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Processing object transformations in human visual cortex

Processing object transformations in human visual cortex
处理人类视觉皮层中的物体变换
批准号:
6738291
负责人:
SCOTT O MURRAY
金额:
$4.16万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-09-30 至 2005-09-29

项目摘要

项目成果

SCOTT O MURRAY的其他基金

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中文摘要
翻译
描述(由申请人提供):人类视觉系统面临着识别对象的计算困难问题,尽管图像位置、比例、视点和照明可能有很大的变化。保持“物体恒常性”的能力对我们的生存至关重要,使我们能够感知稳定,可预测和熟悉的环境并与之互动。尽管它的重要性,视觉系统如何实现对象恒常性在很大程度上是未知的。我们提出了一系列的行为和功能性磁共振成像(fMRI)实验,旨在表征的神经机制,在观看条件的变化过程中的对象识别。在第一系列实验中,我们的目标是表征神经对图像变换的反应,包括旋转,缩放和位置。第二组实验将建立在第一组实验的基础上,评估注意力和期望对行为和神经反应的作用。最后一组实验将试图说明学习和经验在物体知觉和变换不变神经反应发展中的作用。在所有的实验中,我们将使用一种新的功能磁共振成像适应技术来表征特定视觉区域的调谐功能。
英文摘要
DESCRIPTION (provided by applicant): The human visual system is faced with the computationally difficult problem of recognizing objects despite potentially large changes in image position, scale, viewpoint, and illumination. The ability to maintain 'object constancy' is crucial to our survival, allowing us to perceive and interact with a stable, predictable, and familiar environment. Despite its importance, how the visual system achieves object constancy is largely unknown. We propose a series of behavioral and functional MRI (fMRI) experiments aimed at characterizing the neural mechanisms underlying object recognition during changes in viewing conditions. In the first series of experiments, we aim to characterize neural responses to image transformations including rotation, scale, and position. The second set of experiments will build on the results of the first by assessing the role of attention and expectation on behavioral and neural responses. The final set of experiments will attempt to specify the role of learning and experience in the development of object perceptions and transformation-invariant neural responses. In all of the experiments, we will use a novel fMRI adaptation technique to characterize tuning functions in specified visual areas.
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Attention allocation as a computational mechanism for altered sensory processing in autism
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  • 项目类别:
  • 资助金额:
    $81.6万
  • 财政年份:
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  • 项目类别:
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    2019
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