750.

  • [PDF] [Abstract] Frank, M.J., Woroch, B.S. & Curran, T. (2005). Error-related negativity predicts reinforcement learning and conflict biases. Neuron, 47, 495-501.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Seeberger, L. & O'Reilly, R.C. (2004). By carrot or by stick: Cognitive reinforcement learning in Parkinsonism. Science, 306, 1940-1943.

Computational Psychiatry and Neurology

  • [PDF] [Abstract] Hitchcock, P.F., Fried, E.I. & Frank, M.J. (2022). Computational psychiatry needs time and context. Annual Reviews of Psychology, 73, 243-270.

  • [PDF] [Abstract] Pedersen, M.L., Ironside, M., Amemori, K., McGrath, C.M., Kang, M.S., Graybiel, A.M., Pizzagalli, D.A. & Frank, M.J. (2021). Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder. PLoS Computational Biology, 17(5),.

  • [PDF] [Abstract] Geana, A., Barch, D.M., Gold, J.M., Carter, C.S., MacDonald, A.W., Ragland, J.D., Silverstein, S.M. & Frank, M.J. (2021). Using computational modelling to capture schizophrenia-specific reinforcement learning differences and their implications on patient classification. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.

  • [PDF] [Abstract] Nassar, M.N., Waltz, J.A., Albrecht, M.A., Gold, J.M. & Frank, M.J. (2021). All or nothing belief updating in patients with schizophrenia reduces precision and flexibility of beliefs. Brain, 144, 1013-1029.

  • [PDF] [Abstract] Huys, Q.J.M., Browning, M., Paulus, M. & Frank, M.J. (2021). Advances in the computational understanding of mental illness. Neuropsychopharmacology Reviews, 46, 3-19.

  • [PDF] [Abstract] Lamba, A., Frank, M.J. & FeldmanHall, O. (2020). Anxiety impedes adaptive social learning under uncertainty. Psychological Science, 31, 592-603.

  • [PDF] [Abstract] Frey, A., Frank, M.J. & McCabe, C. (2019). Social reinforcement learning as a predictor of real-life experiences in individuals with high and low depressive symptomatology. Psychological Medicine.

  • [PDF] [Abstract] Ironside, M., Amemori, K., McGrath, C.L., Pedersen, M.L., Su Kang, M., Amemori, S., Frank, M.J., Graybiel, A.M. & Pizzagalli, D.A. (2020). Approach-avoidance conflict in major depression: Congruent neural findings in human and non-human primates. Biological Psychiatry, 87, 399-408.

  • [Main PDF] [Supp PDF] [Abstract] Lawlor, V.M., Webb, C.A., Wiecki, T.V., Frank, M.J., Trivedi, M., Pizzagalli, D.A. & Dillon, D.G. (2019). Dissecting the impact of depression on decision-making. Psychological Medicine.

  • [PDF] [Abstract] Cavanagh, J.F., Bismark, A., Frank, M.J. & Allen, J.J.B. (2018). Multiple Dissociations between Comorbid Depression and Anxiety on Reward and Punishment Processing: Evidence from Computationally Informed EEG. Computational Psychiatry, 1, 1-17.

  • [PDF] [Abstract] Patel, S.R., Herrington, T., Sheth, S.A., Mian, M., Bick, S.K., Yang, J.C., Flaherty, A.W., Frank, M.J., Widge, A.S., Dougherty, D. & Eskandar, E.N. (2018). Intermittent subthalamic nucleus deep brain stimulation induces risk-aversive behavior in human subjects. eLife, 7, e36460.

  • [PDF] [Abstract] Hernaus, D., Gold, J.M., Waltz, J.A. & Frank, M.J. (2018). Impaired expected value computations coupled with overreliance on stimulus-response learning in schizophrenia. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.

  • [PDF] [Abstract] Waltz, J.A., Xu, Z., Brown, E.C., Ruiz, R.R., Frank, M.J. & Gold, J.M. (2018). Motivational deficits in schizophrenia are associated with reduced differentiation between gain and loss-avoidance feedback in the striatum. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3, 239-247.

  • [PDF] [Abstract] Maia, T.V., Huys, Q.J.M. & Frank, M.J. (2017). Theory-based computational psychiatry. Biological Psychiatry, 82, 382-384.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E., Albrecht, M., Waltz, J.A., Gold, J.M. & Frank, M.J. (2017). Interactions between working memory, reinforcement learning and effort in value-based choice: A new paradigm and selective deficits in schizophrenia. Biological Psychiatry, 82, 431-439.

  • [PDF] [Abstract] Frank, M.J. (2016). Computational Cognitive Neuroscience Approaches to Deconstructing Mental Function and Dysfunction. A.D. Redish & J.D. Gordon (Eds) Computational Psychiatry: New Perspectives on Mental Illness. Strüngmann Forum Reports, vol. 20, J. Lupp, series editor, 101-120, Cambridge, MA: MIT Press.[Publisher] [Strüngmann Forum Reports]

  • [PDF] [Abstract] Kurth-Nelson, Z., O'Doherty, J.P., M., B.D., Denève, S., Durstewitz, D., Frank, M., Gordon, J., Mathew, S.J., Y., N., Ressler, K. & Tost, H. (2016). Computational Approaches for Studying Mechanisms of Psychiatric Disorders. A.D. Redish & J.D. Gordon (Eds) Computational Psychiatry: New Perspectives on Mental Illness. Strüngmann Forum Reports, vol. 20, J. Lupp, series editor, Cambridge, MA: MIT Press.[Publisher] [Strüngmann Forum Reports]

  • [PDF] [Abstract] Pedersen, M.L., Frank, M.J. & Biele, G. (2017). The drift diffusion model as the choice rule in reinforcement learning. Psychonomic Bulletin & Review, 24, 1234-1251.

