Schriver Brian J, Perkins Sean M, Sajda Paul, Wang Qi
Department of Biomedical Engineering, Columbia University, New York, NY, USA.
Psychophysiology. 2020 Aug;57(8):e13565. doi: 10.1111/psyp.13565. Epub 2020 Mar 30.
In decision-making tasks, neural circuits involved in different aspects of information processing may activate the central arousal system, likely through their interconnection with brainstem arousal nuclei, collectively contributing to the observed pupil-linked phasic arousal. However, the individual components of the phasic arousal associated with different elements of information processing and their effects on behavior remain little known. In this study, we used machine learning techniques to decompose pupil-linked phasic arousal evoked by different components of information processing in rats performing a Go/No-Go perceptual decision-making task. We found that phasic arousal evoked by stimulus encoding was larger for the Go stimulus than the No-Go stimulus. For each session, the separation between distributions of phasic arousal evoked by the Go and by the No-Go stimulus was predictive of perceptual performance. The separation between distributions of decision-formation-evoked arousal on correct and incorrect trials was correlated with decision criterion but not perceptual performance. When a Go stimulus was presented, the action of go was primarily determined by the phasic arousal evoked by stimulus encoding. On the contrary, when a No-Go stimulus was presented, the action of go was determined by phasic arousal elicited by both stimulus encoding and decision formation. Drift diffusion modeling revealed that the four model parameters were better accounted for when phasic arousal elicited by both stimulus encoding and decision formation was considered. These results suggest that the interplay between phasic arousal evoked by both stimulus encoding and decision formation has important functional consequences on forming behavioral choice in perceptual decision-making.
在决策任务中,参与信息处理不同方面的神经回路可能会激活中枢唤醒系统,可能是通过它们与脑干唤醒核的相互连接,共同促成了观察到的与瞳孔相关的相位性唤醒。然而,与信息处理不同元素相关的相位性唤醒的各个组成部分及其对行为的影响仍然鲜为人知。在本研究中,我们使用机器学习技术分解了在执行“Go/No-Go”感知决策任务的大鼠中,由信息处理的不同组成部分诱发的与瞳孔相关的相位性唤醒。我们发现,对于“Go”刺激,由刺激编码诱发的相位性唤醒比“No-Go”刺激更大。对于每个实验环节,“Go”刺激和“No-Go”刺激诱发的相位性唤醒分布之间的差异可预测感知表现。正确和错误试验中由决策形成诱发的唤醒分布之间的差异与决策标准相关,但与感知表现无关。当呈现“Go”刺激时,执行“Go”的动作主要由刺激编码诱发的相位性唤醒决定。相反,当呈现“No-Go”刺激时,执行“Go”的动作由刺激编码和决策形成共同诱发的相位性唤醒决定。漂移扩散模型显示,当考虑刺激编码和决策形成共同诱发的相位性唤醒时,四个模型参数能得到更好的解释。这些结果表明,刺激编码和决策形成共同诱发的相位性唤醒之间的相互作用对感知决策中行为选择的形成具有重要的功能影响。