Analyses of Perceptual Learning

感知学习分析

• 批准号: RGPIN-2019-06945
• 负责人: Bennett, Patrick
• 金额: $ 2.91万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716268
• 关键词: Analyses; Perceptual; Learning

My research examines the factors that determine how we perceive visual patterns, and the way those factors are affected by perceptual learning. Perceptual learning refers to the fact that our ability to detect, discriminate, and identify patterns improves as a result of practice. Studying perceptual learning can provide insights into the general principles of learning and memory, and into the ways our brain adapts and changes as a function of experience. This knowledge could be used to devise ways of improving skills in applied areas requiring a high degree of visual skill (e.g., radiology and medical imaging), or perhaps alleviate visual deficits associated with neural disorders such as amblyopia, brain injuries, or the effects of aging. Many experiments examine how perceptual learning improves our ability to perform simple tasks that require us to discriminate two patterns that differ on a single dimension (e.g., orientation). However, my research examines how learning alters our ability to identify spatially complex patterns like textures and faces. One obvious reason for using more complex patterns is that they may provide better insights into the way learning affects our perception of naturalistic patterns, textures, and objects (as well as important classes of "artificial" patterns such as radiographic images). Also, investigating learning with progressively more complex stimuli and/or tasks is necessary to determine how theories of learning must be altered to account for learning in a variety of conditions. Therefore, the current proposal describes several projects that examine the factors that influence how our perception of complex patterns is altered by learning, and how that learning can be generalized or transferred to new patterns that were not seen during practice

我的研究考察了决定我们如何感知视觉模式的因素，以及这些因素受到感知学习影响的方式。感知学习指的是我们检测、区分和识别模式的能力在实践中得到提高。研究知觉学习可以让我们深入了解学习和记忆的一般原理，以及我们的大脑如何适应和改变经验。这些知识可以用来设计提高需要高度视觉技能的应用领域的技能的方法（例如，放射学和医学成像），或者可能减轻与弱视、脑损伤或衰老影响等神经疾病相关的视觉缺陷。许多实验研究了感知学习如何提高我们执行简单任务的能力，这些任务需要我们区分在单一维度上不同的两种模式（例如，方向）。然而，我的研究考察了学习如何改变我们识别空间复杂模式（如纹理和面孔）的能力。使用更复杂的模式的一个明显原因是，它们可以更好地了解学习如何影响我们对自然模式、纹理和物体的感知（以及重要的“人工”模式类别，如放射图像）。此外，研究越来越复杂的刺激和/或任务的学习是必要的，以确定学习理论必须如何改变，以解释各种条件下的学习。因此，当前的提案描述了几个项目，这些项目检查了影响我们对复杂模式的感知如何通过学习而改变的因素，以及如何将学习归纳或转移到实践中未见的新模式中