  • [Main PDF] [Supp PDF] [Abstract] Maia, T.V. & Frank, M.J. (2017). An integrative perspective on the role of dopamine in schizophrenia. Biological Psychiatry, 81, 52-66.

  • [PDF] [Abstract] Huys, Q.J.M., Maia, T.V. & Frank, M.J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19, 404-413.

  • [PDF] [Abstract] Albrecht, M.A., Waltz, J.A., Cavanagh, J.F., Frank, M.J. & Gold, J. (2016). Reduction of Pavlovian bias in schizophrenia: Enhanced effects in clozapine-administered patients. PLoS ONE, 11:e0152781, 1-23.

  • [PDF] [Abstract] Wiecki, T.V., Antoniades, C.A., Stevenson, A., Kennard, C., Borowsky, B., Owen, G., Leavitt, B., Roos, R., Durr, A., Tabrizi, S.J. & Frank, M.J. (2016). A computational cognitive biomarker for early-stage Huntington's disease. PLoS ONE, 11:e0148409,.

  • [PDF] [Abstract] Stephan, K.E., Bach, D.R., Fletcher, P.C., Flint, J., Frank, M.J., Friston, K.J., Heinz, A., Huys, Q.J.M., Owen, M.J., Binder, E.B., Dayan, P., Johnstone, E.C., Meyer-Lindenberg, A., Montague, P.R., Schnyder, U., Wang, X.J. & Breakspear, M. (2016). Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis. The Lancet Psychiatry, 3, 77-83.

  • [PDF] [Abstract] Gold, J.M., Waltz, J.W. & Frank, M.J. (2015). Effort cost computation in schizophrenia: A commentary on the recent literature. Biological Psychiatry, 78, 747-753.

  • [PDF] [Abstract] Morris, L., Baek, K., Kundu, P., Harrison, N.A., Frank, M.J. & Voon, V. (2016). Biases in the Explore-Exploit Tradeoff in Addictions: the Role of Avoidance of Uncertainty. Neuropsychopharmacology, 41, 940-948.

  • [Main PDF] [Supp PDF] [Abstract] Wiecki, T.V., Poland, J.S. & Frank, M.J. (2015). Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification. Clinical Psychological Science, 3, 378-399.

  • [PDF] [Abstract] Collins, A.G.E., Brown., J., Gold, J., Waltz, J. & Frank, M.J. (2014). Working memory contributions to reinforcement learning in schizophrenia. Journal of Neuroscience, 34, 13747-13756.

  • [PDF] [Abstract] Doll, B.B., Waltz, J.A., Cockburn, J., K., B.J., Frank, M.J. & Gold, J.M. (2014). Reduced susceptibility to confirmation bias in schizophrenia. Cognitive, Affective and Behavioral Neuroscience, 14, 715-728.

  • [PDF] [Abstract] Beeler, J.A., Frank, M.J., McDaid, J., Alexander, E., Turkson, S., Sol Bernandez, M., McGehee, D. & Zhuang, X. (2012). A role for dopamine-mediated learning in the pathophysiology and treatment of Parkinson's Disease. Cell Reports, 2, 1747-1761.

  • [PDF] [Abstract] Gold, J.M., Strauss, G.P., Waltz, J.A., Robinson, B.M., Brown, J.K. & Frank, M.J. (2013). Negative symptoms of schizophrenia are associated with abnormal effort-cost computations. Biological Psychiatry, 74, 130-136.

  • [Main PDF] [Supp PDF] [Abstract] Gold, J.M., Waltz, J.A., Matveeva, T.M., Kasanova, Z., Strauss, G.P., Herbener, E.S., Collins, A.G.E. & Frank, M.J. (2012). Negative symptoms and the failure to represent the expected reward value of actions: Behavioral and computational modeling evidence. Archives of General Psychiatry, 69, 129-138.

  • [Main PDF] [Supp PDF] [Abstract] Cavanagh, J.F., Wiecki, T.V., Cohen, M.X., Figueroa, C.M., Samanta, J., Sherman, S.J. & Frank, M.J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14, 1462-1467.

  • [PDF] [Abstract] Maia, T.V. & Frank, M.J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14, 154-162.

  • [PDF] [Abstract] Cavanagh, J.F., Bismark, A.J., Frank, M.J. & Allen, J.J.B. (2011). Larger error signals in major depression are associated with better avoidance learning. Frontiers in Psychology | Cognition, 2,.

  • [PDF] [Abstract] Strauss, G.P., Frank, M.J., Waltz, J.A., Kasanova, Z., Herbener, E.S. & Gold, J.M. (2011). Deficits in positive reinforcement learning and uncertainty-driven exploration are associated with distinct aspects of negative symptoms in schizophrenia. Biological Psychiatry, 69, 424-431.

  • [PDF] [Abstract] Solomon, M., Smith, A.C., Frank, M.J., Ly, S. & Carter, C. (2011). Probabilistic reinforcement learning in adults with autism spectrum disorders. Autism Research.

  • [PDF] [Abstract] Kasanova, Z., Waltz, J.A., Strauss, G.P., Frank, M.J. & Gold, J.M. (2011). Optimizing vs matching: Response strategy in a probabilistic learning task is associated with negative symptoms of schizophrenia. Schizophrenia Research, 127, 215-222.

  • [PDF] [Abstract] Waltz, J.A., Frank, M.J., Wiecki, T.V. & Gold, J.M. (2011). Altered probabilistic learning and response biases in schizophrenia: Behavioral evidence and neurocomputational modeling. Neuropsychology, 25,.

