Perceptual Learning: Human vs. Optimal Bayesian

感知学习：人类与最佳贝叶斯

• 批准号: 8123224
• 负责人: Miguel Patricio Eckstein
• 金额: $ 28.11万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8123224
• 关键词: Accounting; Address; Adult; Algorithms; Amblyopia; Animals; Area; Behavior; Blindness; Brain Injuries; Brain imaging; Cells; Chin; Complex; Data; Detection; Development; Discrimination; Emotional; Emotions; Environment; Eye; Eye Movements; Face; Frequencies; Funding; Goals; Gold; Human; Image; Individual; Infant; Instruction; Investigation; Judgment; Knowledge; Learning; Learning Disabilities; Life; Literature; Location; Machine Learning; Macular degeneration; Maps; Measurement; Measures; Mediating; Modeling; Monitor; Nature; Neuronal Plasticity; Nose; Oral cavity; Patients; Pattern; Perceptual learning; Performance; Positioning Attribute; Process; Progress Reports; Property; Protocols documentation; Psychophysiology; Public Health; Recording of previous events; Recovery; Research; Research Personnel; Resolution; Retinal; Retinitis Pigmentosa; Role; Sampling; Signal Transduction; Source; Spatial Distribution; Staging; Stimulus; Testing; To specify; Uncertainty; Vision; Vision Disorders; Visual; Visual Fields; Visual impairment; Visual system structure; Work; active vision; area striata; design; experience; flexibility; gaze; ideal observer (Bayesian); improved; interest; neurophysiology; novel; object recognition; oculomotor; prevent; public health relevance; relating to nervous system; sample fixation; tumor; visual information; visual performance; visual process; visual processing; visual search; visual stimulus

DESCRIPTION (provided by applicant): Neural plasticity and perceptual learning are fundamental in the developmental stages of vision, in attaining expertise in specialized perceptual tasks, and in recovery from brain injuries and low- vision disorders. One important process in perceptual learning is the improvement in humans' ability to use task-relevant (signal) information. Although there have been advances in the understanding of the dynamics and algorithms mediating how humans optimize the selection of task relevant visual information, little is known about how eye movement patterns vary with practice and their impact in optimizing perceptual performance. Yet, in real world environments, eye movements are a critical component of active vision as humans explore the visual scene to make perceptual judgments. Understanding perceptual learning in human daily life requires studying the mechanisms mediating the changes in the planning of eye movements with learning and their contributions to optimizing perceptual performance. We hypothesize that two new experimental paradigms with digitally designed visual stimuli, in conjunction with eye position recording, and a newly developed foveated ideal observer and Bayesian learner will help elucidate how humans learn to strategize their eye movements and the contributions of the optimized sampling of the images to improvements in perceptual learning. The proposed work will address the following questions: 1) Do humans use learned information about the statistical properties of the visual stimuli and the requirements of the task at hand to strategize their eye movements to optimize the foveal sampling of the visual scene and perceptual performance?; 2) Do humans use knowledge of the varying resolution of their foveated visual system to optimally learn to plan eye movements for a given set of visual stimuli and task?; 3) What are the contributions of learning to strategize eye movements to the overall improvements in perceptual performance in ecologically important tasks such as face recognition, object identification and visual search?; 4) How do human fixation patterns and performance benefits from strategizing eye movements compare to an optimal foveated observer and learner? The proposed work will improve our understanding of the human neural algorithms mediating the dynamics of adult perceptual learning during active vision for ecologically important tasks. The proposed experimental protocols and theoretical developments will also provide a novel, powerful and flexible framework with which other researchers can study eye movements and learning of humans undergoing visual loss recovery as well as patients with learning disabilities. PUBLIC HEALTH RELEVANCE: The proposed work benefits public health by increasing our understanding of how humans learn to move their eyes to potentially informative regions of the visual scene in important daily tasks such as identifying faces or searching for objects. Thorough understanding of these mechanisms in normal humans will allow identification of learning anomalies in patients recovering from visual-loss or learning disabilities and potentially develop tests to assess treatments.

描述（由申请人提供）：神经可塑性和感知学习是视觉发育阶段、获得专门感知任务的专业知识以及从脑损伤和低视力障碍中恢复的基础。感知学习的一个重要过程是人类使用任务相关（信号）信息的能力的提高。虽然在理解人类如何优化任务相关视觉信息的选择的动力学和算法方面取得了进展，但对眼动模式如何随实践而变化及其在优化感知性能方面的影响知之甚少。然而，在真实的世界环境中，当人类探索视觉场景以做出感知判断时，眼球运动是主动视觉的关键组成部分。理解人类日常生活中的知觉学习需要研究调节眼动规划变化的机制及其对优化知觉表现的贡献。我们假设，两个新的实验范式与数字设计的视觉刺激，结合眼位记录，和一个新开发的foveated理想的观察者和贝叶斯学习者将有助于阐明人类如何学习策略，他们的眼球运动和图像的优化采样的贡献，以改善感知学习。这项工作将解决以下问题：1）人类是否使用有关视觉刺激的统计特性和手头任务的要求的学习信息来制定他们的眼动策略，以优化视觉场景的中央凹采样和感知性能？2)人类是否利用他们的中央凹视觉系统的不同分辨率的知识来最佳地学习为给定的一组视觉刺激和任务规划眼球运动？3)在生态学意义上重要的任务中，如人脸识别、物体识别和视觉搜索，学习策略性眼动对知觉表现的整体改善有什么贡献？4)与最佳的中央凹观察者和学习者相比，人类的注视模式和表现如何从策略性眼动中获益？这项工作将提高我们对人类神经算法的理解，这些算法在主动视觉过程中介导成人感知学习的动态，用于生态上重要的任务。拟议的实验方案和理论发展也将提供一个新颖，强大和灵活的框架，其他研究人员可以研究眼动和学习的人类视力丧失恢复以及学习障碍患者。 公共卫生相关性：这项拟议中的工作通过增加我们对人类如何在重要的日常任务中（如识别人脸或搜索物体）学会将眼睛移动到视觉场景的潜在信息区域的理解，从而有益于公共健康。在正常人中彻底了解这些机制将允许识别从视力丧失或学习障碍中恢复的患者的学习异常，并可能开发评估治疗的测试。