Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.
Korea Institute for Advanced Study, Seoul 02455, South Korea; Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA 94720, USA.
Neuron. 2021 Feb 17;109(4):597-610.e6. doi: 10.1016/j.neuron.2020.12.004. Epub 2021 Jan 6.
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.
决策策略在训练过程中会不断演变，即使是经过良好训练的动物，其决策策略也可能继续变化。然而，关于感觉决策的研究往往根据固定的心理物理函数来描述行为，而这种函数是在训练完成后才拟合的。在这里，我们提出了 PsyTrack，这是一种从选择数据中推断感觉决策策略轨迹的灵活方法。我们将 PsyTrack 应用于从学习执行听觉和视觉决策任务的小鼠、大鼠和人类受试者的训练数据。我们表明，它成功地捕获了感官刺激加权、偏差和任务无关的协变量（如选择和刺激历史）的逐次波动。这种分析揭示了小鼠之间学习的巨大差异以及对任务统计数据变化的快速适应。PsyTrack 易于扩展到大型数据集，并且为在各种动物和任务中量化随时间变化的行为提供了强大的工具。
