Research projects

  • Primate-like decision making in deep RL

    Primate-like perceptual decision making through deep recurrent reinforcement learning

    Nathan Wispinski, Scott Stone, Anthony Singhal, Patrick Pilarski, & Craig Chapman

    Society for Neuroscience talk (2022)


    We show that human-like movements and decision-making abilities emerge in deep neural networks trained using reinforcement learning. But only with the right ingredients—some theory from neuroscience and evolutionary biology.

  • Where do we put the body?

    Hold it! Where do we put the body?

    Nathan Wispinski, James Enns, & Craig Chapman

    Behavioral and Brain Sciences (2023)


    We argue that for a full understanding of the psychology of ownership, we need to consider humans as embodied agents.

  • Adaptive patch foraging in deep RL

    Adaptive patch foraging in deep reinforcement learning agents

    Nathan Wispinski, Andrew Butcher, Kory Mathewson, Craig Chapman, Matthew Botvinick, & Patrick Pilarski

    Transactions on Machine Learning Research (2023)


    During my time at Google DeepMind, we trained deep neural networks to forage. These artificial agents learned to forage adaptively like many animals. They also learned neural patterns that looked like those recorded from foraging primates.

  • Meta-reinforcement learning

    Meta-reinforcement learning

    Nathan Wispinski


    A short, conceptual replication of Prefrontal cortex as a meta-reinforcement learning system in the Jax ecosystem.

  • Delayed reaching retinotopy in EEG

    Delayed reaching retinotopy

    Nathan Wispinski, Scott Stone, Craig Chapman, & Anthony Singhal


    We simultaneously used motion tracking, eye tracking, and high-density EEG to see how the brain reactivates neural patterns to make actions.

  • Reaching for the known unknowns: Rapid reach decisions accurately reflect the future state of dynamic probabilistic information

    Reaching for the known unknowns: Rapid reach decisions accurately reflect the future state of dynamic probabilistic information

    Nathan Wispinski, Scott Stone, Jennifer Bertrand, Alexandra Ouellette Zuk, Ewen Lavoie, Jason Gallivan, & Craig Chapman

    Cortex (2021)


    By tracking high-speed reaching movements, we show that humans use predictions to optimally guide their actions.

  • Selective attention to real world objects drives their emotional appraisal

    Selective attention to real world objects drives their emotional appraisal

    Nathan Wispinski, Shihao Lin, James Enns, & Craig Chapman

    Attention, Perception, & Psychophysics (2021)


    We show that simply attending to 3D objects makes you like those objects more.

  • Models, movements, and minds: Bridging the gap between decision making and action

    Models, movements, and minds: Bridging the gap between decision making and action

    Nathan Wispinski, Jason Gallivan, & Craig Chapman

    Annals of the New York Academy of Sciences [The Year in Cognitive Neuroscience series] (2018)


    In this review, we argue that decision making and motor control are much more intimately linked in the brain than many researchers think.

  • Examining the “species” of situated cognition in humans

    Examining the “species” of situated cognition in humans

    Ewen Lavoie, Jennifer Bertrand, Scott Stone, Nathan Wispinski, Jeff Sawalha, & Craig Chapman

    Comparative Cognition & Behavior Reviews (2018)


    In this review, we discuss distributed, extended, and embodied cognition in humans.

  • Entrainment of theta, not alpha, oscillations is predictive of the brightness enhancement of a flickering stimulus

    Entrainment of theta, not alpha, oscillations is predictive of the brightness enhancement of a flickering stimulus

    Jennifer Bertrand, Nathan Wispinski, Kyle Mathewson, & Craig Chapman

    Scientific Reports (2018)


    In 1864, Brücke found that lights flickering at specific frequencies seemed brighter than others. In 2018, we showed that this strange effect is related to neural oscillations associated with information transfer.

  • Reaching reveals that best-versus-rest processing contributes to biased decision making

    Reaching reveals that best-versus-rest processing contributes to biased decision making

    Nathan Wispinski, Grace Truong, Todd Handy, & Craig Chapman

    Acta Psychologica (2017)


    We show that humans are biased toward the "best" option much more than classic economic theory predicts.

  • Seeing wealth as a responsibility improves attitudes towards taxation

    Seeing wealth as a responsibility improves attitudes towards taxation

    Ashley Whillans, Nathan Wispinski, & Elizabeth Dunn

    Journal of Economic Behavior & Organization (2016)


    Studies show that people disproportionaly dislike taxation. In the lab, we let undergraduates earn money before taxing this income. Using some psychological priming, we found a way for taxation to feel less painful.

  • The snooze of lose: Rapid reaching reveals that losses are processed more slowly than gains

    The snooze of lose: Rapid reaching reveals that losses are processed more slowly than gains

    Craig Chapman, Jason Gallivan, Jeremy Wong, Nathan Wispinski, & James Enns

    Journal of Experimental Psychology: General (2015)


    Using motion tracking when people make decisions under extreme time pressure, we show that humans process rewards faster than losses.

Nathan Wispinski © 2024