Fu Di, Weber Cornelius, Yang Guochun, Kerzel Matthias, Nan Weizhi, Barros Pablo, Wu Haiyan, Liu Xun, Wermter Stefan
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
Front Integr Neurosci. 2020 Feb 27;14:10. doi: 10.3389/fnint.2020.00010. eCollection 2020.
Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a central topic in cognitive science. Compared with unimodal studies, crossmodal studies are more complex but necessary to solve real-world challenges in both human experiments and computational modeling. Although an increasing number of findings on crossmodal selective attention have shed light on humans' behavioral patterns and neural underpinnings, a much better understanding is still necessary to yield the same benefit for intelligent computational agents. This article reviews studies of selective attention in unimodal visual and auditory and crossmodal audiovisual setups from the multidisciplinary perspectives of psychology and cognitive neuroscience, and evaluates different ways to simulate analogous mechanisms in computational models and robotics. We discuss the gaps between these fields in this interdisciplinary review and provide insights about how to use psychological findings and theories in artificial intelligence from different perspectives.
选择性注意在从环境中获取和利用信息方面起着至关重要的作用。在过去的50年里，对选择性注意的研究一直是认知科学的核心话题。与单模态研究相比，跨模态研究更为复杂，但对于解决人类实验和计算建模中的现实世界挑战而言是必要的。尽管越来越多关于跨模态选择性注意的研究结果揭示了人类的行为模式和神经基础，但为智能计算主体带来同样的益处仍需要更好地理解。本文从心理学和认知神经科学的多学科视角回顾了单模态视觉和听觉以及跨模态视听环境中的选择性注意研究，并评估了在计算模型和机器人技术中模拟类似机制的不同方法。在这篇跨学科综述中，我们讨论了这些领域之间的差距，并从不同角度提供了关于如何在人工智能中运用心理学研究结果和理论的见解。