  • [PDF] [Abstract] Wiecki, T.V. & Frank, M.J. (2010). Neurocomputational models of motor and cognitive deficits in Parkinson's disease. Progress in Brain Research, 183, 275-297.

  • [PDF] [Abstract] Cavanagh, J.F., Grundler, T.O.J., Frank, M.J. & Allen, J.J.B. (2010). Altered cingulate sub-region activation accounts for task-related dissociation in ERN amplitude as a function of obsessive-compulsive symptoms. Neuropsychologia, 48, 2098-2109.

  • [PDF] [Abstract] Strauss, G.P., Robinson, B.M., Waltz, J.A., Frank, M.J., Kasanova, Z., Herbener, E.S. & Gold, J.M. (2011). Patients with schizophrenia demonstrate inconsistent preference judgments for affective and nonaffective stimuli. Schizophrenia Bulletin, 37, 1295-1304.

  • [PDF] [Abstract] Chase, H.W., Frank, M.J., Michael, A., Bullmore, E.T., Sahakian, B. & Robbins, T.W. (2010). Approach and avoidance learning in patients with major depression and healthy controls: relation to anhedonia. Psychological Medicine, 40, 433-440.

  • [PDF] [Abstract] Gruendler, T.O.J., Cavanagh, J.F., Figueroa, C.M., Frank, M.J. & Allen, J.J.B. (2009). Task-related dissociation in ERN amplitude as a function of obsessive-compulsive symptoms. Neuropsychologia, 47, 1978-1987.

  • [PDF] [Abstract] Moustafa, A.A., Cohen, M.X., Sherman, S.J. & Frank, M.J. (2008). A role for dopamine in temporal decision making and reward maximization in Parkinsonism. Journal of Neuroscience, 28, 12294-12304.

  • [PDF] Frank, M.J. (2008). Schizophrenia: A computational reinforcement learning perspective. Schizophrenia Bulletin, 34, 1008-1011.

  • [PDF] [Abstract] Moustafa, A.A., Sherman, S.J. & Frank, M.J. (2008). A dopaminergic basis for working memory, learning and attentional shifting in Parkinsonism. Neuropsychologia, 46, 3144-3156.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Samanta, J., Moustafa, A.A. & Sherman, S.J. (2007). Hold your horses: Impulsivity, deep brain stimulation and medication in Parkinsonism. Science, 318, 1309-1312. [Journal Homepage]

  • [PDF] [Abstract] Frank, M.J., Santamaria, A., O'Reilly, R. & Willcutt, E. (2007). Testing computational models of dopamine and noradrenaline dysfunction in Attention Deficit/Hyperactivity Disorder. Neuropsychopharmacology, 32, 1583-99.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Scheres, A. & Sherman, S.J. (2007). Understanding decision making deficits in neurological conditions: Insights from models of natural action selection. Philosophical Transactions of the Royal Society - B, 362, 1641-1654. [Journal Homepage]

  • [Main PDF] [Commentary] [Abstract] Waltz, J.A., Frank, M.J., Robinson, B.M. & Gold, J.M. (2007). Selective reinforcement learning deficits in schizophrenia support predictions from computational models of striatal-cortical dysfunction. Biological Psychiatry, 62, 756-764.

  • [PDF] [Abstract] Frank, M.J. (2005). Dynamic dopamine modulation in the basal ganglia: A neurocomputational account of cognitive deficits in medicated and non-medicated Parkinsonism. Journal of Cognitive Neuroscience, 17, 51-72.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Seeberger, L. & O'Reilly, R.C. (2004). By carrot or by stick: Cognitive reinforcement learning in Parkinsonism. Science, 306, 1940-1943.

Reinforcement Learning and Decision Making in Basal Ganglia and Frontal Cortex

  • [PDF] [Abstract] Scott, D.N. & Frank, M.J. (2022). Adaptive conttol of synaptic plasticity integrates micro- and macroscopic network function. Neuropsychopharmacology Reviews.

  • [PDF] [Abstract] Moolchand, P., Jones, S.R. & Frank, M.J. (2022). Biophysical and Architectural Mechanisms of Subthalamic Theta under Response Conflict. Journal of Neuroscience, 42, 4470-4487.

  • [PDF] [Abstract] Calderon, C.B., Verguts, T. & Frank, M.J. (2022). Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits. PLoS Computational Biology e1009854.

  • [PDF] [Abstract] Hamid, A.A., Frank, M.J.*. & Moore, C.I.*. (2021). Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. *co-senior authors; alphabetical order. Cell, 184, 2733-2749.

  • [PDF] [Abstract] Westbrook, J.A., Frank, M.J. & Cools, R. (2021). A mosaic of cost-benefit control over cortico-striatal circuitry. Trends in Cognitive Sciences, 25, 710-721.

  • [PDF] [Abstract] Lehnert, L., Littman, M.L. & Frank, M.J. (2020). Reward-predictive representations generalize across tasks in reinforcement learning. PLOS Computational Biology, 16(10), e1008317.

  • [Main PDF] [Supp PDF] [Abstract] Westbrook, J., van den Bosch, R., Maatta, J.I., Hofmans, L., Papadopetraki, D., Cools, R.*. & Frank, M.J.*. (2020). Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. *co-senior authors. Science, 367, 1362-1366. [Journal Homepage]

  • [PDF] [Abstract] Pedersen, M.L. & Frank, M.J. (2020). Simultaneous hierarchical Bayesian parameter estimation for reinforcement learning and drift diffusion models: a tutorial and links to neural data. Computational Brain & Behavior, 3, 458-471.

