Beyond immediate health risks, the COVID-19 pandemic poses a variety of stressors, which may require expensive or unavailable strategies during a pandemic (e.g., therapy, socialising). Here, we asked whether musical engagement is an effective strategy for socio-emotional coping. During the first lockdown period (April–May 2020), we surveyed changes in music listening and making behaviours of over 5000 people, with representative samples from three continents. More than half of respondents reported engaging with music to cope. People experiencing increased negative emotions used music for solitary emotional regulation, whereas people experiencing increased positive emotions used music as a proxy for social interaction. Light gradient-boosted regressor models were used to identify the most important predictors of an individual’s use of music to cope, the foremost of which was, intriguingly, their interest in “coronamusic.” Overall, our results emphasise the importance of real-time musical responses to societal crises, as well as individually tailored adaptations in musical behaviours to meet socio-emotional needs.

Beyond immediate health risks, the COVID-19 pandemic poses a variety of stressors, which may require expensive or unavailable strategies during a pandemic (e.g., therapy, socialising). Here we asked whether musical engagement is an effective strategy for socio-emotional coping. During the first lockdown period (April-May 2020), we surveyed changes in music listening and making behaviours of over 5000 people, with representative samples from 3 continents. More than half of respondents reported using music to cope. People experiencing increased negative emotions used music for solitary emotional regulation, whereas people experiencing increased positive emotions used music as a proxy for social interaction. Light gradient-boosted regressor models were used to identify the most important predictors of an individual’s use of music to cope, the foremost of which was, intriguingly, their interest in the novel genre of “coronamusic.” Overall, our results emphasise the importance of real-time musical responses to societal crises, as well as individually tailored adaptations in musical behaviours to meet socio-emotional needs.

While there is an increasing shift in cognitive science to study perception of naturalistic stimuli, this study extends this goal to naturalistic contexts by assessing physiological synchrony across audience members in a concert setting. Cardiorespiratory, skin conductance, and facial muscle responses were measured from participants attending live string quintet performances of full-length works from Viennese Classical, Contemporary, and Romantic styles. The concert was repeated on three consecutive days with different audiences. Using inter-subject correlation (ISC) to identify reliable responses to music, we found that highly correlated responses depicted typical signatures of physiological arousal. By relating physiological ISC to quantitative values of music features, logistic regressions revealed that high physiological synchrony was consistently predicted by faster tempi (which had higher ratings of arousing emotions and engagement), but only in Classical and Romantic styles (rated as familiar) and not the Contemporary style (rated as unfamiliar). Additionally, highly synchronised responses across all three concert audiences occurred during important structural moments in the music—identified using music theoretical analysis—namely at transitional passages, boundaries, and phrase repetitions. Overall, our results show that specific music features induce similar physiological responses across audience members in a concert context, which are linked to arousal, engagement, and familiarity.

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.

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 concert is a common event at which people gather to share a musical experience. While techniques are increasingly offering insights into naturalistic stimuli perception, this study extended methods to a more ecological context in order to explore real-world music listening within a concert setting. Cardiorespiratory, skin conductance, and facial muscle responses were measured from participants attending one of three concerts with live chamber music performances of works of varying Western Classical styles (Viennese Classical, Contemporary, and Romantic). Collective physiological synchronisation of audience members was operationalised via inter-subject correlation (ISC). By assessing which musical features (obtained via Music Information Retrieval and music-theoretical analyses) evoked moments of high synchrony, logistic regressions revealed that tempo consistently predicted physiological synchrony across all concerts in Classical and Romantic styles, but not the Contemporary style. Highly synchronised responses across all three concert audiences seemed to occur during structural transitional passages, boundaries, and at phrase repetitions. The results support the idea that group synchronisation is linked to musical arousal, structural coherence, and familiarity. By employing physiological ISC and an inter-disciplinary musical analysis, the current study demonstrates a novel approach to gain valuable insight into experiences of naturalistic stimuli in an ecological context.

Background and Objectives:Declines in stroke admission, intravenous thrombolysis, and mechanical thrombectomy volumes were reported during the first wave of the COVID-19 pandemic. There is a paucity of data on the longer-term effect of the pandemic on stroke volumes over the course of a year and through the second wave of the pandemic. We sought to measure the impact of the COVID-19 pandemic on the volumes of stroke admissions, intracranial hemorrhage (ICH), intravenous thrombolysis (IVT), and mechanical thrombectomy over a one-year period at the onset of the pandemic (March 1, 2020, to February 28, 2021) compared with the immediately preceding year (March 1, 2019, to February 29, 2020).Methods:We conducted a longitudinal retrospective study across 6 continents, 56 countries, and 275 stroke centers. We collected volume data for COVID-19 admissions and 4 stroke metrics: ischemic stroke admissions, ICH admissions, intravenous thrombolysis treatments, and mechanical thrombectomy procedures. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases.Results:There were 148,895 stroke admissions in the one-year immediately before compared to 138,453 admissions during the one-year pandemic, representing a 7% decline (95% confidence interval [95% CI 7.1, 6.9]; p<0.0001). ICH volumes declined from 29,585 to 28,156 (4.8%, [5.1, 4.6]; p<0.0001) and IVT volume from 24,584 to 23,077 (6.1%, [6.4, 5.8]; p<0.0001). Larger declines were observed at high volume compared to low volume centers (all p<0.0001). There was no significant change in mechanical thrombectomy volumes (0.7%, [0.6,0.9]; p=0.49). Stroke was diagnosed in 1.3% [1.31,1.38] of 406,792 COVID-19 hospitalizations. SARS-CoV-2 infection was present in 2.9% ([2.82,2.97], 5,656/195,539) of all stroke hospitalizations.Discussion:There was a global decline and shift to lower volume centers of stroke admission volumes, ICH volumes, and IVT volumes during the 1st year of the COVID-19 pandemic compared to the prior year. Mechanical thrombectomy volumes were preserved. These results suggest preservation in the stroke care of higher severity of disease through the first pandemic year.Trial Registration Information:This study is registered underNCT04934020.

The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we provide recommendations and offer an up-to-date account of how pupil data can be analyzed in hypothesis-testing experiments. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements, visual features (e.g., luminance), and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. We discuss pre-processing steps and contend that the insights gained from pupillometry are constrained by the analysis techniques available. Then, in addition to the traditional approach of analyzing mean pupil size within some epoch of interest, we focus on time series-based analyses, which enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus series, task of interest, behavioral outcome measures, or other participants' pupil traces. Analytic techniques considered include: correlation (auto-, and cross-, reverse-, and inter/intra-subject-), regression (including temporal response functions), classification, dynamic time warping, phase clustering, magnitude squared coherence, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages.