Neuroimaging research has advanced our understanding of human speech and language processing by providing insights about how speech sounds are processed in the brain. Most current fMRI studies lack the statistical and descriptive power to resolve complex information encoded in distributed patterns of activity, making them an awkward fit to the complexities of speech perception in the real world. In contrast, recent studies using novel multivariate approaches to fMRI analysis have revealed graded, distributed patterns of neural activity that promise to provide detailed, quantitative descriptions of perceptual categorization in the brain. Categorical perception requires enhancing contrast between stimuli of different categories and enhancing similarity between stimuli from the same category. The first specific aim of this proposal is to discover patterns of activity related to this process. High-resolution, event-related fMRI data will be collected while subjects passively listen to many unique, naturalistically resynthesized syllables. Patterns of activity that can be used to identify stimulus categories will be identified, and analyzed using multidimensional scaling analyses to explore the perceptual similarity space they define. The same type of analysis will then be applied to behavioral data, resulting in a novel means of exploring brain-behavior relationships. The second specific aim is focused on exploring methodological issues presented by multivariate analysis of speech categorization, in particular: What is the best way to identify patterns of neural activity that contain information about stimulus identity? A neural network classifier will be trained to determine which syllable was presented on each trial based on the neural response to that stimulus. A series of tests will then be conducted to determine whether this approach provides advantages over standard univariate techniques, or whether the complementary strengths of classifier- and univariate-based methods can be combined. A number of technical details regarding these analyses will be explored in detail, in order to arrive at a set of """"""""best practices"""""""" that will permit this technique to be optimally integrated into a larger program of research including multi-modality neuroimaging and behavioral studies.
If successful, the proposed research will provide a new set of analytic tools for the study of the neural basis of speech perception. These techniques will be applicable to research on adult processing, typical development, and a range of communication disorders -- including dyslexia and specific language impairment -- in which speech perception deficits may play a central role. Specifically, it will provide a means to better characterize individual differences in the representation of speech categories, and to explore more detailed hypotheses about the nature of deficits in different disorders than is possible with currently predominant techniques.
|Emberson, Lauren L; Liu, Ran; Zevin, Jason D (2013) Is statistical learning constrained by lower level perceptual organization? Cognition 128:82-102|
|Yang, Jianfeng; Wang, Xiaojuan; Shu, Hua et al. (2012) Task by stimulus interactions in brain responses during Chinese character processing. Neuroimage 60:979-90|
|Wang, Xiaojuan; Yang, Jianfeng; Shu, Hua et al. (2011) Left fusiform BOLD responses are inversely related to word-likeness in a one-back task. Neuroimage 55:1346-56|
|Yang, Jianfeng; Wang, Xiaojuan; Shu, Hua et al. (2011) Brain networks associated with sublexical properties of Chinese characters. Brain Lang 119:68-79|
|Zevin, Jason D; Yang, Jianfeng; Skipper, Jeremy I et al. (2010) Domain general change detection accounts for ""dishabituation"" effects in temporal-parietal regions in functional magnetic resonance imaging studies of speech perception. J Neurosci 30:1110-7|
|Yang, Jianfeng; McCandliss, Bruce D; Shu, Hua et al. (2009) Simulating Language-specific and Language-general Effects in a Statistical Learning Model of Chinese Reading. J Mem Lang 61:238-257|