  • [PDF] [Abstract] Patel, S.R., Herrington, T., Sheth, S.A., Mian, M., Bick, S.K., Yang, J.C., Flaherty, A.W., Frank, M.J., Widge, A.S., Dougherty, D. & Eskandar, E.N. (2018). Intermittent subthalamic nucleus deep brain stimulation induces risk-aversive behavior in human subjects. eLife, 7, e36460.

  • [PDF] [Abstract] Jahfari, S., Ridderinkhof, K.R., Collins, A.G.E., Knapen, T., Waldorp, L. & Frank, M.J. (2019). Cross-task contributions of frontobasal ganglia circuitry in response inhibition and conflict-induced slowing. Cerebral Cortex, 29, 1969-1983.

  • [PDF] [Abstract] Westbrook, J.A. & Frank, M.J. (2018). Dopamine and proximity in motivation and cognitive control. Current Opinion in Behavioral Sciences, 22, 28-34.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E., Albrecht, M., Waltz, J.A., Gold, J.M. & Frank, M.J. (2017). Interactions between working memory, reinforcement learning and effort in value-based choice: A new paradigm and selective deficits in schizophrenia. Biological Psychiatry, 82, 431-439.

  • [PDF] [Abstract] Collins, A.G.E., Ciullo, B., Frank, M.J. & Badre, D. (2017). Working memory load strengthens reward prediction errors. Journal of Neuroscience, 37, 4332-4342.

  • [PDF] [Abstract] Swart, J.C., Frobose, M.I., Cook, J.L., Geurts, D.E.M., Frank, M.J., Cools, R. & den Ouden, H.E.M. (2017). Catecholaminergic challenge uncovers distinct Pavlovian and instrumental mechanisms of motivated (in)action. eLife, 6:e22169,.

  • [PDF] [Abstract] Kasanova, Z., Ceccarini, J., Frank, M.J., van Amelsvoort, T., Booij, J., Heinzel, A., Mottaghy, F. & Myin-Germeys, I. (2017). Striatal dopaminergic modulation of reinforcement learning predicts reward-oriented behavior in daily life. Biological Psychology, 127, 1-9.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2016). Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning. Cognition, 152, 160-169.

  • [PDF] [Abstract] Franklin, N.T. & Frank, M.J. (2015). A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning. eLife, 4:e12029,.

  • [PDF] [Abstract] Doll, B.B., Bath, K.G., Daw, N.D. & Frank, M.J. (2016). Variability in dopamine genes dissociates model-based and model-free reinforcement learning. Journal of Neuroscience, 36, 1211-1222.

  • [PDF] [Abstract] Slagter, H.A., Georgopoulou, K. & Frank, M.J. (2015). Spontaneous eye blink rate predicts learning from negative, but not positive, outcomes. Neuropsychologia, 71, 126-132.

  • [PDF] [Abstract] Frank, M.J., Gagne, C., Nyhus, E., Masters, S., Wiecki, T.V., Cavanagh, J.F. & Badre, D. (2015). fMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning. Journal of Neuroscience, 35, 484-494.

  • [PDF] [Abstract] Cox, S.M.L., Frank, M.J., Larcher, K., Fellows, L.K., Clark, C.A., Leyton, M. & Dagher, A. (2015). Striatal D1 and D2 signaling differentially predict learning from positive and negative outcomes. NeuroImage, 109, 95-101.

  • [PDF] [Abstract] Frank, M.J. (2015). Linking across levels of computation in model-based cognitive neuroscience. B.U. Forstmann & E. Wagenmakers (Eds) An Introduction to Model-Based Cognitive Neuroscience, 163-181, New York: Springer.

  • [Main PDF] [Supp PDF] [Abstract] Cockburn, J., Collins, A.G.E. & Frank, M.J. (2014). A reinforcement learning mechanism responsible for the valuation of free choice. Neuron, 83, 551-557.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2014). Opponent Actor Learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. Psychological Review, 121, 337-366.

  • [PDF] [Abstract] Cavanagh, J.F., Sanguinetti, J.L., Allen, J.J.B., Sherman, S.J. & Frank, M.J. (2014). The subthalamic nucleus contributes to post-error slowing. Journal of Cognitive Neuroscience, 26, 2637-2644.

  • [PDF] [Abstract] Chatham, C., Frank, M.J. & Badre, D. (2014). Corticostriatal output gating during selection from working memory. Neuron, 81, 930-942.

  • [PDF] [Abstract] Cavanagh, J.F., Eisenberg, I., Guitart-Masip, M., Huys, Q. & Frank, M.J. (2013). Frontal theta overrides Pavlovian learning biases. Journal of Neuroscience, 33, 8541-8548.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2013). Cognitive control over learning: Creating, clustering and generalizing task-set structure. Psychological Review, 120, 190-229.

  • [PDF] [Abstract] Wiecki, T.V. & Frank, M.J. (2013). A computational model of inhibitory control in frontal cortex and basal ganglia. Psychological Review, 120, 329-355.

  • [PDF] [Abstract] Beeler, J.A., Frank, M.J., McDaid, J., Alexander, E., Turkson, S., Sol Bernandez, M., McGehee, D. & Zhuang, X. (2012). A role for dopamine-mediated learning in the pathophysiology and treatment of Parkinson's Disease. Cell Reports, 2, 1747-1761.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2012). How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. European Journal of Neuroscience, 35, 1024-1035.

  • [Main PDF] [Supp PDF] [Abstract] Cavanagh, J.F., Wiecki, T.V., Cohen, M.X., Figueroa, C.M., Samanta, J., Sherman, S.J. & Frank, M.J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14, 1462-1467.

  • [PDF] [Abstract] Ratcliff, R. & Frank, M.J. (2012). Reinforcement-based decision making in corticostriatal circuits: Mutual constraints by neurocomputational and diffusion models. Neural Computation, 24, 1186-1229.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J. & Badre, D. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: Computational analysis. Cerebral Cortex, 22, 509-526.

