Investigating neural mechanisms for flexible, robust speech perception with fMRI

描述（由申请人提供）：大脑受到来自世界的感官信息的轰炸，必须使用有限的神经资源提取某些有用的信息。这意味着大脑必须是高效的，丢弃不需要的信息，以便专注于来自世界的感官信息中最重要的部分。然而，在一种情况下没有信息的信息在另一种情况下可能是非常有用的，因此这种效率必须与灵活性相匹配。在语音感知领域尤其如此，一个嘈杂的、模糊的感觉信号被映射到潜在的语言单位，如音素、单词和句子。这种映射根据说话人的不同而有很大的变化。大脑可能处理这个问题的一种方法是学习特定于说话人的表征，这种表征可以优化处理语音的效率，并在说话人改变时部署或“换掉”这些表征，必要时为新的说话人学习新的表征。虽然有一些证据表明听众确实使用了这种策略，但对其潜在的神经机制知之甚少。这项建议试图通过两个具体目标来澄清这些机制。首先，功能磁共振成像（fMRI）将对听者在听到两个口音不同的说话者混合说话时的大脑进行成像。通过比较说话者转换时活跃的区域和学习每种口音时活跃的区域（通过行为反应来衡量），听者学习和部署特定说话者表征的回路将得到阐明。其次，利用多体素模式分析技术，测量不同说话者对相同语音的不同解释的神经表征，以确定说话者特定知识对语音处理的影响程度。如果在低水平上使用说话人特定知识来优化感知加工的效率，那么类别内差异应该导致更相似的活动模式，而跨类别差异应该导致更不同的活动模式。

DESCRIPTION (provided by applicant): The brain is bombarded by sensory information from the world, and must extract certain pieces of useful information using limited neural resources. This means that the brain must be efficient, throwing away information that is not needed in order to focus on the most important part of the sensory information from the world. However, information that is uninformative in one situation may be highly informative in another, and thus this efficiency must be matched by flexibility. One domain where this is particularly true is speech perception, where a noisy, ambiguous sensory signal is mapped onto underlying linguistic units like phonemes, words, and sentences. This mapping changes substantially depending on who is talking. One way the brain might deal with this is to learn talker-specific representations which optimize the efficiency with which speech sounds are processed, and deploy or "swap out" those representations whenever the talker changes, learning new representations for new talkers as necessary. While there is some evidence that listeners do use such a strategy, little is known about the underlying neural mechanisms. This proposal seeks to clarify these mechanisms through two specific aims. First, functional magnetic resonance imaging (fMRI) will image the brains of listeners while they are hearing words from two talkers with different accents, mixed together. By comparing the areas that are active when the talker switches with areas that are active during periods of learning about each accent (as measured by behavioral responses), the circuits by which listeners learn and deploy talker-specific representations will be elucidated. Second, using multi-voxel pattern analysis techniques, the neural representations of identical speech sounds which have different interpretations depending on the talker will be measured to determine how deeply talker-specific knowledge affects the processing of speech sounds. If talker-specific knowledge is being used to optimize the efficiency of perceptual processing at a low level, then within-category differences should result in more similar patterns of activity, while across-category differences should result in more distinct patterns of activity.