Harrison Ritz

How do we change our state of mind?

My reserach explores the dynamics of cognitive control — e.g., attention, decision strategies, or task sets — though the lens of optimal control theory. I try to make progress on these questions through a combination of emperical experiments (e.g., psychophysics, fMRI, EEG/OPM) and computational modelling (e.g., evidence accumulation, neural network modeling, state space inference, inverse optimal control).

I completed my BSc at Queen’s University and my MSc at University of Western Ontario, working with Ingrid Johnsrude. During my PhD at Brown University I worked with Amitai Shenhav, Michael J. Frank, and Matthew Nassar.

I am currently a C.V. Starr Fellow at the Princeton Neuroscience Institute, working with Jonathan Cohen, Nathaniel Daw, and Jonathan Pillow.
[Curriculum Vitae]

Preprints

• Phantom controllers: Misspecified models create the false appearance of adaptive control during value-based choice
  Ritz, H.+, Frömer, R.+, & Shenhav, A. (+ equal)
  [preprint]

Journal Publications

2024

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

2023

• Humans reconfigure target and distractor processing to address distinct task demands
  Ritz, H. & Shenhav, A. (2023)
  Psychological Review
  [pdf] [web] [supplementary materials] [data + code]

• 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] [data + code]

2022

• 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]

2018 - 2021

• 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]

• 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 & Extended Abstracts

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

• 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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