Michael J. Frank
Affiliations: | 2006-2008 | University of Arizona, Tucson, AZ | |
| 2009- | Brown University, Providence, RI |
Area:
computational models, basal ganglia, reinforcement learning, decision makingWebsite:
http://ski.clps.brown.edu/Google:
"Michael Frank"Mean distance: 13.61 (cluster 23) | S | N | B | C | P |
Cross-listing: Computational Biology Tree
Parents
Sign in to add mentor| Randall C. O'Reilly | grad student | 1999-2004 | CU Boulder | |
| (Dynamic dopamine modulation of striato-cortical circuits in cognition: Converging neuropsychological, psychopharmacological and computational studies.) | ||||
| Tim Curran | post-doc | 2004-2005 | CU Boulder | |
Children
Sign in to add trainee| Wasita Mahaphanit | research assistant | 2018- | Brown |
| Thomas V. Wiecki | grad student | Brown | |
| Alana Jaskir | grad student | 2018- | |
| Meghan Gallo | grad student | 2020- | Brown |
| Shikhar Kumar | grad student | 2007-2009 | University of Arizona |
| Nicholas T Franklin | grad student | 2011-2017 | Brown |
| Harrison Ritz | grad student | 2016-2021 | Brown |
| Guillaume Pagnier | grad student | 2019-2024 | |
| Ahmed A. Moustafa | post-doc | Rutgers, New Brunswick | |
| Matt R. Nassar | post-doc | Brown | |
| Anne GE Collins | post-doc | 2010- | Brown |
| Michael X. Cohen | post-doc | 2008-2009 | Aniversity of Arizona |
| James F. Cavanagh | post-doc | 2010-2013 | Brown |
BETA: Related publications
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Publications
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| Russin J, Pavlick E, Frank MJ. (2024) Human Curriculum Effects Emerge with In-Context Learning in Neural Networks. Arxiv |
| Pagnier GJ, Asaad WF, Frank MJ. (2024) Double dissociation of dopamine and subthalamic nucleus stimulation on effortful cost/benefit decision making. Current Biology : Cb |
| Culbreth AJ, Moran EK, Mahaphanit W, et al. (2023) A Transdiagnostic Study of Effort-Cost Decision-Making in Psychotic and Mood Disorders. Schizophrenia Bulletin |
| Jaskir A, Frank MJ. (2023) On the normative advantages of dopamine and striatal opponency for learning and choice. Elife. 12 |
| Rac-Lubashevsky R, Cremer A, Collins A, et al. (2023) Neural index of reinforcement learning predicts improved stimulus-response retention under high working memory load. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience |
| Liu RG, Frank MJ. (2022) Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning. Artificial Intelligence. 312 |
| Barch DM, Boudewyn MA, Carter CC, et al. (2022) Cognitive [Computational] Neuroscience Test Reliability and Clinical Applications for Serious Mental Illness (CNTRaCS) Consortium: Progress and Future Directions. Current Topics in Behavioral Neurosciences |
| Fengler A, Bera K, Pedersen ML, et al. (2022) Beyond Drift Diffusion Models: Fitting a Broad Class of Decision and Reinforcement Learning Models with HDDM. Journal of Cognitive Neuroscience. 1-26 |
| Provenza NR, Gelin LFF, Mahaphanit W, et al. (2021) Honeycomb: a template for reproducible psychophysiological tasks for clinic, laboratory, and home use. Revista Brasileira De Psiquiatria (Sao Paulo, Brazil : 1999) |
| Diekhof EK, Geana A, Ohm F, et al. (2021) The Straw That Broke the Camel's Back: Natural Variations in 17β-Estradiol and COMT-Val158Met Genotype Interact in the Modulation of Model-Free and Model-Based Control. Frontiers in Behavioral Neuroscience. 15: 658769 |