A linear oscillator model predicts dynamic temporal attention and pupillary entrainment to rhythmic patterns

Abstract

Rhythm is a ubiquitous feature of music that induces specific neural modes of processing. In this paper, we assess the potential of a stimulus-driven linear oscillator model (Tomic & Janata, 2008) to predict dynamic attention to complex musical rhythms on an instant-by-instant basis. We use perceptual thresholds and pupillometry as attentional indices against which to test our model pre- dictions. During a deviance detection task, participants listened to continuously looping, multi- instrument, rhythmic patterns, while being eye-tracked. Their task was to respond anytime they heard an increase in intensity (dB SPL). An adaptive thresholding algorithm adjusted deviant in- tensity at multiple probed temporal locations throughout each rhythmic stimulus. The oscillator model predicted participants’ perceptual thresholds for detecting deviants at probed locations, with a low temporal salience prediction corresponding to a high perceptual threshold and vice versa. A pupil dilation response was observed for all deviants. Notably, the pupil dilated even when partic- ipants did not report hearing a deviant. Maximum pupil size and resonator model output were sig- nificant predictors of whether a deviant was detected or missed on any given trial. Besides the evoked pupillary response to deviants, we also assessed the continuous pupillary signal to the rhythmic patterns. The pupil exhibited entrainment at prominent periodicities present in the stimuli and followed each of the different rhythmic patterns in a unique way. Overall, these results repli- cate previous studies using the linear oscillator model to predict dynamic attention to complex auditory scenes and extend the utility of the model to the prediction of neurophysiological signals, in this case the pupillary time course; however, we note that the amplitude envelope of the acoustic patterns may serve as a similarly useful predictor. To our knowledge, this is the first paper to show entrainment of pupil dynamics by demonstrating a phase relationship between musical stimuli and the pupillary signal.

Microtiming and Mental Effort

“…Magnitude and latency of early EEG-signals appear to mirror the mismatch between expectation and what is heard (e.g., Honing, 2012;Vuust et al, 2005), so that this response has been interpreted as reflecting the degree of expectation violation between rhythm and underlying meter. Interestingly, similar to the MMN component, pupil dilations can also signal metric violation/''surprise'' effects to single rhythmic deviants (Damsma & van Rijn, 2017;Fink, Hurley, Geng, & Janata, 2018). However, an alternative approach is to monitor time-averaged pupillary diameters while participants listen continuously to running rhythms with varying degrees of complexity, but without time-locked, single deviants.…”

Musical and Bodily Predictors of Mental Effort in String Quartet Music: An Ecological Pupillometry Study of Performers and Listeners

“…In a musical context, patterns of pupil dilations reflect listeners' entrainment with musical rhythms (Fink et al, 2018) and listeners' attention to deviations from strict rhythmic regularity. These deviations are referred to as "microtiming" in the context of groove-based jazz music (Skaansar et al, 2019), but are also a common feature of expressively-performed music in many traditions.…”

Music Rhythm Detection Algorithm Based on Multipath Search and Cluster Analysis

“…e acquisition of music information can be roughly divided into three research fields based on the research content and technical difficulty: onset detection of music events [7], which is an intermediate medium for acquiring other advanced music information, and the acquired starting point signal sequence called the onset detection function (ODF); the advanced music feature acquisition based on the onset detection function, such as the analysis of ODF to obtain the pitch, speed, rhythm, beat, bar, chord, or extraction of signal features of specific musical instruments; higher-level music understanding, such as Music Genre Classification, Music Mood Recognition, and Music Tag Classification [8]. Previous articles mainly focused on the acquisition of music rhythm features based on the starting point detection function, which focuses on the music beat tracking technology and briefly gives a method for estimating the tempo and beat structure.…”