Mapping the dynamic allocation of temporal attention in musical patterns.

Abstract

Many environmental sounds, such as music or speech, are patterned in time. Dynamic attending theory, and supporting empirical evidence, suggests that a stimulus's temporal structure serves to orient attention to specific moments in time. One instantiation of this theory posits that attention synchronizes to the temporal structure of a stimulus in an oscillatory fashion, with optimal perception at salient time points or oscillation peaks. We examined whether a model consisting of damped linear oscillators succeeds at predicting temporal attention behavior in rhythmic multi-instrumental music. We conducted 3 experiments in which we mapped listeners' perceptual sensitivity by estimating detection thresholds for intensity deviants embedded at multiple time points within a stimulus pattern. We compared participants' thresholds for detecting intensity changes at various time points with the modeled salience prediction at each of those time points. Across all experiments, results showed that the resonator model predicted listener thresholds, such that listeners were more sensitive to probes at time points corresponding to greater model-predicted salience. This effect held for both intensity increment and decrement probes and for metrically simple and complex stimuli. Moreover, the resonator model explained the data better than did predictions based on canonical metric hierarchy or auditory scene density. Our results offer new insight into the temporal orienting of attention in complex auditory scenes using a parsimonious computational model for predicting attentional dynamics. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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

“…However, we remain agnostic as the actual beat periodicity and metric periodicities listeners perceived in the stimuli, as we leave such predictions to the linear oscillator model. Each stimulus pattern lasted 2.2 s. We use the same stimulus names as in Hurley et al (2018) for consistency. All stimuli can be accessed in the supplemental material of Hurley et al (2018).…”

“…Each stimulus pattern lasted 2.2 s. We use the same stimulus names as in Hurley et al (2018) for consistency. All stimuli can be accessed in the supplemental material of Hurley et al (2018).…”

“…Because the ZEST procedure aims to minimize variance, using reversals as a stopping rule, like in the case of a staircase procedure, does not make sense. Here, we used 20 observations as a stopping rule because Marvit et al (2003) showed that 18 observations were sufficient in a similar auditory task and Hurley et al (2018) demonstrated that, on average, 11 trials allowed for reliable estimation of perceptual threshold when using a dynamic stopping rule. For the current study, 20 was a conservative choice for estimating thresholds, which simultaneously enabled multiple observations over which to average pupillary data.…”

“…We also expected to see phasic pupil dilation responses to intensity deviants. As in Hurley et al (2018), we used an adaptive thresholding procedure to probe participants' perceptual thresholds for detecting intensity increases (dB SPL) inserted at multiple time points throughout realistic, multi-part rhythmic stimuli. Each probed position within the stimulus had a corresponding value in terms of the model's temporal salience predictions.…”

“…For full details about the architecture of the model and the periodicity surface calculations, see Tomic and Janata (2008). For details about the temporal salience calculations, please see Hurley et al (2018).…”