Perceptual Learning: Human vs. Optimal Bayesian

感知学习：人类与最佳贝叶斯

• 批准号: 7988249
• 负责人: Miguel Patricio Eckstein
• 金额: $ 27.8万
• 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/7988249
• 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; Hand; 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(申请人提供)：神经可塑性和知觉学习是视觉发育阶段的基础，是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。知觉学习的一个重要过程是人类使用与任务相关的(信号)信息的能力的提高。尽管对调节人类如何优化任务相关视觉信息选择的动力学和算法的理解已经取得了进展，但人们对眼动模式如何随着练习而变化以及它们在优化知觉表现方面的影响知之甚少。然而，在现实世界中，当人类探索视觉场景以做出知觉判断时，眼球运动是活跃视觉的关键组成部分。要理解人类日常生活中的知觉学习，需要研究眼动计划与学习之间的调节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，以及新开发的凹陷理想观察者和贝叶斯学习器，将有助于阐明人类如何学习制定眼动策略，以及优化图像采样对改善知觉学习的贡献。这项拟议的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要求的已知信息来规划他们的眼球运动，以优化视觉场景的中心凹采样和知觉表现？2)人类是否利用其中心凹视觉系统的不同分辨率的知识，以最佳方式学习为给定的一组视觉刺激和任务计划眼球运动？3)在人脸识别、物体识别和视觉搜索等生态重要任务中，学习制定眼球运动策略对感知表现的整体改善有何贡献？4)与最佳的中心凹观察者和学习者相比，制定眼部运动策略对人类的注视模式和表现有何益处？这项拟议的工作将提高我们对人类神经算法的理解，该算法在生态重要任务的主动视觉过程中调节成人知觉学习的动力学。拟议的实验方案和理论发展还将提供一个新颖、强大和灵活的框架，其他研究人员可以利用该框架研究视力丧失恢复过程中的人类以及学习障碍患者的眼睛运动和学习。 与公共健康相关：这项拟议的工作有利于公共健康，因为它增加了我们对人类如何在识别人脸或搜索物体等重要日常任务中如何学习将眼睛移动到视觉场景中可能提供信息的区域的理解。彻底了解正常人的这些机制将有助于识别视力丧失或学习障碍恢复患者的学习异常，并有可能开发出评估治疗的测试。