Evan Russek

Human decision-making is remarkably efficient. My research investigates both the mechanisms underlying this efficiency and the trade-offs it entails. To measure the cognitive processes and mental representations that support efficient decision-making, I utilize massive naturalistic datasets of complex decisions and employ computational approaches to neuroimaging. I aim to leverage these insights to understand the vulnerabilities in our decision-making, which may play out in mental-health conditions.

I'm currently a postdoc in the Computational Cognitive Science Lab at Princeton University, with Tom Griffiths. Previously, I was a postdoc at The Max Planck UCL Centre for Computational Psychiatry and Ageing Research at University College London, with Quentin Huys. I completed my PhD in Neural Science at New York University, with Nathaniel Daw.

CV / Google Scholar / Email: erussek [at] princeton [dot] edu

Submitted Manuscripts


Learning in online chess increases with time spent thinking and diversity of experience

Lisa Schut, Evan M. Russek, Ionatan Kuperwajs, Marcelo G. Mattar, Wei Ji Ma, Thomas L. Griffiths


Exploring resource-rational planning under time pressure in online chess

Ionatan Kuperwajs, Evan M. Russek, Lisa Schut, Yotam Sagiv, Marcelo G. Mattar,  Wei Ji Ma, Thomas L. Griffiths


Looking deeper into the algorithms underlying human planning

Ionatan Kuperwajs, Evan M. Russek, Marcelo G. Mattar,  Wei Ji Ma, Thomas L. Griffiths


Centaur: a foundation model of human cognition

Marcel Binz, [many authors including Evan M. Russek], Eric Schulz

PsyArXiv


Learning from rewards and social information in naturalistic strategic behavior

Ionatan Kuperwajs, Bas van Opheusden, Evan M. Russek, Thomas L. Griffiths

PsyArxiv


Consolidation of Sequential Planning

Oliver M. Vikbladh, Evan M. Russek, Neil Burgess

bioRxiv


Resolving Feynman’s Restaurant Problem

Brian Christian, Evan M. Russek, Thomas L. Griffiths

Time spent thinking in online chess reflects the value of computation
Evan M. Russek, Dan Acosta-Kane, Bas van Opheusden, Marcelo G. Mattar*, Thomas L. Griffiths*
PsyArXiv

Neural evidence for the successor representation in choice evaluation

Evan M. Russek, Ida Momennejad, Matthew M. Botvinick, Samuel J. Gershman, Nathaniel D. Daw

bioRxiv

Publications


Inverting cognitive models with neural networks to infer preferences from fixations

Evan M. Russek, Frederick Callaway, Thomas L. Griffiths

Cognitive Science (2024)


Modeling the Contributions of Capacity and Control to Working Memory Development
Evan M. Russek, Cameron R. Turner, Emma McEwen, Andrea M. Miscov, Amanda Seed, Thomas L. Griffiths
Proceedings of the Annual Meeting of the Cognitive Science Society (2024)

Heuristics in risky decision-making relate to preferential representation of information
Evan M. Russek, Rani Moran, Yunzhe Liu, Raymond J. Dolan, Quentin J. M. Huys
Nature Communications (2024)

Components of Behavioral Activation Therapy for Depression Engage Specific Reinforcement Learning Mechanisms in a Pilot Study
Quentin J. M. Huys, Evan M. Russek, George Abitante, Thorsten Kahnt, Jacqueline K. Gollan
Computational Psychiatry (2022)

Humans Perseverate on Punishment Avoidance Goals in Multi-Goal Reinforcement Learning
Paul B. Sharp*, Evan M. Russek*, Quentin J. M. Huys, Raymond J. Dolan, Eran Eldar
Elife (2022)

Opportunities for emotion and mental health research in the resource-rationality framework
Evan M. Russek, Rani Moran, Daniel McNamee, Andrea M.F. Reiter, Yunzhe Liu, Raymond J. Dolan, Quentin J. M. Huys
Behavioral and Brain Sciences (2020)

Predictive representations can link model-based reinforcement learning to model-free mechanisms
Evan M. Russek*, Ida Momennejad*, Matthew M. Botvinick, Samuel J. Gershman, Nathaniel D. Daw
PLOS Computational Biology (2017)

The successor representation in human reinforcement learning
Ida Momennejad*, Evan M. Russek*, Jin H. Cheong, Matthew M. Botvinick, Nathaniel D. Daw, Samuel J. Gershman
Nature Human Behaviour (2017)

* denotes equal contribution