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：
8323947
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 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8323947
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 Health 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 relating to nervous system sample fixation tumor visual information visual performance visual process visual processing visual search visual stimulus

项目摘要

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.

神经可塑性和感知学习在视力的发展阶段至关重要，在获得专业的感知任务方面的专业知识以及从脑损伤和低 - 视力障碍。感知学习的一个重要过程是人类的改善使用与任务相关的（信号）信息的能力。尽管在了解人类如何优化选择的动态和算法与任务相关的视觉信息，关于眼动模式如何变化的知之甚少实践及其在优化感知表现方面的影响。但是，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做出感知判断。了解人类日常生活中的知觉学习需要研究介导通过学习的眼睛运动规划变化的机制他们对优化感知性能的贡献。我们假设两个新的具有数字设计的视觉刺激的实验范式，并结合眼睛位置录音，新开发的foveat foveational的理想观察者和贝叶斯学习者将有所帮助阐明人类如何学会制定他们的眼睛运动和贡献优化图像采样以改进感知学习。拟议的工作将解决以下问题：1）人类是否使用有关统计的学习信息视觉刺激的属性和手头的任务要求制定眼睛动作以优化视觉场景和感知性能的动脉采样？ 2）人类是否使用有关其动物视觉系统的不同解决方案的知识来最佳学会为给定的一组视觉刺激和任务计划眼动吗？ 3）什么是学习对眼睛运动进行战略性运动的贡献在生态上重要的任务中的表现，例如面部识别，对象识别和视觉搜索？ 4）人类的固定模式和绩效如何从制定战略中受益眼动与最佳的观察者和学习者相比？拟议的工作将提高我们对介导成人动态的人类神经算法的理解对生态上重要任务的积极愿景期间的感知学习。提议实验方案和理论发展还将提供一种新颖，有力和灵活的框架其他研究人员可以研究眼动和学习人类接受视觉损失恢复以及学习障碍患者。