Resting-state network predicts the decision-making behaviors of the proposer during the ultimatum game.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-11 DOI:10.1088/1741-2552/acf61e
Yuqin Li, Qian Yang, Yuxin Liu, Rui Wang, Yutong Zheng, Yubo Zhang, Yajing Si, Lin Jiang, Baodan Chen, Yueheng Peng, Feng Wan, Jing Yu, Dezhong Yao, Fali Li, Baoming He, Peng Xu
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Abstract

Objective. The decision-making behavior of the proposer is a key factor in achieving effective and equitable maintenance of social resources, particularly in economic interactions, and thus understanding the neurocognitive basis of the proposer's decision-making is a crucial issue. Yet the neural substrate of the proposer's decision behavior, especially from the resting-state network perspective, remains unclear.Approach. In this study, we investigated the relationship between the resting-state network and decision proposals and further established a multivariable model to predict the proposers' unfair offer rates in the ultimatum game.Main results.The results indicated the unfair offer rates of proposers are significantly related to the resting-state frontal-occipital and frontal-parietal connectivity in the delta band, as well as the network properties. And compared to the conservative decision group (low unfair offer rate), the risk decision group (high unfair offer rate) exhibited stronger resting-state long-range linkages. Finally, the established multivariable model did accurately predict the unfair offer rates of the proposers, along with a correlation coefficient of 0.466 between the actual and predicted behaviors.Significance. Together, these findings demonstrated that related resting-state frontal-occipital and frontal-parietal connectivity may serve as a dispositional indicator of the risky behaviors for the proposers and subsequently predict a highly complex decision-making behavior, which contributed to the development of artificial intelligence decision-making system with biological characteristics as well.

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静息状态网络预测最后通牒博弈中提出者的决策行为。
客观的提出者的决策行为是实现有效和公平维护社会资源的关键因素,特别是在经济互动中,因此理解提出者决策的神经认知基础是一个关键问题。然而,提出者决策行为的神经基础,特别是从静息状态网络的角度来看,仍然不清楚。方法在本研究中,我们调查了静息状态网络与决策建议之间的关系,并进一步建立了一个多变量模型来预测最后通牒博弈中建议者的不公平报价率。主要结果。结果表明,提议者的不公平报价率与三角带静息状态下的额枕和额顶叶连接性以及网络特性显著相关。与保守决策组(低不公平报价率)相比,风险决策组(高不公平报价)表现出更强的静息态长期联系。最后,所建立的多变量模型确实准确地预测了提议者的不公平报价率,实际行为与预测行为之间的相关系数为0.466。意义总之,这些发现表明,相关的静息状态-额叶-枕叶和额叶-顶叶连接可能是命题者风险行为的倾向性指标,并随后预测高度复杂的决策行为,这也有助于开发具有生物学特征的人工智能决策系统。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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