Abstract 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 behavioral signatures remains challenging and continues to motivate debate, particularly because aperiodic temporal structure can sometimes be mistaken for rhythmic activity. To assess behavioral rhythmicity while controlling for aperiodic dynamics, we applied a state-of-the-art autoregressive modelling approach to two datasets: one published dataset (final sample: n = 34), and a new dataset (final sample: n = 26) collected with an adapted dense-sampling design. In both datasets, AR modelling 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.
Xu et al. (Tue,) studied this question.