Robust evidence for theta-band rhythmicity in behavior across two dense-sampling datasets
- 1Department of Experimental Psychology, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
- 2Univ Toulouse, Université Toulouse Jean Jaurès, CLLE, CNRS, Toulouse, France
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.
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BibTeX
@article{xu2026robust,
title = {{Robust evidence for theta-band rhythmicity in behavior across two dense-sampling datasets}},
author = {Mengting Xu and Esperanza Badaya and Mehdi Senoussi and Tom Verguts},
journal = {Communications Psychology},
year = {2026},
month = jul,
doi = {10.1038/s44271-026-00500-0},
url = {https://doi.org/10.1038/s44271-026-00500-0},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Robust evidence for theta-band rhythmicity in behavior across two dense-sampling datasets
AU - Mengting Xu
AU - Esperanza Badaya
AU - Mehdi Senoussi
AU - Tom Verguts
PY - 2026
DA - 2026/07/07
JO - Communications Psychology
DO - 10.1038/s44271-026-00500-0
UR - https://doi.org/10.1038/s44271-026-00500-0
AB - 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.
SN - 2731-9121
ER -