Decision-making dynamics are predicted by arousal and uninstructed movements.
Decision-making dynamics are predicted by arousal and uninstructed movements.
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决策动态是通过唤醒和无指导的运动来预测的。
DOI：
10.1016/j.celrep.2024.113709
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发表时间：
2024
期刊：
影响因子：
8.8
通讯作者：
Jaramillo,Santiago
中科院分区：
文献类型：
--
作者：
Hulsey,Daniel;Zumwalt,Kevin;Mazzucato,Luca;McCormick,DavidA;Jaramillo,Santiago
在感觉引导的行为中，动物的决策动态通过一系列不同的表现状态展开，即使刺激奖励偶然性保持静态。很少有人知道的因素，这些变化的任务表现。我们假设这些决策动态可以通过外部可观察的措施来预测，例如未经指示的运动和唤醒的变化。在这里，使用小鼠视觉和听觉任务表现数据的计算建模，我们发现了战略任务表现状态的转变与动物的觉醒和未经指示的运动之间的合法关系。使用隐马尔可夫模型应用于行为选择在感觉歧视任务，我们发现，动物之间的几分钟长的最佳，次优，和脱离性能状态波动。通过瞳孔直径和运动的中间水平和降低的可变性来预测最佳状态时期。我们的研究结果表明，外部可观察到的未经指示的行为可以预测最佳性能状态，并建议小鼠调节其唤醒过程中的最佳性能。
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, using computational modeling of visual and auditory task performance data from mice, 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 find that animals fluctuate between minutes-long optimal, sub-optimal, and disengaged performance states. Optimal state epochs are predicted by intermediate levels, and reduced variability, of pupil diameter and movement. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states and suggest that mice regulate their arousal during optimal performance.