  • [PDF] [Abstract] Badre, D. & Frank, M.J. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 2: Evidence from fMRI. Cerebral Cortex, 22, 527-536.

  • [PDF] [Abstract] Doll, B.B., Hutchison, K.E. & Frank, M.J. (2011). Dopaminergic genes predict individual differences in susceptibility to confirmation bias. Journal of Neuroscience 6188-6198.

  • [PDF] [Abstract] Frank, M.J. (2011). Computational models of motivated action selection in corticostriatal circuits. Current Opinion in Neurobiology, 21, 381-386.

  • [PDF] [Abstract] Maia, T.V. & Frank, M.J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14, 154-162.

  • [PDF] [Abstract] Frank, M.J. & Fossella, J.A. (2011). Neurogenetics and pharmacology of learning, motivation and cognition. Neuropsychopharmacology Reviews, 36, 133-152.

  • [PDF] [Abstract] Cockburn, J. & Frank, M.J. (2011). Reinforcement learning, conflict monitoring, and cognitive control: An integrative model of cingulate-striatal interactions and the ERN. R. Mars, J. Sallet, M. Rushworth & N. Yeung (Eds) Neural Basis of Motivational and Cognitive Control, 311-331, Cambridge: The MIT Press.

  • [PDF] [Abstract] Solomon, M., Smith, A.C., Frank, M.J., Ly, S. & Carter, C. (2011). Probabilistic reinforcement learning in adults with autism spectrum disorders. Autism Research.

  • [PDF] [Abstract] Wietzikoski, E.C., Boschen, S.L., Miyoshi, E., Bortolanza, M., Mendes dos Santos, L., Frank, M.J., Brandao, M.L., Winn, P. & Da Cunha, C. (2012). Roles of D1-like dopamine receptors in the nucleus accumbens and dorsolateral striatum in conditioned avoidance responses. Psychopharmacology, 219, 159-69.

  • [PDF] [Abstract] Wiecki, T.V. & Frank, M.J. (2010). Neurocomputational models of motor and cognitive deficits in Parkinson's disease. Progress in Brain Research, 183, 275-297.

  • [PDF] [Abstract] Cavanagh, J.F., Frank, M.J., Klein, T.J. & Allen, J.J.B. (2010). Frontal theta links prediction errors to behavioral adaptation in reinforcement learning. NeuroImage, 49, 3198-3209.

  • [PDF] [Abstract] Robinson, O.J., Frank, M.J., Sahakian, B.J. & Cools, C. (2010). Dissociable responses to punishment in distinct striatal regions during reversal learning. NeuroImage 1459-1467.

  • [PDF] [Abstract] Samson, R.D., Frank, M.J. & Fellous, J. (2010). Computational models of reinforcement learning: the role of dopamine as a reward signal. Cognitive Neurodynamics, 4, 91-105.

  • [PDF] [Abstract] Hazy, T.E., Frank, M.J. & O'Reilly, R.C. (2010). Neural mechanisms of acquired phasic dopamine responses in learning. Neuroscience & Biobehavioral Reviews, 34, 701-720.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Doll, B.B., Oas-Terpstra, J. & Moreno, F. (2009). Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation. Nature Neuroscience, 12, 1062-1068.

  • [PDF] Daw, N.D. & Frank, M.J. (2009). Reinforcement learning and higher level cognition: Introduction to special issue. Cognition, 113, 259-261.

  • [PDF] Frank, M.J. & Surmeier, D.J. (2009). Do substantia nigra dopaminergic neurons differentiate between reward and punishment? (Commentary on Matsumoto & Hikosaka, 2009). Journal of Molecular Cell Biology, 1, 15-16.

  • [email me for PDF] [Abstract] Doll, B.B. & Frank, M.J. (2009). The basal ganglia in reward and decision making: computational models and empirical studies. J. Dreher & L. Tremblay (Eds) Handbook of Reward and Decision Making, 399-425, Oxford: Academic Press.

  • [PDF] [Abstract] Frank, M.J. & Hutchison, K.E. (2009). Genetic contributions to avoidance-based decisions: Striatal D2 receptor polymorphisms. Neuroscience, 164, 131-140.

  • [PDF] Frank, M.J. (2009). Slave to the striatal habit (Commentary on Tricomi et al, 2009). European Journal of Neuroscience, 29, 2223-2224.

  • [Main PDF] [Supp PDF] [Abstract] Wiecki, T.V., Riedinger, K., Meyerhofer, A., Schmidt, W. & Frank, M.J. (2009). A neurocomputational account of catalepsy sensitization induced by D2-receptor-blockade in rats: Context-dependency, extinction, and renewal. Psychopharmacology, 204, 265--277.

  • [PDF] [Abstract] Doll, B.B., Jacobs, W.J., Sanfey, A.G. & Frank , M.J. (2009). Instructional control of reinforcement learning: A behavioral and neurocomputational investigation. Brain Research, 1299, 74-94.

  • [PDF] [Abstract] Frank, M.J., Cohen, M.X. & Sanfey, A.G. (2009). Multiple systems in decision making: A neurocomputational perspective. Current Directions in Psychological Science, 18, 73--77.

  • [Main PDF] [Supp PDF] [Abstract] Cools, R., Frank, M.J., Gibbs, S.E., Miyakawa, A., Jagust, W. & D'Esposito, M. (2009). Striatal dopamine predicts outcome-specific reversal learning and its sensitivity to dopaminergic drug administration. Journal of Neuroscience, 29, 1538-1543.

  • [PDF] [Abstract] Cohen, M.X. & Frank, M.J. (2009). Neurocomputational models of basal ganglia function in learning, memory and choice. Behavioural Brain Research, 199, 141-156.

