New methods for oscillation analyses push new theories of discrete cognition

Abstract

Classical ways of analyzing neural time series data has led to static views on cognition, in which the cognitive processes are linked to sustained neural activity and interpreted as stationary states. The core analytical focus was on slow power modulations of neural oscillations averaged across many experimental trials. Whereas this custom analytical approach reduces the complexity and increases the signal-to-noise ratio, it may disregard or even remove important aspects of the underlying neural dynamics. Novel analysis methods investigate the instantaneous frequency and phase of neural oscillations and relate them to the precisely controlled timing of brief successive sensory stimuli. This enables to capture how cognitive processes unfold in discrete windows within and across oscillatory cycles. Moreover, several recent studies analyze the oscillatory power modulations on single experimental trials. They suggest that the power modulations are packed into discrete bursts of activity, which occur at different rates and times, and with different durations from trial-to-trial. Here, we review the current work that made use of these methodological advances for neural oscillations. These novel analysis perspectives emphasize that cognitive processes occur in discrete time windows, instead of sustained, stationary states. Evidence for discretization was observed for the entire range of cognitive functions from perception and attention to working memory, goal-directed thought and motor actions, as well as throughout the entire cortical hierarchy and in subcortical regions. These empirical observations create demand for new psychological theories and computational models of cognition in the brain, which integrate its discrete temporal dynamics.