Hulsey Daniel, Zumwalt Kevin, Mazzucato Luca, McCormick David A, Jaramillo Santiago
Institute of Neuroscience, University of Oregon, Eugene, OR, USA.
Department of Biology, University of Oregon, Eugene, OR, USA.
bioRxiv. 2023 Mar 28:2023.03.02.530651. doi: 10.1101/2023.03.02.530651.
During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, combining behavioral experiments in mice with computational modeling, we uncovered lawful relationships between transitions in strategic task performance states and an animal's arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we found that animals fluctuate between minutes-long optimal, sub-optimal and disengaged performance states. Optimal state epochs were predicted by intermediate levels, and reduced variability, of pupil diameter, along with reduced variability in face movements and locomotion. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states, and suggest mice regulate their arousal during optimal performance.
在感觉引导行为过程中，即使刺激-奖励的意外情况保持不变，动物的决策动态也会通过一系列不同的表现状态展现出来。对于这些任务表现变化背后的因素，我们知之甚少。我们假设这些决策动态可以通过外部可观察的指标来预测，比如自发动作和觉醒状态的变化。在这里，我们将小鼠行为实验与计算模型相结合，揭示了策略性任务表现状态的转变与动物的觉醒和自发动作之间的规律关系。通过将隐马尔可夫模型应用于感觉辨别任务中的行为选择，我们发现动物会在持续数分钟的最优、次优和脱离状态之间波动。最优状态阶段可通过瞳孔直径处于中等水平且变异性降低来预测，同时面部动作和运动的变异性也会降低。我们的结果表明，外部可观察的自发行为能够预测最优表现状态，并表明小鼠在最优表现期间会调节自身的觉醒状态。