  • [PDF] [Abstract] Moustafa, A.A., Cohen, M.X., Sherman, S.J. & Frank, M.J. (2008). A role for dopamine in temporal decision making and reward maximization in Parkinsonism. Journal of Neuroscience, 28, 12294-12304.

  • [PDF] Frank, M.J. (2008). Schizophrenia: A computational reinforcement learning perspective. Schizophrenia Bulletin, 34, 1008-1011.

  • [PDF] [Abstract] Santesso, D., Evins, A., Frank, M., Cowman, E. & Pizzagalli, D. (2009). Single dose of a dopamine agonist impairs reinforcement learning in humans: Converging evidence from electrophysiology and computational modeling of striatal-cortical function. Human Brain Mapping, 30, 1963--1976.

  • [PDF] [Abstract] Frank, M.J. & Kong, L. (2008). Learning to Avoid in Older Age. Psychology and Aging, 23, 392-398.

  • [PDF] [Abstract] Pizzagalli, D.A., Evins, A.E., Schetter, E.C., Frank, M.J., Pajtas, P.E., Santesso, D.L. & Culhane, M. (2008). Single dose of a dopamine agonist impairs reinforcement learning in humans: Behavioral evidence from a laboratory-based measure of reward responsiveness. Psychopharmacology, 196, 221--232.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Samanta, J., Moustafa, A.A. & Sherman, S.J. (2007). Hold your horses: Impulsivity, deep brain stimulation and medication in Parkinsonism. Science, 318, 1309-1312. [Journal Homepage]

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Moustafa, A.A., Haughey, H., Curran, T. & Hutchison, K. (2007). Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning. Proceedings of the National Academy of Sciences, 104, 16311-16316. [Journal Homepage]

  • [PDF] [Abstract] Frank, M.J., D'Lauro, C. & Curran, T. (2007). Cross-task individual differences in error processing: Neural, electrophysiological and genetic components. Cognitive, Affective and Behavioral Neuroscience, 7, 297-308.

  • [PDF] [Abstract] Frank, M.J., Santamaria, A., O'Reilly, R. & Willcutt, E. (2007). Testing computational models of dopamine and noradrenaline dysfunction in Attention Deficit/Hyperactivity Disorder. Neuropsychopharmacology, 32, 1583-99.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Scheres, A. & Sherman, S.J. (2007). Understanding decision making deficits in neurological conditions: Insights from models of natural action selection. Philosophical Transactions of the Royal Society - B, 362, 1641-1654. [Journal Homepage]

  • [Main PDF] [Commentary] [Abstract] Waltz, J.A., Frank, M.J., Robinson, B.M. & Gold, J.M. (2007). Selective reinforcement learning deficits in schizophrenia support predictions from computational models of striatal-cortical dysfunction. Biological Psychiatry, 62, 756-764.

  • [PDF] [Abstract] Aron, A.R., Behrens, T.E., Smith, S., Frank, M.J. & Poldrack, R.A. (2007). Triangulating a cognitive control network using diffusion-weighted MRI and functional MRI. Journal of Neuroscience, 27, 3743-52.

  • [PDF] [Abstract] O'Reilly, R.C., Frank, M.J., Hazy, T.E. & Watz, B. (2007). PVLV: The Primary Value and Learned Value Pavlovian learning algorithm. Behavioral Neuroscience, 121, 31-49.

  • [PDF] [Abstract] Frank, M.J. (2006). Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making. Neural Networks, 19, 1120-1136.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J. & O'Reilly, R.C. (2006). A mechanistic account of striatal dopamine function in human cognition: Psychopharmacological studies with cabergoline and haloperidol. Behavioral Neuroscience, 120, 497-517.

  • [PDF] [Abstract] Frank, M.J. & Claus, E.D. (2006). Anatomy of a decision: Striato-orbitofrontal interactions in reinforcement learning, decision making and reversal. Psychological Review, 113, 300-326.

  • [PDF] [Abstract] Frank, M.J., OReilly, R.C. & Curran, T. (2006). When memory fails, intuition reigns: Midazolam enhances implicit inference in humans. Psychological Science, 17, 700-707.

  • [PDF] [Abstract] Frank, M.J. (2005). Dynamic dopamine modulation in the basal ganglia: A neurocomputational account of cognitive deficits in medicated and non-medicated Parkinsonism. Journal of Cognitive Neuroscience, 17, 51-72.

  • [PDF] [Abstract] Frank, M.J., Woroch, B.S. & Curran, T. (2005). Error-related negativity predicts reinforcement learning and conflict biases. Neuron, 47, 495-501.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J., Seeberger, L. & O'Reilly, R.C. (2004). By carrot or by stick: Cognitive reinforcement learning in Parkinsonism. Science, 306, 1940-1943.

  • [PDF] [Abstract] Frank, M.J. (2004). Dynamic dopamine modulation of striato-cortical circuits in cognition: Converging neuropsychological, psychopharmacological and computational studies. Phd Thesis, University of Colorado at Boulder, Boulder, CO

  • [PDF] [Abstract] Atallah, H.E., Frank, M.J. & O'Reilly, R.C. (2004). Hippocampus, cortex and basal ganglia: Insights from computational models of complementary learning systems. Neurobiology of Learning and Memory, 82/3, 253-67.

Working Memory and Cognitive Control in the Prefrontal Cortex and Basal Ganglia

  • [PDF] [Abstract] Brown, V.M., Hallquist, M.N., Frank, M.J. & Dombrovski, A.Y. (2022). Humans adaptively resolve the explore-exploit dilemma under cognitive constraints: Evidence from a multi-armed bandit task. Cognition, 229,.

