Linking Nonlinear Neural Dynamics to Single‐Trial Human Behavior

Abstract

Human neural dynamics are complex and high dimensional. There seem to be limitless possibilities for developing novel data-driven analyses to examine patterns of activity that unfold over time, frequency, and space, as well as interactions within and among these dimensions. A better understanding of the neurophysiological mechanisms that support cognition, however, requires linking these complex neural dynamics to ongoing behavioral performance. Performance on cognitive tasks (measured, e.g., via response accuracy and reaction time) typically varies across trials, thus providing a means to determine which neural dynamical processes are related to which cognitive processes. In this chapter, we highlight several methods for linking nonlinear neural dynamics to behavioral dynamics, based on oscillatory phase, phase-based synchronization, and phase–amplitude cross-frequency coupling. In general, the approach of linking nonlinear neural dynamics based on phase values with trial variations in task performance has two significant advantages for understanding neurocognitive processes: (1) they allow researchers to distinguish those neural dynamics specifically related to cognitive task performance from other neural dynamics that reflect more generic background neural dynamics; and (2) oscillation phase has been linked to a variety of synaptic, cellular, and systems-level phenomena implicated in learning, information processing, and network formation, and therefore provide a neurophysiologically grounded framework within which to interpret results.