Shared and specific mechanisms of auditory and visual category learning

项目摘要/摘要 学习新的知觉类别的能力使一些最复杂的人类行为能够从语言中 从感知到视觉对象识别。关于知觉范畴涉及机制的最新认识 学习依赖于这样一个基本假设，即这种学习的基础过程是在 感官。然而，这项工作的绝大多数都集中在视觉形态上。因此， 关于人类如何学习将复杂的听觉信息归类的研究在很大程度上依赖于 基于视觉领域的研究结论，而不检验这一关键假设。然而，最近 来自注意力文献的证据表明，即使是看似泛化的认知过程，如 作为工作记忆，是通过额叶皮质的感觉偏向区域完成的。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 在功能核磁共振扫描仪中，在两种模式下对相同的人进行类别培训。目标1 研究将确定在听觉过程中支持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 在模式方面，预计在反馈处理过程中将以类似的程度招募类似的区域。如果 相反，神经回路对于特定的模式是不同的，预计感觉偏向区域将 作为听觉和视觉通道的辅助类别学习而出现。Aim#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 在学习过程中出现了类别级别的神经表征。目标3将确定 神经回路的功能和结构连接，因为它们有助于知觉类别学习。这个 拟议的研究将直接检验关于这个复杂问题性质的基本假设 影响日常行为。这项研究有可能影响对情态-- 特定的学习能力可能受到损害，如语音学习和与语言有关的障碍 阅读障碍、自闭症和特殊语言障碍。拟议的研究将提供培训基础 支持PI的长期目标，即发展受限制的知觉类别学习理论 通过神经生物学和行为学，并将指定行为，计算和神经机制 学习。该项目提供了直接测试一个关键假设的机会，该假设基于对 知觉范畴学习。拟议的研究将在特殊的培训环境中进行， PI将由一支知识渊博、成就斐然的科学家团队指导。这项研究将提供 PI接受了功能磁共振实验设计和分析方面的培训，这将使她为 作为计算认知神经科学领域的一名独立科学家。

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.