Word recognition is crucial not only for comprehending spoken language but for mapping spoken words onto text in reading. Individuals with language and reading deficits (e.g., Specific Language Impairment, Dyslexia, Autism, which together affect up to 16% of children) have been shown to have deficits in word recognition, making it crucial to understand this process. A hallmark of word recognition is that listeners activate neural representations of multiple candidate words that are consistent with the early acoustic input, and these candidates compete for recognition as they unfold in real-time. The overall goal of this proposal is to capitalize on recent developments in multivariate and machine- learning techniques for analyzing signals obtained from the human brain to measure the real-time unfolding of spoken word recognition. Although these techniques have been most widely used with fMRI data, we propose to extend them to EEG data because EEG is easily used with children and clinical populations, and provides access to the time-course of word recognition, thereby revealing underlying cognitive mechanisms of word recognition, such as lexical competition. Our preliminary findings using this EEG-based paradigm have demonstrated that we can decode the recognition of a specific word (among a set of 8-12 alternatives) at each msec time-step after stimulus onset. The method is sensitive to partial activation of competing words that share some phonological features with the target word, thereby revealing the dynamics of lexical competition as the word-recognition system settles on the final target. Our objectives are to conduct a series of small-scale experiments that achieve three aims. First, we develop and optimize the method with adults (e.g., the experimental procedure and computational implementation). Second, we validate the method with adults by measuring its test/re-test reliability, comparing its estimates of word recognition with traditional behavioral paradigms, and examining how lexical status, and semantic and orthographic expectations shape lexical competition revealed by the EEG measure. This will yield a new, non-invasive, and highly reliable method suitable for assessing spoken word recognition in adults, children, and special populations. Third, we will preliminarily extend the method to children to pave the way for future developmental studies. Taken together, accomplishing these three aims would provide an innovative and powerful tool for assessing a crucial component of language processing in a wide variety of typical and atypical populations.
The proposed research will impact public health by establishing a new EEG-based paradigm for understanding how listeners recognize spoken words. Word recognition is crucial not only for comprehending spoken language but for mapping spoken words onto text in reading. Individuals with language and reading deficits (e.g., Specific Language Impairment, Dyslexia, Autism, which together affect up to 16% of children) have been shown to have deficits in word recognition, making it crucial to understand this process. A hallmark of word recognition is that listeners activate neural representations of multiple candidate words that are consistent with the early acoustic input, and these candidates compete for recognition as they unfold in real-time. Our project will develop and optimize a new method in which machine learning tools are used to decode EEG-based neural signals to characterize the time-course of competition. Such a method offers the prospect of a diagnostic tool to identify young children who are at risk for communication disorders.
