Perceptual Learning: Human vs. Optimal Bayesian

神经可塑性和知觉学习是视觉发育阶段的基础， 在获得专门知觉任务方面的专业知识，以及从脑损伤和低血压中恢复 视力障碍。知觉学习的一个重要过程是人类认知能力的提高 能够使用与任务相关的(信号)信息。尽管在这方面已经取得了进展 对人类如何优化选择的动力学和算法的理解 与任务相关的视觉信息，关于眼动模式如何随 实践及其对优化知觉表现的影响。然而，在现实世界中， 当人类探索视觉场景时，眼动是活跃视觉的关键组成部分 做出感性的判断。理解人类日常生活中的知觉学习需要 学习调节眼球运动计划变化的机制研究 以及他们对优化知觉表现的贡献。我们假设有两个新的 结合眼睛位置的数字设计视觉刺激的实验范例 记录，以及新开发的凹陷理想观察者和贝叶斯学习器将有所帮助 阐明人类如何学习规划他们的眼球运动，以及 优化图像采样以改善知觉学习。拟议中的工作 将解决以下问题：1)人类是否使用有关统计的已学信息 视觉刺激的特性和手头任务的要求来规划他们的眼睛 运动以优化中心凹采样的视觉场景和知觉表现？；2) 人类是否利用其中心凹视觉系统的不同分辨率的知识来优化 学习为一组给定的视觉刺激和任务计划眼动？3)什么是 学习眼球运动策略对知觉整体改善的贡献 执行重要的生态任务，如人脸识别、物体识别和 视觉搜索？；4)人类的注视模式和表现如何从制定战略中受益 眼球运动与最佳凹陷观察者和学习者相比？拟议的工作将 提高我们对人类神经算法的理解，这些算法调节成人的动态 在生态重要任务的主动视觉过程中的知觉学习。建议数 实验协议和理论发展还将提供一种新颖、强大和 灵活的框架，其他研究人员可以用来研究眼球运动和学习 正在进行视力丧失恢复的人类以及学习障碍患者。

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.