US-German Research Proposal: Neurocomputation in the Visual Periphery: Experiments and Models

美德研究计划：视觉外围的神经计算：实验和模型

• 批准号: 1607486
• 负责人: Ruth Rosenholtz
• 金额: $ 68.17万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1607486&HistoricalAwards=false
• 关键词: US; German; Research; Proposal; Neurocomputation

Peripheral vision comprises over 99.99% of the visual field. Its strengths and limitations strongly constrain visual perception -- what humans can see at a glance, and the processes by which they move their eyes to piece together information about the world. Peripheral vision differs from foveal vision in complex and interesting ways, most importantly due to "crowding," in which identifying a peripheral stimulus can be substantially impaired by the presence of other, nearby stimuli. This project will examine the nature of the encoding in visual cortex, through development and testing of a set of models of peripheral vision. These models will be targeted at answering key questions about the neurobiological mechanisms. The collaborating investigators, in the US and Germany, will develop models and create a benchmark dataset of behavioral results to be explained. The models and dataset will be made freely available, to aid other researchers and to inform the development of applications such as heads up displays and user interfaces. This work will provide insight into what features are encoded in visual cortex, as well as what tradeoffs may have led the visual system to develop that encoding. Understanding those tradeoffs may inform computer vision which, like human vision, faces constraints on processing capacity. The development of new model variants will be based on insights from neurophysiology, natural image statistics, sparse coding, and the recent success of convolutional neural networks in artificial intelligence. The investigators will gather benchmark behavioral phenomena far richer than existing crowding datasets, through a combination of studying natural image tasks and model-driven experiments. They will then compare predictions of the new models, as well as of Dr. Rosenholtz's existing high-performing model of peripheral vision, on the benchmark dataset. Doing so will identify the best-performing model(s), and answer key questions about the nature of pooling computations and of non-linear operators, and about the complexity, nature, and purpose of the features encoded by peripheral vision.A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF).

周边视觉占视野的99.99%以上。它的优势和局限性强烈地限制了视觉感知--人类一眼就能看到的东西，以及他们移动眼睛拼凑世界信息的过程。周边视觉以复杂和有趣的方式不同于中央凹视觉，最重要的是由于“拥挤”，其中识别周边刺激可能会因其他附近刺激的存在而受到严重损害。这个项目将通过开发和测试一套周边视觉模型来研究视觉皮层编码的本质。这些模型将针对回答有关神经生物学机制的关键问题。美国和德国的合作研究人员将开发模型，并创建一个待解释的行为结果的基准数据集。这些模型和数据集将免费提供，以帮助其他研究人员，并为平视显示器和用户界面等应用程序的开发提供信息。这项工作将深入了解视觉皮层中编码的特征，以及可能导致视觉系统开发这种编码的权衡。理解这些权衡可以为计算机视觉提供信息，就像人类视觉一样，计算机视觉也面临着处理能力的限制。新模型变体的开发将基于神经生理学、自然图像统计、稀疏编码以及最近卷积神经网络在人工智能中的成功。研究人员将通过研究自然图像任务和模型驱动实验的结合，收集比现有拥挤数据集丰富得多的基准行为现象。然后，他们将在基准数据集上比较新模型的预测，以及Rosenholtz博士现有的高性能周边视觉模型的预测。这样做将确定性能最好的模型，并回答有关池计算和非线性运算符的性质，以及外围视觉编码特征的复杂性，性质和目的的关键问题。一个配套项目正在由联邦教育和研究部，德国（BMBF）资助。