Brain mechanisms underlying the influence of emotions on spatial decision-making: An EEG study

Yanyan Zhao; Danli Wang; Xinyuan Wang; Steve C Chiu

doi:10.3389/fnins.2022.989988

Brain mechanisms underlying the influence of emotions on spatial decision-making: An EEG study

Front Neurosci. 2022 Sep 30:16:989988. doi: 10.3389/fnins.2022.989988. eCollection 2022.

Authors

Yanyan Zhao^{1

2}, Danli Wang^{1

2}, Xinyuan Wang^{1

2}, Steve C Chiu³

Affiliations

¹ State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
² School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
³ ECE Department, Idaho State University, Pocatello, ID, United States.

Abstract

It is common for people to make bad decisions because of their emotions in life. When these decisions are important, such as aeronautical decisions and driving decisions, the mistakes of decisions can cause irreversible damage. Therefore, it is important to explore how emotions influence decision-making, so as to avoid the negative influence of emotions on decision-making as much as possible. Although existing researchers have found some mechanisms of emotion's influence on decision-making, only a few studies focused on the influence of emotions on decision-making based on electroencephalography (EEG). In addition, most of them were focused on risky and uncertain decision-making. We designed a novel experimental task to explore the influence of emotion on spatial decision-making and recorded subjective data, decision-making behavioral data, and EEG data. By analyzing these data, we came to three conclusions. Firstly, we observed three similar event-related potentials (ERP) microstates in the decision-making process under different emotions by microstate analysis. Additionally, the prefrontal, parietal and occipital lobes played key roles in decision-making. Secondly, we found that the P2 component of the prefrontal lobe presented the influence of different emotions on decision-making by ERP analysis. Among them, positive emotion evoked the largest P2 amplitude compared to negative emotions and no stimuli. Thirdly, we found some graph metrics that were significantly associated with decision accuracy by effective connectivity analysis combined with graph theoretic analysis. In consequence, the finding of our study may shed more light on the brain mechanisms underlying the influence of emotions on spatial decision-making, thereby providing a basis for avoiding decision-making accidents caused by emotions and realizing better decision-making.

Keywords: decision-making; effective connectivity analysis; electroencephalography (EEG); emotion; event-related potentials (ERP); graph theoretical analysis; microstate analysis; spatial task.