Michael Frank

About

Michael J. Frank, PhD, is a Professor in the Department of Cognitive and Psychological Sciences at Brown University and serves as the Neuroscience Graduate Program Director. He is affiliated with the Nancy G Zimmerman Center for Computational Brain Science and the Carney Institute for Brain Science, as well as the Department of Psychiatry and Human Behavior. His research integrates computational modeling and experimental approaches to investigate the neural mechanisms underlying reinforcement learning, decision making, and cognitive control. Specifically, his work focuses on developing neural circuit and algorithmic models that simulate interactions between brain areas such as the prefrontal cortex and basal ganglia, with an emphasis on dopamine modulation. These models are tested through neuropsychological, pharmacological, genetic, and imaging techniques, primarily EEG. Michael Frank's educational background includes a PhD in Neuroscience and Psychology from the University of Colorado at Boulder, where he was part of the Computational Cognitive Neuroscience Lab, an MS in Electrical and Computer Engineering with a biomedicine option from the same institution, and a BSc in Electrical Engineering from Queen's University in Canada. He teaches courses related to computational cognitive neuroscience and motivated decision making, reflecting his expertise in computational approaches to understanding brain function and behavior.

Research topics

• Artificial Intelligence
• Computer Science
• Psychology
• Neuroscience
• Cognitive psychology
• Clinical psychology
• Social psychology
• Mathematics
• Systems engineering
• Engineering

Selected publications

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

  Science · 2020 · 370 citations

  Senior authorCorresponding

  Stimulants such as methylphenidate are increasingly used for cognitive enhancement but precise mechanisms are unknown. We found that methylphenidate boosts willingness to expend cognitive effort by altering the benefit-to-cost ratio of cognitive work. Willingness to expend effort was greater for participants with higher striatal dopamine synthesis capacity, whereas methylphenidate and sulpiride, a selective D2 receptor antagonist, increased cognitive motivation more for participants with lower s…

  DOI

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

  Brain · 2020 · 73 citations

  Senior authorCorresponding

  Schizophrenia is characterized by abnormal perceptions and beliefs, but the computational mechanisms through which these abnormalities emerge remain unclear. One prominent hypothesis asserts that such abnormalities result from overly precise representations of prior knowledge, which in turn lead beliefs to become insensitive to feedback. In contrast, another prominent hypothesis asserts that such abnormalities result from a tendency to interpret prediction errors as indicating meaningful change,…

  DOI

• Managing EEG studies: How to prepare and what to do once data collection has begun

  Psychophysiology · 2023 · 25 citations

  In this paper, we provide guidance for the organization and implementation of EEG studies. This work was inspired by our experience conducting a large-scale, multi-site study, but many elements could be applied to any EEG project. Section 1 focuses on study activities that take place before data collection begins. Topics covered include: establishing and training study teams, considerations for task design and piloting, setting up equipment and software, development of formal protocol documents,…

  PublisherOA PDFDOI

• Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning

  Proceedings of the National Academy of Sciences · 2025-08-28 · 6 citations

  articleOpen accessSenior authorCorresponding

  Human learning embodies a striking duality: Sometimes, we can rapidly infer and compose logical rules, benefiting from structured curricula (e.g., in formal education), while other times, we rely on an incremental approach or trial-and-error, learning better from curricula that are randomly interleaved. Influential psychological theories explain this seemingly conflicting behavioral evidence by positing two qualitatively different learning systems-one for rapid, rule-based inferences (e.g., in w…

  PublisherOA PDFDOI

• Striatal dopamine can enhance both fast working memory, and slow reinforcement learning, while reducing implicit effort cost sensitivity

  Nature Communications · 2025-07-09 · 5 citations

  articleOpen access

  Abstract Associations can be learned incrementally, via reinforcement learning (RL), or stored instantly in working memory (WM). While WM is fast, it is also capacity-limited and effortful. Striatal dopamine may promote WM, by facilitating WM updating and effort exertion and also RL, by boosting plasticity. Yet, prior studies have failed to distinguish between the effects of dopamine manipulations on RL versus WM. N = 100 participants completed a paradigm isolating these systems in a double-blin…

  PublisherOA PDFDOI

Recent grants

• Electrophysiological and Computational studies on action monitoring

  NSF · $644k · 2011–2015

• Training Program for Interactionist Cognitive Neuroscience (ICoN)

  NIH · $1.4M · 2019–2029

• How prefrontal cortex augments reinforcement learning

  NSF · $593k · 2015–2019

Frequent coauthors

• Blas Larrauri

  ASM International

  85 shared

• Anne Collins

  University of California, Berkeley

  83 shared

• Samuel Luyasu

  Centre Hospitalier de Luxembourg

  81 shared

• Iris Leibovich‐Nassi

  Barzilai Medical Center

  81 shared

• James F. Cavanagh

  78 shared

• James M. Gold

  70 shared

• Matthew R. Nassar

  Allen Institute for Brain Science

  69 shared

• C. Garren Hester

  67 shared

Awards & honors

• Kavli Fellow (2016)
• Cognitive Neuroscience Society Young Investigator Award (201…
• Janet T Spence Award for early career transformative contrib…
• DG Marquis award for best paper published in Behavioral Neur…

Similar researchers at Brown University

• Thomas Serreh-145
• Bertram F. Malleh-65
• David Badreh-59