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...

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Published inNeuron (Cambridge, Mass.) Vol. 109; no. 4; pp. 597 - 610.e6
Main Authors Roy, Nicholas A., Bak, Ji Hyun, Akrami, Athena, Brody, Carlos D., Pillow, Jonathan W.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 17.02.2021
Elsevier Limited
Subjects
Acoustic Stimulation - methods
Adult
Animals
Auditory discrimination learning
Auditory Perception - physiology
Behavior
behavioral dynamics
Bias
Decision making
Decision Making - physiology
Experiments
Female
Generalized linear models
Humans
Laboratories
Learning
Male
Mice
Mice, Inbred C57BL
Photic Stimulation - methods
Psychomotor Performance - physiology
psychophysics
Quantitative psychology
Rats
Rats, Long-Evans
Reaction Time - physiology
sensory decision making
Sensory integration
Sensory stimuli
Statistical analysis
Variables
Visual Perception - physiology
Young Adult
sensory decision making
behavioral dynamics
learning
psychophysics
Online AccessGet full text
ISSN0896-6273
1097-4199
1097-4199
DOI10.1016/j.neuron.2020.12.004

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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. [Display omitted] •Dynamic model for time-varying sensory decision-making behavior•Visualize changes in behavioral strategies of mice, rats, and humans across training•Infer how quickly different parameters change between trials and between sessions•Colab notebook reproduces all figures and analyses, facilitating application to new data 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.
AbstractList 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, fit 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, and 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, & humans.
SummaryDecision-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.
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.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.
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.
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. [Display omitted] •Dynamic model for time-varying sensory decision-making behavior•Visualize changes in behavioral strategies of mice, rats, and humans across training•Infer how quickly different parameters change between trials and between sessions•Colab notebook reproduces all figures and analyses, facilitating application to new data 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.
Author Roy, Nicholas A.
Akrami, Athena
Bak, Ji Hyun
Brody, Carlos D.
Pillow, Jonathan W.
AuthorAffiliation d Sainsbury Wellcome Centre, University College London, London W1T 4JG, UK
e Howard Hughes Medical Institute, Princeton University, Princeton, NJ 08544, USA
f Dept. of Psychology, Princeton University, Princeton, NJ 08544, USA
b Korea Institute for Advanced Study, Seoul 02455, South Korea
g Current address: DeepMind, London N1C 4AG, UK
i Lead Contact
c Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA 94720, USA
a Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
h Current address: University of California, San Francisco, San Francisco, CA 94158, USA
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behavioral dynamics
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psychophysics
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AUTHOR CONTRIBUTIONS
Conceptualization, N.A.R., J.H.B., J.W.P.; Methodology, N.A.R., J.H.B., J.W.P.; Software, N.A.R.; Formal Analysis, N.A.R.; Investigation, N.A.R., I.B.L., A.A.; Resources, I.B.L., A.A., C.D.B., J.W.P.; Data Curation, N.A.R., I.B.L., A.A.; Writing – Original Draft, N.A.R.; Writing – Review & Editing, N.A.R., J.H.B., I.B.L., A.A., C.D.B., J.W.P.; Visualization, N.A.R.; Supervision, J.W.P.; Project Administration, N.A.R., J.W.P.; Funding Acquisition, I.B.L., J.W.P.
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Snippet Decision-making strategies evolve during training and can continue to vary even in well-trained animals. However, studies of sensory decision-making tend to...
SummaryDecision-making strategies evolve during training and can continue to vary even in well-trained animals. However, studies of sensory decision-making...
Decision-making strategies evolve during training, and can continue to vary even in well-trained animals. However, studies of sensory decision-making tend to...
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StartPage 597
SubjectTerms Acoustic Stimulation - methods
Adult
Animals
Auditory discrimination learning
Auditory Perception - physiology
Behavior
behavioral dynamics
Bias
Decision making
Decision Making - physiology
Experiments
Female
Generalized linear models
Humans
Laboratories
Learning
Male
Mice
Mice, Inbred C57BL
Photic Stimulation - methods
Psychomotor Performance - physiology
psychophysics
Quantitative psychology
Rats
Rats, Long-Evans
Reaction Time - physiology
sensory decision making
Sensory integration
Sensory stimuli
Statistical analysis
Variables
Visual Perception - physiology
Young Adult
Title Extracting the dynamics of behavior in sensory decision-making experiments
URI https://dx.doi.org/10.1016/j.neuron.2020.12.004
https://www.ncbi.nlm.nih.gov/pubmed/33412101
https://www.proquest.com/docview/2490569352
https://www.proquest.com/docview/2476568605
https://pubmed.ncbi.nlm.nih.gov/PMC7897255
Volume 109
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