Although artificial intelligence (AI) has achieved many feats at a rapid pace, there still exist open problems and fundamental shortcomings related to performance and resource efficiency. Since AI researchers benchmark a significant proportion of performance standards through human intelligence, cognitive sciences-inspired AI is a promising domain of research. Studying cognitive science can provide a fresh perspective to building fundamental blocks in AI research, which can lead to improved performance and efficiency. In this review paper, we focus on the cognitive functions of perception, which is the process of taking signals from one's surroundings as input, and processing them to understand the environment. Particularly, we study and compare its various processes through the lens of both cognitive sciences and AI. Through this study, we review all current major theories from various sub-disciplines of cognitive science (specifically neuroscience, psychology and linguistics), and draw parallels with theories and techniques from current practices in AI. We, hence, present a detailed collection of methods in AI for researchers to build AI systems inspired by cognitive science. Further, through the process of reviewing the state of cognitive-inspired AI, we point out many gaps in the current state of AI (with respect to the performance of the human brain), and hence present potential directions for researchers to develop better perception systems in AI.

翻译：尽管人工智能（AI）在快速发展中取得了诸多成就，但仍存在与性能和资源效率相关的悬而未决的问题与根本性缺陷。由于AI研究人员通过人类智能来基准测试大部分性能标准，认知科学启发的AI是一个有前景的研究领域。研究认知科学可为构建AI研究的基本模块提供全新视角，进而提升性能与效率。在本综述中，我们聚焦于感知这一认知功能——即从环境中接收信号作为输入，并处理它们以理解环境的过程。具体而言，我们通过认知科学与AI的双重视角，研究并对比了其多种处理机制。通过这项研究，我们系统回顾了认知科学各分支学科（特别是神经科学、心理学和语言学）当前所有主流理论，并将其与AI实践中的理论与技术进行类比。由此，我们为研究者提供了一套详尽的、受认知科学启发的AI系统构建方法。此外，在综述认知启发AI发展现状的过程中，我们指出了当前AI（相对于人脑性能）存在的诸多差距，从而为研究者开发更优秀的AI感知系统提供了潜在方向。