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Michael J. Frank
Person information
- affiliation: Brown University, Brown Institute for Brain Science, Providence, RI, USA
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2020 – today
- 2024
- [i5]Aaron Traylor, Jack Merullo, Michael J. Frank, Ellie Pavlick:
Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks. CoRR abs/2402.08211 (2024) - [i4]Jacob Russin
, Ellie Pavlick, Michael J. Frank:
Human Curriculum Effects Emerge with In-Context Learning in Neural Networks. CoRR abs/2402.08674 (2024) - 2023
- [i3]Lakshmi Narasimhan Govindarajan, Rex G. Liu, Drew Linsley, Alekh Karkada Ashok, Max Reuter, Michael J. Frank, Thomas Serre:
Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning. CoRR abs/2309.13181 (2023) - [i2]Drew Linsley, John Griffin, Jason Parker Brown, Adam N. Roose, Michael J. Frank, Peter Linsley, Steven Finkbeiner, Jeremy Linsley:
Neural scaling laws for phenotypic drug discovery. CoRR abs/2309.16773 (2023) - 2022
- [j21]Rex G. Liu
, Michael J. Frank
:
Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning. Artif. Intell. 312: 103770 (2022) - [j20]Alexander Fengler
, Krishn Bera, Mads L. Pedersen, Michael J. Frank:
Beyond Drift Diffusion Models: Fitting a Broad Class of Decision and Reinforcement Learning Models with HDDM. J. Cogn. Neurosci. 34(10): 1780-1805 (2022) - [j19]Cristian Buc Calderon
, Tom Verguts
, Michael J. Frank
:
Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits. PLoS Comput. Biol. 18(2) (2022) - [i1]Lucas Lehnert, Michael J. Frank, Michael L. Littman:
Reward-Predictive Clustering. CoRR abs/2211.03281 (2022) - 2021
- [j18]Mads L. Pedersen
, Maria Ironside
, Ken-ichi Amemori
, Callie L. McGrath, Min S. Kang, Ann M. Graybiel
, Diego A. Pizzagalli, Michael J. Frank
:
Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder. PLoS Comput. Biol. 17(5) (2021) - [j17]Rachel Rac-Lubashevsky
, Michael J. Frank
:
Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence. PLoS Comput. Biol. 17(6) (2021) - 2020
- [j16]Nicholas T. Franklin
, Michael J. Frank
:
Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning. PLoS Comput. Biol. 16(4) (2020) - [j15]Lucas Lehnert
, Michael L. Littman, Michael J. Frank
:
Reward-predictive representations generalize across tasks in reinforcement learning. PLoS Comput. Biol. 16(10) (2020) - [c2]Alexander Fengler, Lakshmi Narasimhan Govindarajan, Michael J. Frank:
Encoder-Decoder Neural Architectures for Fast Amortized Inference of Cognitive Process Models. CogSci 2020 - [c1]Harrison Ritz, Joe DeGutis, Michael Joshua Frank, Michael Esterman, Amitai Shenhav:
An evidence accumulation model of motivational and developmental influences over sustained attention. CogSci 2020
2010 – 2019
- 2019
- [j14]Payam Piray
, Amir Dezfouli, Tom Heskes
, Michael J. Frank, Nathaniel D. Daw
:
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Comput. Biol. 15(6) (2019) - 2018
- [j13]Harrison Ritz
, Matthew R. Nassar
, Michael Joshua Frank, Amitai Shenhav:
A Control Theoretic Model of Adaptive Learning in Dynamic Environments. J. Cogn. Neurosci. 30(10) (2018) - [j12]Nicholas T. Franklin
, Michael Joshua Frank:
Compositional clustering in task structure learning. PLoS Comput. Biol. 14(4) (2018) - 2016
- [j11]Andrew S. Kayser, Zdena A. Op de Macks, Ronald E. Dahl
, Michael Joshua Frank:
A Neural Correlate of Strategic Exploration at the Onset of Adolescence. J. Cogn. Neurosci. 28(2): 199-209 (2016) - [j10]Anne Gabrielle Eva Collins, Michael Joshua Frank:
Motor Demands Constrain Cognitive Rule Structures. PLoS Comput. Biol. 12(3) (2016) - 2015
- [j9]Sylvia M. L. Cox, Michael Joshua Frank, Kevin Larcher, Lesley K. Fellows
, Crystal A. Clark, Marco Leyton, Alain Dagher:
Striatal D1 and D2 signaling differentially predict learning from positive and negative outcomes. NeuroImage 109: 95-101 (2015) - 2014
- [j8]James F. Cavanagh, Joseph L. Sanguinetti, John J. B. Allen, Scott J. Sherman, Michael Joshua Frank:
The Subthalamic Nucleus Contributes to Post-error Slowing. J. Cogn. Neurosci. 26(11): 2637-2644 (2014) - 2013
- [j7]Thomas V. Wiecki, Imri Sofer, Michael Joshua Frank:
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Frontiers Neuroinformatics 7: 14 (2013) - 2012
- [j6]Roger Ratcliff, Michael Joshua Frank:
Reinforcement-Based Decision Making in Corticostriatal Circuits: Mutual Constraints by Neurocomputational and Diffusion Models. Neural Comput. 24(5): 1186-1229 (2012) - 2010
- [j5]James F. Cavanagh
, Michael Joshua Frank, Theresa J. Klein, John J. B. Allen:
Frontal theta links prediction errors to behavioral adaptation in reinforcement learning. NeuroImage 49(4): 3198-3209 (2010) - [j4]Oliver J. Robinson
, Michael Joshua Frank, Barbara J. Sahakian
, Roshan Cools
:
Dissociable responses to punishment in distinct striatal regions during reversal learning. NeuroImage 51(4): 1459-1467 (2010)
2000 – 2009
- 2006
- [j3]Randall C. O'Reilly
, Michael Joshua Frank:
Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia. Neural Comput. 18(2): 283-328 (2006) - [j2]Michael Joshua Frank:
Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making. Neural Networks 19(8): 1120-1136 (2006) - 2005
- [j1]Michael Joshua Frank:
Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism. J. Cogn. Neurosci. 17(1): 51-72 (2005)
Coauthor Index

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last updated on 2024-10-07 21:16 CEST by the dblp team
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