Interplay between components of pupil-linked phasic arousal and its role in driving behavioral choice in Go/No-Go perceptual decision-making.
Interplay between components of pupil-linked phasic arousal and its role in driving behavioral choice in Go/No-Go perceptual decision-making.
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DOI：
10.1111/psyp.13565
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发表时间：
2020-08
期刊：
影响因子：
3.7
通讯作者：
Wang Q
中科院分区：
文献类型：
--
作者：
Schriver BJ;Perkins SM;Sajda P;Wang Q
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. Pupil size has been used as a reliable, noninvasive index of arousal, able to account for variability in both neural activity and behavioral performance. Recent studies have shown that phasic arousal indexed by pupil size is driven throughout the decision-making process, modulating the perceptual interpretation of sensory input. However, this phasic arousal may reflect the summation of several, superimposed arousal events evoked by different information processing components during decision-making. To better understand the individual components of phasic arousal evoked by different elements of information processing and their functional consequences, we used machine learning to disentangle task-evoked pupil responses into their elementary components while simultaneously learning the contribution of each underlying process on a trial-by-trial basis. For the first time, we were able to look at the relationships between the strengths of the unique elementary components and behavioral performance on a trial-by-trial basis. We discovered that these components better explained behavioral variability and found complex interplay between phasic pupil-linked arousals evoked by stimulus encoding and decision formation drove behavioral choice. Taken together, our findings show that machine learning allowed us to unveil otherwise hidden aspects of the task-evoked pupil response. By deconvolving the task-evoked pupil response, we disentangled independent information streams that allowed us to account for more variability in the decision-making process. This work may lead to new technology for noninvasive measurement of activation of separate neural circuits which are responsible for processing different aspects of information during decision-making.
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