Spatiotemporal Neural Dynamics of Visual Decisions

观看的一个重要方面是决定关注什么。这些决定是由多个大脑区域的活动控制的。人类视觉系统的最新模型倾向于将其视为前馈机器，但反馈连接对生物视觉的效率也非常重要。这个项目将开发视觉系统的数学模型，来展示视觉处理、决策和注意力是如何紧密联系在一起的。这些模型将在三个任务上进行测试：视觉追踪曲线、视觉搜索树和检测运动。结果将显示人类视觉系统不仅仅是过滤图像，而是可以执行复杂的决策任务的程度。用视觉假体治疗失明需要继续研究摄像头与大脑的接口，这需要对大脑的视觉处理能力有深入的计算知识。该项目将为视觉系统的效率和潜在的神经原理提供新颖而重要的数学见解。这项工作的成果也将用于为科罗拉多大学博尔德分校新开发的统计与数据科学专业和专业硕士开发课程材料，培训下一代数据科学家。此外，这项工作将增强典型的机器学习方法来建模人类视觉，并可能激发计算机辅助视觉和分类的新技术。视觉的数学理论必须扩展，以解决反映自然世界复杂性的认知任务。该项目推进这一努力有三个主要原因。首先，它专注于理解视觉皮层如何参与决策，这是一个无处不在的认知过程。大多数视觉系统的研究都考虑对象过滤和图像分类。该项目将开发空间扩展的神经网络模型，既处理时空输入，又解释它们以做出复杂的决策。这些模型将反映人类观察和解释的视觉世界的几何形状，由此产生的理论将有助于预测人们用于做出日常视觉决策的策略。其次，该项目基于视觉皮层的数学模型，我们将用实验合作者的数据集进行验证。这些模型适用于数学分析，因此神经网络的架构可以与它们执行的认知计算联系起来，提供可测试的预测。这一技能对于分析视觉皮层的时空活动模型至关重要。学员将熟悉非线性和随机系统的分析技术，以及适用于科学和工业中许多问题的决策的概率模型。PI将在网页上分享教育图形、软件和外联材料，以广泛交流研究结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

An important aspect of seeing is deciding what to focus on. Such decisions are governed by activity across multiple brain areas. State of the art models of the human visual system tend to treat it as a feedforward machine, but feedback connections are also very important to the efficiency of biological vision. This project will develop mathematical models of the visual system that demonstrate how visual processing, decision making, and attention are intimately linked. These models will tested on three tasks: visually tracing a curve, visual searching a tree, and detecting motion. Results will show the degree to which the human visual system does not simply filter images, but can perform complex decision making tasks. Treatment of blindness with visual prostheses requires continued research on camera-to-brain interfaces that require deep computational knowledge of the brain's visual processing abilities. This project will develop novel and important mathematical insights into the efficiency of the visual system and the underlying neural principles. Results from this work will also be leveraged to develop course material for newly developed major and professional masters in Statistics and Data Science at CU Boulder, training the next generation of data scientists. In addition, this work will augment typical machine learning approaches to modeling human vision, and potentially inspire new technologies for computer-aided vision and categorization. Mathematical theories of vision must be extended to address cognitive tasks that reflect the complexity of the natural world. This project advances this effort for three main reasons. First, it focuses on understanding how the visual cortex participates in decision-making, a ubiquitous cognitive process. Most studies of the visual system consider object filtering and image classification. This project will develop spatially-extended neuronal network models that both process spatiotemporal inputs and interpret them to make complex decisions. These models will reflect the geometry of the visual world that humans observe and interpret, and the resulting theories will help to predict strategies people use to make everyday visual decisions. Second, the project is grounded in mathematical models of the visual cortex we will validate with the data sets of experimental collaborators. These models are amenable to mathematical analysis, so the architecture of neuronal networks can be linked to the cognitive computations they perform, providing testable predictions. This skill set will be crucial for analyzing models of spatiotemporal activity in visual cortex. Trainees will become conversant in techniques for the analysis of nonlinear and stochastic systems as well as probabilistic models of decision-making applicable to many problems in science and industry. The PI will share educational graphics, software, and outreach materials on a webpage to communicate findings widely.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.