Extracting the dynamics of behavior in sensory decision-making experiments.
Autor: | Roy NA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA. Electronic address: nicholas.roy.42@gmail.com., Bak JH; Korea Institute for Advanced Study, Seoul 02455, South Korea; Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA 94720, USA., Akrami A; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Sainsbury Wellcome Centre, University College London, London W1T 4JG, UK., Brody CD; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Howard Hughes Medical Institute, Princeton University, Princeton, NJ 08544, USA., Pillow JW; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Department of Psychology, Princeton University, Princeton, NJ 08544, USA. Electronic address: pillow@princeton.edu. |
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Jazyk: | angličtina |
Zdroj: | Neuron [Neuron] 2021 Feb 17; Vol. 109 (4), pp. 597-610.e6. Date of Electronic Publication: 2021 Jan 06. |
DOI: | 10.1016/j.neuron.2020.12.004 |
Abstrakt: | 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. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2020 Elsevier Inc. All rights reserved.) |
Databáze: | MEDLINE |
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