Harrison Ritz

How do we change our state of mind?

My research explores the dynamics of cognitive control — e.g., attention, decision strategies, or task sets — though the lens of optimal control theory. I approach these questions through a combination of empirical experimentats (e.g., psychophysics, fMRI OPM-MEG, SQUID-MEG) and computational modelling (e.g., evidence accumulation, neural network modeling, state space inference, inverse optimal control). [Curriculum Vitae]

I completed my BSc at Queen’s University and my MSc at University of Western Ontario, both working with Ingrid Johnsrude. During my PhD at Brown University, I worked with Amitai Shenhav, Michael J. Frank, and Matthew Nassar. I recently completed a C.V. Starr Fellowship at the Princeton Neuroscience Institute, working with Jonathan Cohen and Nathaniel Daw.

I am currently a Connected Minds Fellow at Queen’s Univeristy, working with Gunnar Blohm, and a visting fellow at the Rotman Research Institute, working with Björn Herrmann and Douglas Cheyne.

Publications

Preprints

• Convergent neural dynamical systems for task control in artificial networks and human brains
  Ritz, H., Jha, A., Daw, N.D., & Cohen, J.D.
  [preprint] [StateSpaceAnalysis.jl]

• Ten Simple Rules for AI-Assisted Coding in Science.
  Bridgeford, E., Campbell, I.D., Chen, Z., Lin, Z., Ritz, H., Vandekerckhove, J., Poldrack. R.A. [middle authors alphabetical]
  [preprint] [ebook]

Published

• Misspecified models create the appearance of adaptive control during value-based choice
  Ritz, H., Frömer, R., & Shenhav, A. (2026) Communications Psychology
  [pdf] [web] [supplementary materials] [code]

• Adaptive behavior is guided by integrated representations of controlled and non-controlled information
  Huang, B., Ritz, H., Jiang, J. (2025) eLife
  [pdf] [web]

• Orthogonal neural encoding of targets and distractors supports multivariate cognitive control
  Ritz, H. & Shenhav, A. (2024b) Nature Human Behaviour
  [pdf] [web] [supplementary materials] [code] [data]

• Humans reconfigure target and distractor processing to address distinct task demands
  Ritz, H. & Shenhav, A. (2024a) Psychological Review
  [pdf] [web] [supplementary materials] [code + data]

• Test-Retest Reliability of the Human Connectome: An OPM-MEG study
  Rier, L., Michelmann, S., Ritz, H., Shah, V., Hill, R.M., Osborne, J., Doyle, C., Holmes, N., Bowtell, R., Brookes, M.J., Norman, K.A., Hasson, U., Cohen, J.D., Boto, E. (2023) Imaging Neuroscience
  [pdf] [web] [code + data]

• Parametric cognitive load reveals hidden costs in the neural processing of perfectly intelligible degraded speech
  Ritz, H., Wild, C., & Johnsrude, I. (2022) Journal of Neuroscience
  [pdf] [web]

• Humans can navigate complex graph structures acquired during latent learning
  Rmus, M., Ritz, H., Hunter, L.E., Bornstein, A.M., & Shenhav, A. (2022) Cognition
  [pdf] [web] [supplementary materials]

• Cognitive control as a multivariate optimization problem
  Ritz, H., Leng, X., & Shenhav, A. (2022) Journal of Cognitive Neuroscience
  [pdf] [web]

• Dissociable influences of reward and punishment on adaptive cognitive control
  Leng, X., Yee, D., Ritz, H., & Shenhav, A. (2021) PLOS Computational Biology
  [pdf] [web] [supplementary materials] [code] [data]

• Bridging motor and cognitive control: It’s about time! (Spotlight)
  Ritz, H., Frömer, R. & Shenhav, A. (2020) Trends in Cognitive Sciences
  [pdf] [web]

• Dissociable forms of uncertainty-driven representational change across the human brain
  Nassar, M.R., McGuire, J.T., Ritz, H., & Kable, J. (2019) Journal of Neuroscience
  [pdf] [web]

• A control theoretic model of adaptive behavior in dynamic environments
  Ritz, H., Nassar, M.R., Frank, M.J., & Shenhav, A. (2018) Journal of Cognitive Neuroscience
  [pdf] [web]

Conference Proceedings

• Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations
  Geadah, V., Arbelaiz, J., Ritz, H., Daw, N., Cohen, J.D., Pillow J. (2024) IEEE Conference on Decision and Control
  [pdf] [web]

• Dynamic neural control of task representations in humans and neural networks
  Ritz, H., Jha, A., Pillow, J., Daw, N., & Cohen J.D. (2024) Cognitive Computational Neuroscience
  

• Task preparation is reflected in neural state space dynamics
  Ritz, H., Jha, A., Pillow, J., & Cohen J.D. (2023) Cognitive Computational Neuroscience
  

• Continuous and Discrete Transitions during Task-Switching
  Ritz, H., Wolf, W., & Cohen J.D. (2023) Cognitive Science Society
  [pdf] [poster]

• Orthogonal neural encoding of targets and distractors supports cognitive control
  Ritz, H. & Shenhav, A. (2022) Cognitive Computational Neuroscience

• An evidence accumulation model of motivational and developmental influences over sustained attention
  Ritz, H., DeGutis, J., Frank M.J., Esterman, M., & Shenhav, A. (2020) Cognitive Science Society
  [pdf]

• Dissociable influences of reward and punishment on adaptive cognitive control
  Leng, X., Ritz, H., Yee, D., & Shenhav, A. (2020) Cognitive Science Society

• Parametric control of distractor-oriented attention
  Ritz, H. & Shenhav, A. (2019) Cognitive Science Society

• Decisions about reward and effort for the learning and control of dynamical systems
  Ritz, H., Nassar, M.R., Frank, M.J., & Shenhav, A. (2019) Reinforcement Learning and Decision Making
  [pdf]

• Behavioral evidence for PID-like feedback control
  Ritz, H., Nassar, M.R., Frank, M.J., & Shenhav, A. (2017) Reinforcement Learning and Decision Making

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