Extracting the dynamics of behavior in sensory decision-making experiments

Neuron. 2021 Feb 17;109(4):597-610.e6. doi: 10.1016/j.neuron.2020.12.004. Epub 2021 Jan 6.

Abstract

Decision-making strategies evolve during training and can continue to vary even in well-trained animals. However, studies of sensory decision-making tend to characterize behavior in terms of a fixed psychometric function that is fit only after training is complete. Here, we present PsyTrack, a flexible method for inferring the trajectory of sensory decision-making strategies from choice data. We apply PsyTrack to training data from mice, rats, and human subjects learning to perform auditory and visual decision-making tasks. We show that it successfully captures trial-to-trial fluctuations in the weighting of sensory stimuli, bias, and task-irrelevant covariates such as choice and stimulus history. This analysis reveals dramatic differences in learning across mice and rapid adaptation to changes in task statistics. PsyTrack scales easily to large datasets and offers a powerful tool for quantifying time-varying behavior in a wide variety of animals and tasks.

Keywords: behavioral dynamics; learning; psychophysics; sensory decision making.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acoustic Stimulation / methods
  • Adult
  • Animals
  • Auditory Perception / physiology*
  • Decision Making / physiology*
  • Female
  • Humans
  • Male
  • Mice
  • Mice, Inbred C57BL
  • Photic Stimulation / methods
  • Psychomotor Performance / physiology*
  • Rats
  • Rats, Long-Evans
  • Reaction Time / physiology*
  • Visual Perception / physiology*
  • Young Adult