Perceptual Learning: Human vs. Optimal Bayesian

神经可塑性和感知学习是视觉发育阶段的基础， 在获得专门的知觉任务的专业知识，并从脑损伤和低- 视力障碍感知学习的一个重要过程是人类认知能力的提高。 使用任务相关（信号）信息的能力。尽管在这方面取得了进展， 了解人类如何优化选择的动力学和算法 任务相关的视觉信息，很少有人知道眼动模式如何变化， 实践及其对优化感知性能的影响。然而，在真实的世界环境中， 当人类探索视觉场景时，眼球运动是主动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感知学习需要 研究调节眼动规划与学习变化的机制 以及它们对优化感知性能的贡献。我们假设两个新的 实验范例与数字设计的视觉刺激，结合眼睛的位置 记录，以及新开发的foveated理想观察者和贝叶斯学习者将有助于 阐明人类如何学会战略化他们的眼球运动和贡献的 优化的图像采样，以改善感知学习。拟议工作 将解决以下问题：1）人类是否使用有关统计信息的学习信息 视觉刺激的性质和手头任务的要求，以策略他们的眼睛 运动来优化视觉场景的中央凹采样和感知性能？（二） 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， 学习为给定的视觉刺激和任务规划眼球运动？3)有哪些 学习策略性眼动对知觉的整体改善的贡献 在生态上重要的任务，如人脸识别，物体识别， 视觉搜索？4)人类的固定模式和绩效如何从战略制定中受益 眼动与最佳中央凹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， 在主动视觉过程中对生态重要任务的感知学习。拟议 实验方案和理论发展也将提供一个新的，强大的， 灵活的框架，其他研究人员可以研究眼动和学习的 经历视力丧失恢复的人以及具有学习障碍的患者。

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.