Speaker
Description
Acoustic regularities in human-generated sounds, such as speech and music, can be leveraged to improve neural processing and comprehension. Over the past several decades, a longstanding debate has centered on whether neural oscillations, as measured with M/EEG, actively extract this temporal information to enhance processing, or whether these neural signals merely reflect temporal structure already present in the stimulus. This presentation will briefly summarize the key outcomes of this debate and argue that oscillator-based and evoked-response theories should be integrated into a novel framework of complex potentiated dynamics. This framework is developed through dynamical computational modeling and validated using both surface and intracranial recordings. I will demonstrate how this approach suggests that the future of surface recordings in audition—and cognition more broadly—will be fundamentally tied to our ability to generate functional models that bridge dynamical and cognitive frameworks, thereby improving inference about successful brain function.