@article{b512c83854d14ce7a82a5e97a5e31dad,
title = "Extracting the dynamics of behavior in sensory decision-making experiments",
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. Roy et al. present a method for inferring the time course of behavioral strategies in sensory decision-making tasks, which they use to analyze how behavior evolves during training in rats, mice, and humans.",
keywords = "behavioral dynamics, learning, psychophysics, sensory decision making",
author = "{The International Brain Laboratory} and Roy, {Nicholas A.} and Bak, {Ji Hyun} and Athena Akrami and Brody, {Carlos D.} and Pillow, {Jonathan W.}",
note = "Funding Information: The authors thank A. Churchland, A. Pouget, M. Carandini, A. Urai, and Y. Niv for helpful feedback on the manuscript. We also thank K. Osorio and J. Teran for animal and laboratory support in collecting the rat high-throughput data. This work was supported by grants from the Wellcome Trust (209558 and 216324) and the Simons Foundation, to the IBL and the Simons Collaboration on the Global Brain (SCGB AWD543027), the NIH BRAIN Initiative (NS104899, R01EB026946), and a U19 NIH-NINDS BRAIN Initiative Award (5U19NS104648) (N.A.R. and J.W.P.). Conceptualization, N.A.R. J.H.B. and J.W.P.; Methodology, N.A.R. J.H.B. and J.W.P.; Software, N.A.R.; Formal Analysis, N.A.R.; Investigation, N.A.R. the IBL, and A.A.; Resources, the IBL, A.A. C.D.B. and J.W.P.; Data Curation, N.A.R. the IBL, and A.A.; Writing – Original Draft, N.A.R.; Writing – Review & Editing, N.A.R. J.H.B. the IBL, A.A. C.D.B. and J.W.P.; Visualization, N.A.R.; Supervision, J.W.P.; Project Administration, N.A.R. and J.W.P.; Funding Acquisition, the IBL and J.W.P. The authors declare no competing interests. Publisher Copyright: {\textcopyright} 2020 Elsevier Inc.",
year = "2021",
month = feb,
day = "17",
doi = "10.1016/j.neuron.2020.12.004",
language = "English (US)",
volume = "109",
pages = "597--610.e6",
journal = "Neuron",
issn = "0896-6273",
publisher = "Cell Press",
number = "4",
}