Extracting the dynamics of behavior in sensory decision-making experiments

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 flexi...

Full description

Saved in:
Bibliographic Details
Published in:Neuron (Cambridge, Mass.) Vol. 109; no. 4; p. 597
Main Authors: Roy, Nicholas A, Bak, Ji Hyun, Akrami, Athena, Brody, Carlos D, Pillow, Jonathan W
Format: Journal Article
Language:English
Published: United States 17.02.2021
Subjects:
ISSN:1097-4199, 1097-4199
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1097-4199
1097-4199
DOI:10.1016/j.neuron.2020.12.004