Abstract representations underlie rhythm perception and production: Evidence from a probabilistic model of temporal structure

Cognition, vol. 268, pp. 106345

Abstract

Rhythm, such as in music, contains structure in the form of rhythmic patterns: the more or less predictable successions of longer and shorter intervals (i.e., the "morse code" of the rhythm). Listeners can use rhythmic patterns to predict the timing of sounds and guide their perception and action. It is still unclear how rhythmic patterns are represented in the human mind. Here, we used a probabilistic model of auditory expectations to simulate the perception and production of rhythmic patterns. We modelled expectations in rhythmic sequences at three different levels of abstraction: as the predictability of absolute inter-onset intervals (IOI), ratios between successive intervals (ratio), and the direction of change of successive intervals (contour). Subsequently, we selected rhythms that varied maximally in their modelled predictability across the three levels of abstraction for three behavioral tasks: a target detection task in which the rhythm was not task-relevant (implicit task), a complexity rating task (explicit task), and a tapping task (motor task). We found that both ratio and contour affected behavioral responses across all tasks, with the largest effects in the explicit rating task. IOI only affected responses for the explicit and motor tasks, where the rhythm was task-relevant, and to a greater extent when an imprecise, categorical representation of IOI was assumed. These findings suggest that humans rely mostly on imprecise representations of rhythmic patterns, but may flexibly adapt their representation based on task demands.

Authors 5

1. Fleur L. Bouwer corresponding Aachen

   Leiden University · University of Amsterdam

   Affiliation as printed

   Cognitive Psychology Unit, Institute of Psychology & Leiden Institute for Brain and Cognition, Leiden University, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, the Netherlands; Department of Psychology, University of Amsterdam, the Netherlands. Electronic address: f.l.bouwer@fsw.leidenuniv.nl

2. Atser Damsma

   Amsterdam University of the Arts · University of Amsterdam

   Affiliation as printed

   Amsterdam Brain and Cognition, University of Amsterdam, the Netherlands; Music Cognition Group, Institute for Logic, Language and Computation, University of Amsterdam, The Netherlands; Conservatorium van Amsterdam, Amsterdam University of the Arts, the Netherlands

3. Thomas M. Kaplan

   Queen Mary University of London

   Affiliation as printed

   School of Electronic Engineering and Computer Science, Queen Mary University of London, United Kingdom

4. Mohsen Ghorashi Sarvestani

   University of Amsterdam

   Affiliation as printed

   Department of Psychology, University of Amsterdam, the Netherlands

5. Marcus Thomas Pearce

   Queen Mary University of London · Aarhus University

   Affiliation as printed

   School of Electronic Engineering and Computer Science, Queen Mary University of London, United Kingdom; Department of Clinical Medicine, Aarhus University, Denmark