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).

外围视力占视野的99.99％以上。它的优势和局限性强烈限制了视觉感知 - 人类可以看出的东西，以及他们移动眼睛以将有关世界的信息拼凑在一起的过程。外围视觉以复杂而有趣的方式与中央凹视觉不同，最重要的是，由于“拥挤”，在附近的刺激附近的其他刺激中存在识别外围刺激的识别可能会大大损害。该项目将通过开发和测试一组外围视觉模型来检查视觉皮层中编码的性质。这些模型将针对回答有关神经生物学机制的关键问题。美国和德国的合作调查人员将开发模型，并创建一个基准数据集，以供行为结果进行解释。模型和数据集将免费提供，以帮助其他研究人员并告知诸如Heads Up显示器和用户界面之类的应用程序。这项工作将洞悉视觉皮层中编码的功能，以及哪些权衡可能导致视觉系统开发该编码。理解这些权衡可能会为计算机愿景提供信息，就像人类的视野一样，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，稀疏编码以及人工智能中卷积神经网络的最新成功的见解。研究人员将通过研究自然图像任务和模型驱动的实验的结合来收集比现有拥挤数据集的基准行为现象。然后，他们将在基准数据集上比较新模型的预测以及Rosenholtz博士现有的外围视觉模型。这样做将确定表现最佳的模型，并回答有关合并计算和非线性操作员的性质的关键问题，以及有关外围视觉所编码的功能的复杂性，性质和目的。