This project integrates knowledge from the fields of computational neuroscience, machine learning, and non-invasive brain stimulation to approach a long-standing problem in rehabilitation therapy with a novel perspective.
This project combines the field of motor control, which seeks to determine the behavioral consequences of learning to move (e.g., how does the brain adapt the next movement following an error?), and neurophysiology, which attempts to identify the correlates of this motor learning in the firing of neurons in the brain.
This project integrates physical measures, dietary measures, and brain recording methods to explore the neural mechanisms underlying distraction by high-fat foods and explore how these are moderated by individual differences in dietary characteristics.
This project integrates the perspectives of the cognitive neuroscientist, educational researcher, and classroom teacher. Both behavioral and neuroimaging methodologies will be used to address the same issues at both cognitive and neural levels of analysis. The results will address outstanding questions about the role of modality in literacy development by bringing to bear, for the first time, both neural and behavioral measures to a longitudinal design and generate findings that can be implemented in actual classroom settings to improve the learning of a new alphabet letters by second language learners.
This project brings insights from machine learning, cognitive science, and linguistic theory to bear on a long-standing question in language learning: how words are learned. It does this by constructing explicit computer models of what is going on in a speaker’s mind when they are learning a word. Humans are much better than machines at understanding human language. This research aims to construct explicit models of language learning that will make computers better at understanding human language.
Can brain stimulation improve the outcomes of a reading comprehension training intervention in high-functioning individuals with autism? This fellowship project bridges education and cognitive neuroscience interventions to improve reading comprehension in individuals with autism. This project will inform the development of future literacy interventions and how they can better target the underlying neurobiology in autism.
How do listeners adapt to speaker-specific acoustic variation in speech? Is there structured variability in speech that can be learned at the speaker level? This fellowship integrates approaches from speech perception, phonetics, and automatic speech recognition (ASR) to address the range and limits of speaker variability that must be learned for successful speech perception. The results from this project can inform techniques for improved speech perception in hearing impaired and cochlear implant patients, as well as talker adaptation in both cognitive and automatic speech recognition systems.
How does working memory training improve cognitive ability? This fellowship project combines cognitive and clinical psychology with electrophysiological methods to determine how working memory training is effective in improving general cognitive ability, and how deficits in attention influence training outcomes. The results of this research will help us understand how working memory training improves general cognitive ability. The results will also inform theories of learning and memory and provide new directions for improving training programs for specific populations with working memory deficits, such as individuals with ADHD.