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

Lauren K Fink,Brian K Hurley,Joy J Geng,Petr Janata

doi:10.16910/jemr.11.2.12

Lauren K Fink, Brian K Hurley + Show 2 more

Open Access

https://doi.org/10.16910/jemr.11.2.12

Copy DOI

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 (57) 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 predictions. During a deviance detection task, participants listened to continuously looping, multiinstrument, 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 intensity 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 participants did not report hearing a deviant. Maximum pupil size and resonator model output were significant 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 replicate 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.

Highlights

Though diverse forms of music exist across the globe, all music shares the property of evolving through time
As widely recommended (e.g. by Burnham and Anderson (2004)), we rescaled Akaike’s Information Criterion (AIC) values to represent the amount of information lost if choosing an alternative model, as opposed to the preferred model, with the equation:
We hypothesized that the linear oscillator model would predict perceptual thresholds for detecting intensity deviants that were adaptively embedded into our stimuli, as well as the continuous pupillary response to the stimuli

Summary

Introduction

Though diverse forms of music exist across the globe, all music shares the property of evolving through time. Modes, meters, or timbres may be more or less prevalent depending on the culture in question, the use of time to organize sound is universal. (2018) Pupillometry and Temporal Attention fore, rhythm, one of the most basic elements of music, provides an excellent scientific starting point to begin to question and characterize the neural mechanisms underlying music-induced changes in motor behavior and attentional state. To remain consistent with previous literature, here rhythm is defined as patterns of duration, timing, and stress in the amplitude envelope of an auditory signal (a physical property), whereas meter is a perceptual phenomenon that tends to include the pulse (beat or tactus) frequency perceived in a rhythmic sequence, as well as slower and faster integer-related frequencies (London, 2012)

Methods

Results

Conclusion