Task-based ideal observer analysis and the efficiency of biological motion percep

基于任务的理想观察者分析与生物运动感知效率

• 批准号: 7943582
• 负责人: Jason M Gold
• 金额: $ 11.37万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7943582
• 关键词: Address; Affect; Affective; Auditory; Behavior; Biological; Boxing; Characteristics; Classification; Color; Complex; Decision Making; Detection; Devices; Discrimination; Disease; Electronics; Eye; Face; Future; Gender Identity; Goals; Human; Human body; Knowledge; Laboratories; Light; Lipreading; Measures; Mediating; Methods; Metric; Modeling; Motion; Motion Perception; Movement; Noise; Patients; Pattern; Pattern Recognition; Perception; Perceptual learning; Performance; Play; Process; Property; Psychophysiology; Published Comment; Relative (related person); Research; Research Personnel; Retina; Role; Series; Side; Signal Detection Analysis; Signal Transduction; Social Interaction; Source; Spottings; Staging; Stimulus; System; Techniques; Technology; Testing; Theoretical model; Time; To specify; Translating; Uncertainty; User-Computer Interface; Visual; Visual Agnosias; Visual Motion; Visual Perception; Visual system structure; Work; animation; base; computerized data processing; data exchange; design; disability; falls; flexibility; information processing; interest; novel; novel strategies; object recognition; public health relevance; research study; spatiotemporal; visual information; visual memory; visual process; visual processing

DESCRIPTION (provided by applicant): People are able to easily discriminate the movement of humans from other kinds of movement. This ability is sometimes referred to as `biological motion perception', and it has been the focus of a great deal of scientific interest due to the important role it plays in human behavior and social interactions. Although a great deal of research has been carried out to explore various aspects of how we are able to detect and recognize human movement, there has been little or no research that has tried to quantify how much information is carried by the motion of the human body nor how much of this information a human observer is able to use when trying to detect, discrimination or identify human motion. Ideal observer analysis is a technique that allows one to quantitatively answer these questions. Our preliminary results have shown that, contrary to current dogma, human observers make very poor use of information carried by the stimuli and tasks typically used in biological motion experiments. The first set of experiments described in this proposal are designed to build on these results by using a signal-detection based model of visual information processing and a series of system identification techniques to specify the mechanisms that mediate the perception of biological motion. The finding that information use in biological motion perception tasks is so poor has led us to a second, related line of research. The poor use of information we have observed in biological motion perception tasks is actually not uncommon for tasks that involve the perception of complex objects, such as faces and geometric forms. This has led use to ask the question: why is information processing so poor in these tasks? It is our hypothesis that the cause of inefficient information use in these tasks is due to the highly artificial and constrained conditions typically used in psychophysical experiments. Rather, the human visual system has evolved to be robust under conditions of stimulus variability and uncertainty. In this proposal, we outline a new approach called `task-based ideal observer analysis' (TBIOA) that involves the systematic manipulation of a task in order to maximize the human ability to use information. The goal of this approach is to allow us to determine what tasks and stimuli our perceptual systems have evolved to deal with most efficiently. In this proposal, we describe a series of experiments designed to implement and test TBIOA with both simple and complex pattern discrimination tasks. In addition to the immediate goals of the two lines of research described above, there are also several eventual applications of this work. First, this research can be used to inform the design of applications that rely on efficient encoding of information, such as visual data transmission and compression as well as video surveillance technology. Second, this research could potentially be used to help understand how patients with disabilities related to visual perception (e.g., visual agnosias) differ from normal observers and inform how one might design human-machine interfaces to overcome such disabilities. PUBLIC HEALTH RELEVANCE The first goal of the research described in this proposal is to understand how well people use the information that is available to them when they perceive the bodily motions of their fellow humans. The second goal is to develop and test a new modeling approach called "task-based ideal observer analysis", which seeks to determine what tasks and stimuli the human visual system deals with most efficiently. This research has potentially important implications for applications related to data transmission and compression, video surveillance technology and for our eventual understanding and treatment of disorders related to the perception of visual motion (for example, visual agnosias in which people are unable to recognize particular classes of visual motion).

描述（由申请人提供）：人们能够很容易地将人类的运动与其他类型的运动区分开来。这种能力有时被称为“生物运动感知”，由于它在人类行为和社会互动中发挥的重要作用，它一直是大量科学兴趣的焦点。虽然已经进行了大量的研究来探索我们如何能够检测和识别人体运动的各个方面，但是很少或没有研究试图量化人体运动携带了多少信息，也没有研究人类观察者在试图检测、区分或识别人体运动时能够使用多少信息。理想观察者分析是一种定量回答这些问题的技术。我们的初步结果表明，与目前的教条相反，人类观察者对生物运动实验中通常使用的刺激和任务所携带的信息利用得很差。在这个建议中描述的第一组实验的目的是建立在这些结果的基础上，通过使用基于信号检测的模型的视觉信息处理和一系列的系统识别技术，以指定的机制，介导的感知生物运动。在生物运动感知任务中，信息的使用是如此之差，这一发现使我们进入了第二个相关的研究领域。我们在生物运动感知任务中观察到的信息使用不足，实际上在涉及复杂物体（如面部和几何形状）感知的任务中并不罕见。这就导致了一个问题：为什么这些任务中的信息处理如此糟糕？我们的假设是，这些任务中信息使用效率低下的原因是由于心理物理实验中通常使用的高度人工和约束条件。相反，人类视觉系统已经进化到在刺激可变性和不确定性的条件下是鲁棒的。在这项建议中，我们概述了一种新的方法，称为“基于任务的理想观察者分析”（TBIOA），涉及系统的操作任务，以最大限度地提高人类使用信息的能力。这种方法的目的是让我们确定我们的感知系统已经进化到最有效地处理什么任务和刺激。在这个建议中，我们描述了一系列的实验，旨在实现和测试TBIOA与简单和复杂的模式识别任务。除了上述两条研究路线的直接目标外，这项工作还有几个最终的应用。首先，这项研究可以用来通知依赖于有效的信息编码，如视觉数据传输和压缩以及视频监控技术的应用程序的设计。其次，这项研究可能被用来帮助理解残疾患者如何与视觉感知相关（例如，视觉失认症）不同于正常的观察者，并告知人们如何设计人机界面来克服这种残疾。 公共卫生相关性 这项研究的第一个目标是了解人们在感知其他人的身体运动时，如何利用可用的信息。第二个目标是开发和测试一种称为“基于任务的理想观察者分析”的新建模方法，该方法旨在确定人类视觉系统最有效地处理哪些任务和刺激。这项研究具有潜在的重要意义，与数据传输和压缩，视频监控技术的应用程序，并为我们最终理解和治疗有关的视觉运动的感知障碍（例如，视觉失认症，其中人们无法识别特定类别的视觉运动）。