Michael J. Frank

Orcid: 0000-0001-8451-0523

Affiliations:
  • Brown University, Brown Institute for Brain Science, Providence, RI, USA


According to our database1, Michael J. Frank authored at least 28 papers between 2005 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2024
Human Curriculum Effects Emerge with In-Context Learning in Neural Networks.
CoRR, 2024

Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks.
CoRR, 2024

2023
Neural scaling laws for phenotypic drug discovery.
CoRR, 2023

Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning.
CoRR, 2023

2022
Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.
PLoS Comput. Biol., 2022

Beyond Drift Diffusion Models: Fitting a Broad Class of Decision and Reinforcement Learning Models with HDDM.
J. Cogn. Neurosci., 2022

Reward-Predictive Clustering.
CoRR, 2022

Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning.
Artif. Intell., 2022

2021
Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence.
PLoS Comput. Biol., 2021

Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder.
PLoS Comput. Biol., 2021

2020
Reward-predictive representations generalize across tasks in reinforcement learning.
PLoS Comput. Biol., 2020

Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning.
PLoS Comput. Biol., 2020

An evidence accumulation model of motivational and developmental influences over sustained attention.
Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

Encoder-Decoder Neural Architectures for Fast Amortized Inference of Cognitive Process Models.
Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.
PLoS Comput. Biol., 2019

2018
Compositional clustering in task structure learning.
PLoS Comput. Biol., 2018

A Control Theoretic Model of Adaptive Learning in Dynamic Environments.
J. Cogn. Neurosci., 2018

2016
Motor Demands Constrain Cognitive Rule Structures.
PLoS Comput. Biol., 2016

A Neural Correlate of Strategic Exploration at the Onset of Adolescence.
J. Cogn. Neurosci., 2016

2015
Striatal D1 and D2 signaling differentially predict learning from positive and negative outcomes.
NeuroImage, 2015

2014
The Subthalamic Nucleus Contributes to Post-error Slowing.
J. Cogn. Neurosci., 2014

2013
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.
Frontiers Neuroinformatics, 2013

2012
Reinforcement-Based Decision Making in Corticostriatal Circuits: Mutual Constraints by Neurocomputational and Diffusion Models.
Neural Comput., 2012

2010
Dissociable responses to punishment in distinct striatal regions during reversal learning.
NeuroImage, 2010

Frontal theta links prediction errors to behavioral adaptation in reinforcement learning.
NeuroImage, 2010

2006
Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making.
Neural Networks, 2006

Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia.
Neural Comput., 2006

2005
Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism.
J. Cogn. Neurosci., 2005


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