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视觉持续注意的研究综述:神经机制与计算模型。

A review of visual sustained attention: neural mechanisms and computational models.

机构信息

National Engineering Research Center for E-learning, Central China Normal University, Wuhan, Hubei, China.

Brain Cognition and Intelligent Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, Fujian, China.

出版信息

PeerJ. 2023 Jun 13;11:e15351. doi: 10.7717/peerj.15351. eCollection 2023.

Abstract

Sustained attention is one of the basic abilities of humans to maintain concentration on relevant information while ignoring irrelevant information over extended periods. The purpose of the review is to provide insight into how to integrate neural mechanisms of sustained attention with computational models to facilitate research and application. Although many studies have assessed attention, the evaluation of humans' sustained attention is not sufficiently comprehensive. Hence, this study provides a current review on both neural mechanisms and computational models of visual sustained attention. We first review models, measurements, and neural mechanisms of sustained attention and propose plausible neural pathways for visual sustained attention. Next, we analyze and compare the different computational models of sustained attention that the previous reviews have not systematically summarized. We then provide computational models for automatically detecting vigilance states and evaluation of sustained attention. Finally, we outline possible future trends in the research field of sustained attention.

摘要

持续注意力是人类的基本能力之一,它可以使人在较长时间内集中注意力于相关信息,而忽略不相关信息。本综述的目的是提供一种方法,将持续注意力的神经机制与计算模型相结合,以促进研究和应用。尽管有许多研究评估了注意力,但对人类持续注意力的评估还不够全面。因此,本研究对视觉持续注意力的神经机制和计算模型进行了综述。我们首先回顾了持续注意力的模型、测量和神经机制,并提出了视觉持续注意力的可能神经途径。接下来,我们分析和比较了之前综述中没有系统总结的不同持续注意力计算模型。然后,我们提供了用于自动检测警戒状态和评估持续注意力的计算模型。最后,我们概述了持续注意力研究领域的未来发展趋势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4c49/10274610/98a77d896975/peerj-11-15351-g001.jpg

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