My research 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 empirical 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]
Humans actively reconfigure neural task states
Ritz, H., Jha, A., Pillow, J.W., Daw, N.D., & Cohen, J.D.
[preprint]
Phantom controllers: Misspecified models create the false appearance of adaptive control during value-based choice
Ritz, H.+, Frömer, R.+, & Shenhav, A. (+ equal)
[preprint]
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]
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]
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]
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 Decision and Control
[pdf]
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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