Perceptual Learning: Human vs. Optimal Bayesian

感知学习：人类与最佳贝叶斯

基本信息

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
项目类别：
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

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

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项目摘要

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在视力的发展阶段，在专业知识任务中获得专业知识，以及从脑损伤和低视力障碍中恢复方面的基础。感知学习的一个重要过程是人类使用与任务相关（信号）信息的能力的提高。尽管了解人类如何优化与任务相关的视觉信息的选择的动力和算法的理解取得了进步，但关于眼睛运动模式如何随练习及其在优化感知性能方面的影响而变化的知之甚少。然而，在现实世界环境中，眼睛运动是主动视觉的关键组成部分，因为人类探索视觉场景以做出感知判断。了解人类日常生活中的感知学习需要研究通过学习及其对优化感知表现的贡献进行介导的眼动运动的变化。我们假设两个新的实验范式具有数字设计的视觉刺激，并结合眼睛位置记录，以及新开发的foveat foveat foveat foveat的理想观察者和贝叶斯学习者将有助于阐明人类如何策略他们的眼睛运动以及对感知学习的图像对图像进行优化采样的贡献。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以及手头任务的要求的学习信息，以制定其眼睛运动以优化视觉场景和感知性能的动静抽样？ 2）人类是否使用有关其动物视觉系统的不同分辨率的知识来最佳地学习为一组视觉刺激和任务计划眼动吗？ 3）在生态重要任务（例如面部识别，对象识别和视觉搜索）中，学习对眼睛运动进行战略性运动的整体改进的策略的贡献是什么？ 4）人类的固定模式和绩效如何使战略眼动作用与最佳的观察者和学习者相比，如何受益？拟议的工作将提高我们对人类神经算法的理解，从而介导成人感知学习在生态上重要的任务中的活跃视力期间的动态。提出的实验方案和理论发展还将提供一个新颖，强大而灵活的框架，其他研究人员可以通过这些框架研究眼睛运动和学习经历视觉丧失恢复的人以及学习障碍患者。公共卫生相关性：拟议的工作通过增加了我们如何学习将眼睛移到重要日常任务中的视觉场景的潜在信息区域（例如识别面孔或寻找对象）的潜在信息区域，从而使公共卫生受益。对正常人中这些机制的透彻理解将允许识别从视觉损失或学习障碍中恢复的患者中学习异常，并有可能开发测试以评估治疗方法。