CAREER: Computational mechanisms of rapid visual categorization: Models and psychophysics

灵长类动物可以一目了然地识别嵌在复杂自然视觉场景中的物体。尽管我们看起来很容易，但视觉识别--计算机视觉中解决的关键问题之一--对机器来说是相当困难的。了解哪些计算是由视皮层执行的，将为科学家提供一个强大的工具，以揭示人类感知和认知的关键机制，并创造新一代“看”机器。PI的中心研究目标是识别感知原理，并对快速视觉分类背后的神经机制进行建模。通过强制快速处理，快速的视觉分类范例有助于在更复杂的视觉例程发生之前隔离视觉信息的第一次传递。因此，在研究自然的日常视觉之前，理解一目了然的视觉可以说是必要的第一步。众所周知，眼球运动和注意力转移起着关键作用。具体地说，这一提议将导致建立一个灵长类视觉系统快速视觉识别的计算神经科学模型，该模型既符合视觉皮质细胞的生理特性，又能够预测人类参与者在一系列条件下的行为反应(包括正确和不正确的反应以及反应时间)。提出的模型将把视觉和决策计算模型的最新发展与大规模机器学习技术结合起来。将生成新的刺激集，这些集是针对针对人类心理物理数据的可选视觉表示和计算中的测试而优化定制的。这些实验反过来将使计算模型得以改进。作为这项建议的一部分开发的计算模型将被整合到课程中，并通过网络图形界面广泛传播。总体而言，该提案的跨学科性质将使学生有机会体验一个跨越学科和部门传统界限的研究环境。增加本科生对计算神经科学的参与将有助于将该领域整合到主流计算机科学和神经科学课程中。

Primates can recognize objects embedded in complex natural visual scenes at a glance. Despite the ease with which we see, visual recognition -- one of the key issues addressed in computer vision -- is quite difficult for machines. Understanding which computations are performed by the visual cortex would give scientists a powerful tool to uncover key mechanisms of human perception and cognition as well as to create a new generation of 'seeing' machines. The PI's central research goal is to identify the perceptual principles and model the neural mechanisms underlying rapid visual categorization. By forcing processing to be fast, rapid visual categorization paradigms help isolate the very first pass of visual information before more complex visual routines take place. Hence, understanding 'vision at a glance' is arguably a necessary first step before studying natural everyday vision where eye movements and attentional shifts are known to play a key role.Specifically, this proposal will lead to the development of a computational neuroscience model of rapid visual recognition in the primate visual system, which is both consistent with physiological properties of cells in the visual cortex and able to predict behavioral responses (both correct and incorrect responses as well as reaction times) from human participants across a range of conditions. The proposed model will integrate recent developments in computational models of vision and decision making with large-scale machine learning techniques. New stimulus sets will be generated, which are optimally tailored for testing among alternative visual representations and computations against human psychophysics data. These experiments will, in turn, enable the refinement of computational models.The computational models developed as part of this proposal will be integrated in courses and disseminated broadly via a web graphical interface. Overall the interdisciplinary nature of the proposal will give students the opportunity to experience a research environment that crosses traditional boundaries across disciplines and departments. Increased undergraduate participation in computational neuroscience will help integrate this area into the mainstream computer science and neuroscience curricula.