Shared and specific mechanisms of auditory and visual category learning

听觉和视觉类别学习的共享和特定机制

• 批准号: 10197776
• 负责人: Casey L Roark
• 金额: $ 6.6万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10197776
• 关键词: Affect; Area; Attention; Auditory; Behavior; Behavioral; Brain; Brain region; Categories; Classification; Cognitive; Complex; Corpus striatum structure; Disease; Dyslexia; Environment; Esters; Feedback; Finches; Foundations; Functional Magnetic Resonance Imaging; Goals; Hobbies; Human; Hybrids; Impairment; Individual; Investigation; Knowledge; Language; Learning; Literature; Machine Learning; Magnetic Resonance; Mentors; Modality; Modeling; Nature; Neurobiology; Participant; Pattern; Process; Property; Research; Scientist; Sensory; Short-Term Memory; Specific qualifier value; Specificity; Speech; Speech Perception; Structure; Techniques; Testing; Theoretical model; Training; Visual; Work; auditory stimulus; autism spectrum disorder; base; behavior measurement; career; cognitive neuroscience; cognitive process; cognitive system; design; experimental study; frontal lobe; individual variation; innovation; learning ability; neural circuit; neuromechanism; object recognition; programs; recruit; relating to nervous system; response; sound; specific language impairment; theories; visual learning

PROJECT SUMMARY/ABSTRACT The ability to learn new perceptual categories enables some of the most complex human behaviors, from speech perception to visual object recognition. Current understanding of the mechanisms involved in perceptual category learning relies on the fundamental assumption that the processes underlying such learning are shared across the senses. However, the vast majority of this work has focused on the visual modality. As a consequence, the research regarding how humans learn to group complex auditory information into categories has relied greatly on conclusions from the research in the visual domain without testing this critical assumption. However, recent evidence from the attention literature suggests that even seemingly domain-general cognitive processes, such as working memory, are accomplished via sensory-biased regions in frontal cortex. The current investigation will directly compare the computational and neural mechanisms supporting auditory and visual category learning by training the same individuals on categories in both modalities while in an fMRI scanner. Aim #1 of this investigation will identify the shared and sensory-biased circuits supporting feedback processing during auditory and visual category learning. If the neural circuits supporting perceptual category learning are shared across the modalities, it is expected that similar regions will be recruited to a similar extent during feedback processing. If instead, the neural circuits are distinct for particular modalities, it is expected that sensory-biased regions will emerge as supporting category learning for auditory and visual modalities. Aim #2 will utilize advanced machine learning techniques (multivariate pattern classification and representational similarity analyses) to characterize the emergence of category-level neural representations over the course of learning. Aim #3 will identify the functional and structural connectivity of the circuits as they contribute to perceptual category learning. The proposed research will directly test the fundamental assumption about the nature of this complex problem that affects everyday behaviors. This research has the potential to impact understanding of cases where modality- specific learning abilities might be impaired, such as phonetic learning and language-related impairments in dyslexia, autism, and specific language impairment. The proposed research will provide the training foundation to support the PI’s long-term objective of developing theories of perceptual category learning that are constrained by neurobiology and behavior and will specify the behavioral, computational, and neural mechanisms of such learning. This project presents the opportunity to directly test a critical assumption underlying understanding of perceptual category learning. The proposed research will take place in an exceptional training environment and the PI will be mentored by a team of knowledgeable and accomplished scientists. The research will provide the PI with training in functional magnetic resonance experiment design and analysis which will prepare her well for a career as an independent scientist in computational cognitive neuroscience.

项目概要/摘要 学习新的感知类别的能力使一些最复杂的人类行为成为可能，例如语音 感知到视觉对象识别。目前对知觉范畴所涉及机制的理解 学习依赖于这样一个基本假设：这种学习背后的过程是跨组织共享的。 感官。然而，这项工作的绝大多数都集中在视觉形态上。结果， 关于人类如何学习将复杂的听觉信息分类的研究在很大程度上依赖于 基于视觉领域研究的结论，而不测试这个关键假设。然而，最近 来自注意力文献的证据表明，即使是看似领域通用的认知过程，例如 作为工作记忆，是通过额叶皮层中的感觉偏向区域来完成的。目前的调查将 直接比较支持听觉和视觉类别学习的计算和神经机制 在功能磁共振成像扫描仪中对同一个人进行两种模式的类别培训。目标#1 调查将确定支持听觉过程中反馈处理的共享和感觉偏向电路 和视觉类别学习。如果支持感知类别学习的神经回路在整个系统中共享 方式，预计在反馈处理过程中，类似的区域将被以类似的程度招募。如果 相反，神经回路对于特定模式是不同的，预计感觉偏向区域将 作为听觉和视觉方式的支持类别学习而出现。目标#2将利用先进的机器 学习技术（多变量模式分类和代表性相似性分析）来表征 学习过程中类别级神经表征的出现。目标 #3 将确定 电路的功能和结构连接，因为它们有助于感知类别学习。这 拟议的研究将直接检验关于这个复杂问题的性质的基本假设： 影响日常行为。这项研究有可能影响对模式案例的理解—— 特定的学习能力可能会受到损害，例如语音学习和语言相关的障碍 阅读障碍、自闭症和特定语言障碍。拟议的研究将为培训提供基础 支持 PI 发展受约束的感知类别学习理论的长期目标 通过神经生物学和行为学，并将详细说明此类的行为、计算和神经机制 学习。该项目提供了直接测试基本理解的关键假设的机会 感知类别学习。拟议的研究将在特殊的培训环境中进行 PI 将由知识渊博且卓有成就的科学家团队提供指导。该研究将提供 PI 接受过功能磁共振实验设计和分析培训，这将为她做好准备 作为计算认知神经科学领域的独立科学家。