NEURAL BASIS OF SHAPE REPRESENTATION AND RECOGNITION

形状表示和识别的神经基础

• 批准号: 7958851
• 负责人: Anitha Pasupathy
• 金额: $ 31.52万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7958851
• 关键词: Agnosia; Binding; Brain; Brain Diseases; Computer Retrieval of Information on Scientific Projects Database; Eye; Functional disorder; Funding; Goals; Grant; Image; Image Analysis; Institution; Laboratories; Pathway interactions; Primates; Process; Research; Research Personnel; Resources; Retinal; Shapes; Signal Transduction; Source; Staging; Tight Junctions; United States National Institutes of Health; V4 neuron; Visual; Visual system structure; area V4; awake; base; discount; neuromechanism; object recognition; psychologic; relating to nervous system; research study; response; visual information; visual process; visual processing

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The visual system rapidly and accurately recognizes objects that are partially occluded. The long-term goal of research in my laboratory is to determine how this is achieved by the primate visual system. Previous research has demonstrated that visual information that reaches our eyes is processed along the multi-stage ventral "shape processing" pathway. Our goal is to decipher the contributions of area V4, an intermediate stage in this pathway, to the processing of partial occlusion and to shape representation and object recognition in general. Under natural viewing conditions, visual objects that are closer to the viewer partially or completely occlude objects that are farther away. This produces "accidental" contour features due to the juxtaposition of the bounding contours of the occluded and occluding objects. Secondly, parts of the occluded object are missing and may even be fragmented in the retinal image. To accurately recognize the occluded object despite partial occlusion, the visual system needs to discount the accidental contour features and then sew together the fragmented parts by amodally completing the missing contours. Psychological and theoretical evidence suggests that analysis of image features at the intersecting junctions of the occluded and occluding contours (T-like junctions) in the early stages of visual processing underlies processing of occlusion but the neural mechanisms are unknown. Preliminary results from our laboratory suggest that responses of V4 neurons in awake primates differentially represent real and accidental contours. Amodal completion signals in area V4 also appear before accurate recognition of the partially occluded object. These results strongly support the hypothesis that area V4 is crucial for recognition under partial occlusion. Object recognition is impaired in visual agnosia, a dysfunction of the occipitotemporal pathway. Results from these experiments will constitute a major advance in our understanding of the brain computations that underlie object recognition and will bring us closer to devising strategies to alleviate and treat this brain disorder.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 视觉系统可以快速准确地识别部分被遮挡的物体。我实验室的长期研究目标是确定灵长类动物视觉系统是如何实现这一目标的。先前的研究表明，到达我们眼睛的视觉信息是沿着多阶段腹侧“形状处理”路径进行处理的。我们的目标是破译 V4 区域（该路径的中间阶段）对部分遮挡处理以及形状表示和一般物体识别的贡献。 在自然观看条件下，离观看者较近的视觉对象部分或完全遮挡较远的对象。由于被遮挡和遮挡对象的边界轮廓并置，这会产生“意外”轮廓特征。其次，视网膜图像中被遮挡物体的部分缺失，甚至可能破碎。为了在部分遮挡的情况下准确识别被遮挡的物体，视觉系统需要忽略偶然的轮廓特征，然后通过非模态地完成缺失的轮廓将碎片部分缝合在一起。心理学和理论证据表明，在视觉处理的早期阶段，对遮挡和遮挡轮廓相交处（T 型交界处）的图像特征的分析是遮挡处理的基础，但其神经机制尚不清楚。我们实验室的初步结果表明，清醒灵长类动物中 V4 神经元的反应不同地代表了真实和偶然的轮廓。在准确识别部分遮挡的物体之前，区域 V4 中的非模态完成信号也会出现。这些结果有力地支持了以下假设：V4 区域对于部分遮挡下的识别至关重要。视觉失认症（枕颞通路功能障碍）导致物体识别受损。这些实验的结果将推动我们对物体识别背后的大脑计算的理解取得重大进展，并使我们更接近于制定缓解和治疗这种大脑疾病的策略。