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视觉拥挤效应的神经机制
引用本文:范真知,方 方,陈 娟.视觉拥挤效应的神经机制[J].中国科学:生命科学,2014(5):450-462.
作者姓名:范真知  方 方  陈 娟
作者单位:[1]北京大学心理学系、机器感知与智能教育部重点实验室,北京100871 [2]北京大学、清华大学生命科学联合中心,北京100871 [3]北京大学麦戈文脑研究所,北京100871 [4]The Brain and Mind Institute,The University of Western Ontario,London N6A 5B7,Canada
基金项目:国家自然科学基金重点项目(批准号:31230029)和国家重点基础研究发展计划(批准号:2011CBA00400,2010CB833903)资助
摘    要:当出现在边缘视野的一个物体被周围其他物体包围时,视觉系统对它的识别会很困难,这种现象叫做视觉拥挤效应.研究拥挤效应既有利于理解人类进行客体识别的过程,也对治疗黄斑变性、弱视和阅读障碍等视觉病变有显著的临床意义.自拥挤效应被提出以来,对拥挤效应的特性、神经机制和影响因素等都做了深入地研究.本文将系统地综述拥挤效应的研究进展,包括其特性、现有的理论假设、计算模型、可能涉及的大脑区域以及近年来利用知觉学习消除拥挤效应的一些工作,最后对该领域的未来发展给出建议.尽管在这个领域已经获得了丰富的成果,但在许多问题上仍有争议,未来还需要更为巧妙的设计和精确的技术进一步解决这些问题.

关 键 词:视觉拥挤效应  神经机制  计算模型  知觉学习

Neural Mechanisms of Visual Crowding Effect
FAN ZhenZhi,FANG Fang& CHEN Juan.Neural Mechanisms of Visual Crowding Effect[J].Scientia Sinica Vitae,2014(5):450-462.
Authors:FAN ZhenZhi  FANG Fang& CHEN Juan
Institution:1 Key Laboratory of Machine Perception (Ministry of Education), Department of Psychology, Peking University, Beijing 100871, China 2 PeMng-Tsinghua Center for Life Sciences, Beijing 100871, China; 3 IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China; 4 The Brain andMindlnstitute, The University of Western Ontario, London N6A 5B7, Canada)
Abstract:When a target is presented with nearby flankers in the peripheral visual field, it becomes difficult to be identified, which is a phenomenon termed crowding. Studying crowding not only facilitates understanding of object recognition, but also benefits the remedy of macular degeneration, amblyopia and dyslexia. Since the concept of crowding was put forward, researchers have studied it extensively and gained much knowledge. Here, we provide an overview of the advances in this research field, including the properties of crowding, the existing theories and computational models that were proposed to explain the underlying neural mechanisms of crowding and how to alleviate crowding with perceptual learning. Although there has been tremendous growth of this topic, controversies remain. Further studies with elaborate designs and advanced technologies are required to address these controversies.
Keywords:visual crowding  neural mechanisms  computational models  perceptual learning
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