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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Vydáno v:Neuron (Cambridge, Mass.) Ročník 109; číslo 4; s. 597
Hlavní autoři: Roy, Nicholas A, Bak, Ji Hyun, Akrami, Athena, Brody, Carlos D, Pillow, Jonathan W
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 17.02.2021
Témata:
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
sensory decision making
behavioral dynamics
learning
psychophysics
ISSN:1097-4199, 1097-4199
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1097-4199
1097-4199
DOI:10.1016/j.neuron.2020.12.004