Sensorimotor synchronisation (SMS) is prevalent and readily studied in musical settings, as most people are able to perceive and synchronise with a beat (e.g., by finger tapping). We took an individual differences approach to understanding SMS to real music characterised by expressive timing (i.e., fluctuating beat regularity). Given the dynamic nature of SMS, we hypothesised that individual differences in working memory and auditory imagery-both fluid cognitive processeswould predict SMS at two levels: (1) mean absolute asynchrony (a measure of synchronisation error) and (2) anticipatory timing (i.e., predicting, rather than reacting to beat intervals). In Experiment 1, participants completed two working memory tasks, four auditory imagery tasks, and an SMS-tapping task. Hierarchical regression models were used to predict SMS performance, with results showing dissociations among imagery types in relation to mean absolute asynchrony, and evidence of a role for working memory in anticipatory timing. In Experiment 2, a new sample of participants completed an expressive timing perception task to examine the role of imagery in perception without action. Results suggest that imagery vividness is important for perceiving and control is important for synchronising with irregular but ecologically valid musical time series. Working memory is implicated in synchronising by anticipating events in the series.

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1/ f fluctuations have been described in numerous physical and biological processes. This noise structure describes an inverse relationship between the intensity and frequency of events in a time series (for example reflected in power spectra), and is believed to indicate long-range dependence, whereby events at one time point influence events many observations later. 1/ f has been identified in rhythmic behaviors, such as music, and is typically attributed to long-range correlations. However short -range dependence in musical performance is a well-established finding and past research has suggested that 1/ f can arise from multiple continuing short-range processes. We tested this possibility using simulations and time-series modeling, complemented by traditional analyses using power spectra and detrended fluctuation analysis (as often adopted more recently). Our results show that 1/ f -type fluctuations in musical contexts may be explained by short-range models involving multiple time lags, and the temporal ranges in which rhythmic hierarchies are expressed are apt to create these fluctuations through such short-range autocorrelations. We also analyzed gait, heartbeat, and resting-state EEG data, demonstrating the coexistence of multiple short-range processes and 1/ f fluctuation in a variety of phenomena. This suggests that 1/f fluctuation might not indicate long-range correlations, and points to its likely origins in musical rhythm and related structures.