Touchscreen Response Precision Is Sensitive to the Explore/Exploit Trade-off

Dana Mueller, Erin Giglio, Cathy S. Chen, Aspen Holm, R. Becket Ebitz, Nicola M. Grissom

Research output: Contribution to journalArticlepeer-review

Abstract

The explore/exploit trade-off is a fundamental property of choice selection during reward-guided decision making, where the “same” choice can reflect either of these internal cognitive states. An unanswered question is whether the execution of a decision provides an underexplored measure of internal cognitive states. Touchscreens are increasingly used across species for cognitive testing and afford the ability to measure the precise location of choice touch responses. We examined how male and female mice in a restless bandit decision making task interacted with a touchscreen to determine if the explore/exploit trade-off, prior reward, and/or sex differences change the variability in the kinetics of touchscreen choices. During exploit states, successive touch responses are closer together than those made in an explore state, suggesting exploit states reflect periods of increased motor stereotypy. Although exploit decisions might be expected to be rewarded more frequently than explore decisions, we find that immediate past reward reduces choice variability independently of explore/exploit state. Male mice are more variable in their interactions with the touchscreen than females, even in low-variability trials such as exploit or following reward. These results suggest that as exploit behavior emerges in reward-guided decision making, all mice become less variable and more automated in both their choice and the actions taken to make that choice, but this occurs on a background of increased male variability. These data uncover the hidden potential for touchscreen decision making tasks to uncover the latent neural states that unite cognition and movement.

Original languageEnglish (US)
Article numberENEURO.0538-24.2025
JournaleNeuro
Volume12
Issue number5
DOIs
StatePublished - May 2025

Bibliographical note

Publisher Copyright:
© 2025, Society for Neuroscience. All rights reserved.

Keywords

  • bandit
  • hidden Markov model (HMM)
  • reinforcement learning
  • sex differences
  • touchscreen

PubMed: MeSH publication types

  • Journal Article

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