Rhythmicity Revisited: Evidence from Robust Behavioral Oscillations

Rhythmic fluctuations in neuronal excitability, called neural oscillations, pervade brain activity. If these oscillations have a functional role in behavior, they should also be expressed in a behavioral signature. However, identifying such a behavioral signature has remained controversial: evidence for behavioral oscillations is not yet firmly established, and aperiodic components can be misidentified as rhythmic. To test their existence while controlling for aperiodic dynamics, we applied a state-of-the-art autoregressive (AR) approach on two datasets: one published dataset, and a new dataset collected with a revised dense-sampling design. In both datasets, AR modeling recovered reliable behavioral oscillations in the theta band. Reanalyses of the published dataset also reproduced the reported theta shift with task demands, with more difficult tasks showing slower theta. In contrast, despite clear rhythmicity, the new dataset showed no consistent frequency shift across conditions. Overall, this study establishes rigorous and robust detection of behavioral oscillations. It also suggests that the frequency modulation of behavioral oscillations by task difficulty requires further study.