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

Huimin Huang; Rui Li; Junsong Zhang

doi:10.7717/peerj.15351

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

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

Authors

Huimin Huang^#¹, Rui Li^#¹, Junsong Zhang²

Affiliations

¹ National Engineering Research Center for E-learning, Central China Normal University, Wuhan, Hubei, China.
² Brain Cognition and Intelligent Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, Fujian, China.

^# Contributed equally.

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.

Keywords: Computational models; Evaluation; Neural mechanisms; Neural pathways; Sustained attention.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Attention*
Computer Simulation
Humans
Models, Neurological
Neural Pathways
Wakefulness*

Grants and funding

This work was financially supported by the National Nature Science Foundation of China (No. 61772440), the National Nature Science Foundation of China (No. 62007016), and supported by Fundamental Research Funds for the Central Universities (No. CCNU20XJ015), the Collaborative Innovation Platform Project for Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone (No. 2022-P-028), 2022 Hulunbuir City’s Special Key Project for Revitalizing City by Science and Technology (No. 2022HZZX002), and the Joint Laboratory of Xiamen University and Hejia Company for Intelligent Cognitive Assessment. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.