  • [PDF] [Abstract] Rac-Lubashevsky, R. & Frank, M.J. (2021). Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence. PLoS Computational Biology, 17, e1008971.

  • [PDF] [Abstract] Westbrook, J.A., Frank, M.J. & Cools, R. (2021). A mosaic of cost-benefit control over cortico-striatal circuitry. Trends in Cognitive Sciences, 25, 710-721.

  • [Main PDF] [Supp PDF] [Abstract] Westbrook, J., van den Bosch, R., Maatta, J.I., Hofmans, L., Papadopetraki, D., Cools, R.*. & Frank, M.J.*. (2020). Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. *co-senior authors. Science, 367, 1362-1366. [Journal Homepage]

  • [PDF] [Abstract] Franklin, N.T. & Frank, M.J. (2020). Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning. PLOS Computational Biology, 16, e1007720.

  • [PDF] [Abstract] Nassar, M.R., Bruckner, R. & Frank, M.J. (2019). Statistical context dictates the relationship between feedback-related EEG signals and learning. eLife, 8, e46975.

  • [PDF] [Abstract] Franklin, N.T. & Frank, M.J. (2018). Compositional clustering in task structure learning. PLOS Computational Biology, 14(4), e1006116.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2018). Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory. Proceedings of the National Academy of Sciences, 115, 2502-2507.

  • [PDF] [Abstract] Nassar, M.R., Helmers, J. & Frank, M.J. (2018). Chunking as a rational strategy for lossy data compression in visual working memory. Psychological Review, 125, 486-511.

  • [PDF] [Abstract] Swart, J.C., Frank, M.J., Maatta, J.I., Jensen, O., Cools, R. & den Ouden, H.E.M. (2018). Frontal network dynamics reflect neurocomputational mechanisms for reducing maladaptive biases in motivated action. PLoS Biology, 16, e2005959.

  • [PDF] [Abstract] Jahfari, S., Ridderinkhof, K.R., Collins, A.G.E., Knapen, T., Waldorp, L. & Frank, M.J. (2019). Cross-task contributions of frontobasal ganglia circuitry in response inhibition and conflict-induced slowing. Cerebral Cortex, 29, 1969-1983.

  • [PDF] [Abstract] Westbrook, J.A. & Frank, M.J. (2018). Dopamine and proximity in motivation and cognitive control. Current Opinion in Behavioral Sciences, 22, 28-34.

  • [Main PDF] [Supp PDF] [Abstract] Collins, A.G.E., Albrecht, M., Waltz, J.A., Gold, J.M. & Frank, M.J. (2017). Interactions between working memory, reinforcement learning and effort in value-based choice: A new paradigm and selective deficits in schizophrenia. Biological Psychiatry, 82, 431-439.

  • [PDF] [Abstract] Collins, A.G.E., Ciullo, B., Frank, M.J. & Badre, D. (2017). Working memory load strengthens reward prediction errors. Journal of Neuroscience, 37, 4332-4342.

  • [PDF] [Abstract] Werchan, D.M., Collins, A.G.E., Frank, M.J. & Amso, D. (2016). Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants. Journal of Neuroscience, 36, 10314-10322.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2016). Motor demands constrain cognitive rule structures. PLoS Computational Biology, 12:e1004785,.

  • [PDF] [Abstract] Doll, B.B., Bath, K.G., Daw, N.D. & Frank, M.J. (2016). Variability in dopamine genes dissociates model-based and model-free reinforcement learning. Journal of Neuroscience, 36, 1211-1222.

  • [PDF] [Abstract] Kayser, A., Mitchell, J.M., Weinstein, D. & Frank, M.J. (2015). Dopamine, locus of control, and the exploration-exploitation tradeoff. Neuropsychopharmacology, 40, 454-462.

  • [PDF] [Abstract] Cavanagh, J.F. & Frank, M.J. (2014). Frontal theta as a mechanism for cognitive control. Trends in Cognitive Sciences, 18, 414-421.

  • [PDF] [Abstract] Collins, A.G.E., Cavanagh, J.F. & Frank, M.J. (2014). Human EEG uncovers latent generalizable task-set structure during learning. Journal of Neuroscience, 34, 4677-4685.

  • [PDF] [Abstract] Chatham, C., Frank, M.J. & Badre, D. (2014). Corticostriatal output gating during selection from working memory. Neuron, 81, 930-942.

  • [PDF] [Abstract] Narayanan, S.N., Cavanagh, J.F., Frank, M.J. & Laubach, M. (2013). Common medial frontal mechanisms of adaptive control in humans and rodents. Nature Neuroscience, 16, 1888-95.

  • [PDF] [Abstract] Cavanagh, J.F., Eisenberg, I., Guitart-Masip, M., Huys, Q. & Frank, M.J. (2013). Frontal theta overrides Pavlovian learning biases. Journal of Neuroscience, 33, 8541-8548.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2013). Cognitive control over learning: Creating, clustering and generalizing task-set structure. Psychological Review, 120, 190-229.

  • [PDF] [Abstract] Wiecki, T.V. & Frank, M.J. (2013). A computational model of inhibitory control in frontal cortex and basal ganglia. Psychological Review, 120, 329-355.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2012). How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. European Journal of Neuroscience, 35, 1024-1035.

  • [PDF] [Abstract] Cavanagh, J.F., Figueroa, C.M., Cohen, M.X. & Frank, M.J. (2012). Frontal theta reflects uncertainty and unexpectedness during exploration and exploitation. Cerebral Cortex 2575-86.

  • [Main PDF] [Supp PDF] [Abstract] Badre, D., Doll, B.B., Long, N.M. & Frank, M.J. (2012). Rostrolateral prefrontal cortex and individual differences in uncertainty-driven exploration. Neuron, 73, 595-607.

