The effects of rhythmic structure on tapping accuracy

Abstract

By modelling tapping behaviour – rhythm-level tapping accuracy and pulse-level tap probabilities, velocities, and timing errors – to 91 unfamiliar and complex rhythms, we uncover novel cognitive mechanisms that play a role in temporal prediction. This was achieved with multilevel Bayesian regression using a large number of mostly new predictors, each derived from the structure of the rhythm and each indicative of one or more underlying cognitive mechanisms. This is the first synchronized tapping study to use such a wide variety of unfamiliar rhythms and the results show that, for these tricky rhythms, participants’ taps are disrupted by unfamiliar period lengths and are guided by rather crude encodings of each rhythm: the density of rhythmic cues, their circular mean and variance, and recognizing common small patterns and the approximate positions of groups of cues. These lossy encodings are often counterproductive for discriminating between cued and uncued pulses and are quite different to mechanisms – such as metricization and emphasizing group boundaries – thought to guide tapping behaviours in learned and familiar rhythms.

Hierarchical syntax models of music predict theta power during music listening

“…All continuous variables were standardised to have a mean of 0 and a standard deviation of 1, and the model was provided with a random effect for participant. Each model ran on 4 chains, was initialised with 0s, and was provided with a weakly informative prior in form of a t-distribution with mean 0, a standard deviation of 1, and 3 degrees of freedom (Gelman, Jakulin, Pittau, & Su, 2008), a prior commonly used in recent music perception studies (Beveridge, Cano, & Herff, 2021;Herff, Cecchetti, Taruffi, & Déguernel, 2021;MacRitchie, Breaden, Milne, & McIntyre, 2020;Milne, Dean, & Bulger, 2021;Smit, Dobrowohl, Schaal, Milne, & Herff, 2020). We then conducted hypothesis tests on the posterior of the fitted models for effects of prolongation and preparation in each region.…”

Information processing biases: The effects of negative emotional symptoms on sampling pleasant and unpleasant information.

“…All continuous predictors were scaled ( M = 0, SD = 1). The prior was chosen as it regularizes the effect for possible small-to-medium values (Milne et al, 2021). We used STAN’s default No-U-Turn sampler (Hoffman & Gelman, 2014), which is an extension of the Hamiltonian Monte Carlo algorithm (Duane et al, 1987; also see Neal, 2011).…”