Abstract
The aim of this study was to test different methods for distinguishing between two known timing processes involved in human rhythmic behaviours. We examined the implementation of two approaches used in the literature: the high-frequency slope of the power spectrum and the lag one value of the autocorrelation function, ACF(1). We developed another method based on the Wing and Kristofferson Citation(1973a) model and the predicted negative ACF(1) for event-based series: the detrended windowed (lag one) autocorrelation (DWA). We compared the reliability and performance of these three methods on simulation and experimental series. DWA gave the best results, and guidelines are given for its appropriate use for identifying underlying timing processes.