  • [PDF] [Abstract] Jahfari, S., Verbruggen, F., Frank, M., Waldorp, L., Colzato, L., Ridderinkhof, K. & Forstmann, B. (2012). How preparation changes the need for top-down control of the basal ganglia when inhibiting premature actions. Journal of Neuroscience, 32, 10870-8.

  • [Main PDF] [Supp PDF] [Abstract] Cavanagh, J.F., Wiecki, T.V., Cohen, M.X., Figueroa, C.M., Samanta, J., Sherman, S.J. & Frank, M.J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14, 1462-1467.

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J. & Badre, D. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: Computational analysis. Cerebral Cortex, 22, 509-526.

  • [PDF] [Abstract] Badre, D. & Frank, M.J. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 2: Evidence from fMRI. Cerebral Cortex, 22, 527-536.

  • [Main PDF] [Supp PDF] [Abstract] Chatham, C.H., Frank, M.J. & Munakata, Y. (2009). Pupillometric and behavioral markers of a developmental shift in the temporal dynamics of cognitive control. Proceedings of the National Academy of Sciences, 106, 5529-5533.

  • [PDF] [Abstract] Moustafa, A.A., Sherman, S.J. & Frank, M.J. (2008). A dopaminergic basis for working memory, learning and attentional shifting in Parkinsonism. Neuropsychologia, 46, 3144-3156.

  • [PDF] [Abstract] Frank, M.J., Santamaria, A., O'Reilly, R. & Willcutt, E. (2007). Testing computational models of dopamine and noradrenaline dysfunction in Attention Deficit/Hyperactivity Disorder. Neuropsychopharmacology, 32, 1583-99.

  • [Main PDF] [Supp PDF] [Abstract] Hazy, T.E., Frank, M.J. & O'Reilly, R.C. (2007). Toward an executive without a homunculus: Computational models of the prefrontal cortex/basal ganglia system. Philosophical Transactions of the Royal Society - B, 362, 1601-1613. [Journal Homepage]

  • [Main PDF] [Supp PDF] [Abstract] Frank, M.J. & O'Reilly, R.C. (2006). A mechanistic account of striatal dopamine function in human cognition: Psychopharmacological studies with cabergoline and haloperidol. Behavioral Neuroscience, 120, 497-517.

  • [PDF] [Abstract] Frank, M.J. & Claus, E.D. (2006). Anatomy of a decision: Striato-orbitofrontal interactions in reinforcement learning, decision making and reversal. Psychological Review, 113, 300-326.

  • [PDF] [Abstract] O'Reilly, R.C. & Frank, M.J. (2006). Making working memory work: A computational model of learning in the frontal cortex and basal ganglia. Neural Computation, 18, 283-328.

  • [PDF] [Abstract] Hazy, T.E., Frank, M.J. & O'Reilly, R.C. (2006). Banishing the homunculus: Making working memory work. Neuroscience, 139, 105--118.

  • [PDF] [Abstract] Frank, M.J. (2004). Dynamic dopamine modulation of striato-cortical circuits in cognition: Converging neuropsychological, psychopharmacological and computational studies. Phd Thesis, University of Colorado at Boulder, Boulder, CO

  • [PDF] [Abstract] Frank, M.J., Loughry, B. & O'Reilly, R.C. (2001). Interactions between the frontal cortex and basal ganglia in working memory: A computational model. Cognitive, Affective, and Behavioral Neuroscience, 1, 137-160.

Learning, Memory and Generalization in the Hippocampus

  • [PDF] [Abstract] Lehnert, L., Littman, M.L. & Frank, M.J. (2020). Reward-predictive representations generalize across tasks in reinforcement learning. PLOS Computational Biology, 16(10), e1008317.

  • [PDF] [Abstract] Frank, M.J., O'Reilly, R. & Curran, T. (2008). Midazolam, hippocampal function, and transitive inference: Reply to Greene. Behavioral and Brain Functions, 4, 5.

  • [PDF] [Abstract] Frank, M.J., OReilly, R.C. & Curran, T. (2006). When memory fails, intuition reigns: Midazolam enhances implicit inference in humans. Psychological Science, 17, 700-707.

  • [PDF] [Abstract] Frank, M.J., Rudy, J.W., Levy, W.B. & O'Reilly, R.C. (2005). When logic fails: Implicit transitive inference in humans. Memory and Cognition, 33, 742--750.

  • [PDF] [Abstract] Atallah, H.E., Frank, M.J. & O'Reilly, R.C. (2004). Hippocampus, cortex and basal ganglia: Insights from computational models of complementary learning systems. Neurobiology of Learning and Memory, 82/3, 253-67.

  • [PDF] [Abstract] Frank, M.J., Rudy, J.W. & O'Reilly, R.C. (2003). Transitivity, flexibility, conjunctive representations and the hippocampus: II. A computational analysis. Hippocampus, 13, 341-354.

Commentaries

  • [PDF] [Abstract] Maia, T.V., Huys, Q.J.M. & Frank, M.J. (2017). Theory-based computational psychiatry. Biological Psychiatry, 82, 382-384.

  • [PDF] [Abstract] Collins, A.G.E. & Frank, M.J. (2016). Surprise! Dopamine signals mix action, value and error. Nature Neuroscience, 19, 3-5.

  • [PDF] [Abstract] Badre, D., Frank, M.J. & Moore, C.I. (2015). Interactionist Neuroscience. Neuron, 88, 855-860.

  • [PDF] Daw, N.D. & Frank, M.J. (2009). Reinforcement learning and higher level cognition: Introduction to special issue. Cognition, 113, 259-261.

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