Extracting the dynamics of behavior in sensory decision-making experiments

• Published in: Neuron (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
• Language: English
• 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
• ISSN: 0896-6273; 1097-4199; 1097-4199
• DOI: 10.1016/j.neuron.2020.12.004

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.