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• Nat. Hum.
  Behav. 2025

  Capturing the complexity of human strategic decision-making with machine learning

  Zhu, Peterson, Enke, Griffiths

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• Nature 2025

  A foundation model to predict and capture human cognition

  Binz, ..., Peterson, et al.

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• Nat. Hum.
  Behav. 2025

  Predicting human decisions with behavioral theories and machine learning

  Plonsky, ..., Peterson, et al.

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• ICLR 2025

  Large language models assume people are more rational than we really are

  Liu, Geng, Peterson, Sucholutsky, Griffiths

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• UAI 2023

  On the informativeness of supervision signals

  Sucholutsky, Battleday, Collins, Marjieh, Peterson, Singh, Bhatt, Jacoby, Weller, Griffiths

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• PNAS 2022

  Deep models of superficial face judgments

  Peterson, Uddenberg, Griffiths, Todorov, Suchow

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• Science 2021

  Using large-scale experiments and machine learning to discover theories of human decision-making

  Peterson, Bourgin, Agrawal, Reichman, Griffiths

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• Nat. Comms
  2020

  Capturing human categorization of natural images by combining deep networks and cognitive models

  Battleday*, Peterson*, Griffiths

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• PNAS 2020

  Scaling up psychology via Scientific Regret Minimization

  Agrawal, Peterson, Griffiths

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• ICCV 2019

  Human uncertainty makes classification more robust

  Peterson*, Battleday*, Griffiths, Russakovsky

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• ICML 2019

  Cognitive model priors for predicting human decisions

  Bourgin*, Peterson*, Reichman, Griffiths, Russell

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• ICCV 2015

  What makes an object memorable?

  Dubey*, Peterson*, Khosla, Yang, Ghanem

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• Decision
  2024

  Machine learning for modeling human decisions

  Reichman, Peterson, Griffiths

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• JEP: Gen.
  2024

  The Universal Law of Generalization holds for naturalistic stimuli

  Marjieh, Jacoby, Peterson, Griffiths

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• JEP: Gen.
  2023

  Stress, intertemporal choice, and mitigation behavior during the COVID-19 pandemic

  Agrawal, Peterson, Cohen, Griffiths

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• CogSci
  2023

  To each their own theory: Exploring the limits of individual differences in decisions under risk

  Peterson, Mancoridis, Griffiths

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• Cog. Sci.
  2023

  Extracting low-dimensional psychological representations from convolutional neural networks

  Jha, Peterson, Griffiths

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• Ann. NYAS
  2021

  From convolutional neural networks to models of higher-level cognition (and back again)

  Battleday*, Peterson*, Griffiths

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• Cognition
  2020

  Parallelograms revisited: Exploring the limitations of vector space models for simple analogies

  Peterson*, Chen*, Griffiths

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• Cog. Sci.
  2018

  Evaluating (and improving) the correspondence between deep neural networks and human representations

  Peterson, Abbott, Griffiths

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