Geoff Luck

Music artists play a crucial role in shaping both regional and global sonic trends. While technological and cultural shifts are often credited with driving musical change, less is known about how an artist’s age influences stylistic evolution. Here, we examine how acousticness, that is, the extent to which a track features acoustic instrumentation, varies across an artist’s career. Using data from Spotify’s API, we analyse 45,478 tracks from 190 artists with a Generalised Additive Mixed Model (GAMM) to assess the relationship between acousticness and artist age, controlling for release year and genre. Results reveal a consistent positive association between artist age and acousticness, with artists tending to produce more acoustic music as they age, irrespective of their genre. However, the release year shows a strong negative effect, with newer music increasingly characterised by fewer acoustic features, likely reflecting broader industry trends such as digital production and evolving listener preferences. Our findings suggest a dynamic interplay between individual artistic development and external cultural forces, where ageing artists gravitate towards more organic sounds, even as popular music trends continue to favour synthetic production.

Correspondences between the timing of motor behaviour and that of musical performance are well-established. Motor behaviour, however, is known to degrade across the adult lifespan due to neurobiological decay. In particular, performance on speed-dependent motor tasks deteriorates, spontaneous motor tempo (SMT) slows, and upper motor rate limit falls. Here, we examine whether this slowdown in motor behaviour impacts the tempo of musical performance as a function of age. We analysed 13,180 songs released between 1956 and 2020 by artists with careers spanning at least 20 years. Generalised Additive Mixed Models (GAMMs) and Linear Mixed Models (LMMs) were employed to assess the effects of age, operationalised by subtracting the birth year from the release year of each track, on musical tempo. Results revealed a slight tempo increase from early adulthood to age 30, followed by a marked, linear slowdown with age across the remainder of the lifespan. From artists’ thirties to their eighties, tempo decreased by almost 10 bpm, averaging around 2 bpm per decade. This decrease aligns with the slowing-with-age hypothesis and mirrors rates of decline observed in studies of SMT and gait speed. Our findings highlight a significant gap in the understanding of creative performance across the lifespan, particularly the role of age as a mediating factor in musical tempo. Moreover, that a discernible decrease in the tempo is apparent even in commercial recordings further emphasises the inescapable connection between the dynamics of motor behaviour and the timing of musical performance.

Background: Personalized Interactive Music Systems (PIMSs) are emerging as promising devices for enhancing physical activity and exercise outcomes. By leveraging real-time data and adaptive technologies, PIMSs align musical features, such as tempo and genre, with users’ physical activity patterns, including frequency and intensity, enhancing their overall experience. Objective: This exploratory systematic review and meta-analysis evaluates the effectiveness of PIMSs across physical, psychophysical, and affective domains. Methods: Searches across 9 databases identified 18 eligible studies, of which 6 (comprising 17 intervention arms) contained sufficient data for meta-analysis. Random-effects meta-analyses and meta-regression were performed to assess outcomes for physical activity levels, physical exertion, ratings of perceived exertion, and affective valence. Results: Results showed significant improvements in physical activity levels (g=0.49, CI 0.07 to 0.91, P=.02, k=4) and affective valence (g=1.65, CI 0.35 to 2.96, P=.01, k=4), with faster music tempo identified as a significant moderator (P=.03). No significant effects were observed for ratings of perceived exertion (g=0.72, CI −0.13 to 1.58, P=.10, k=3) or physical exertion (g=0.78, CI −0.55 to 2.11, P=.25, k=5). Conclusions: Substantial heterogeneity and limited study quality indicate the need for more robust, randomized controlled trials to establish the efficacy of PIMSs in diverse populations.

Background: Visuospatial neglect (VSN) affects spatial awareness, leading to functional and motor challenges. This case study explores virtual reality (VR) as a potential complementary tool for VSN rehabilitation. Objective: Specifically, we aim to explore the initial experiences of patients and physiotherapists engaging with a novel protocol, using an audiovisual cue task to support VSN rehabilitation. Methods: A preliminary VR task integrating audiovisual cues was co-designed with 2 physiotherapists. The task was then tested with 2 patients with VSN over 12 sessions. The intervention focused on engaging neglected spatial areas, with physiotherapists adapting the task to individual needs and monitoring responses. Results: Initial testing with 2 trainee physiotherapists indicated high usability, engagement, and perceived safety. Two patients with VSN completed 12 VR sessions. For Patient A, completion times increased following the introduction of an audio cue, though modeling indicated a nonsignificant linear trend (β=0.08; P=.33) and a marginally significant downward curvature (β=−0.001; P=.08). In contrast, Patient B showed a significant linear decrease in completion times (β=−0.53; P=.009), with a quadratic trend indicating a performance minimum around session 10 (β=0.007; P=.04). Intraweek variability also decreased. Motor scores (Box and Block Test and 9-Hole Peg Test) remained stable, and subjective feedback indicated improved mobility confidence and positive task engagement. Conclusions: Further research with larger cohorts is needed to confirm the VR task’s utility and refine the intervention.

Cognitive control, defined as the allocation of mental resources required for goal-directed behaviour, is crucial for exercise participation as it is involved in regulating negative cognitive and affective responses caused by the demands of exercise. Research on both music and acute exercise separately show engagement of cognitive control processes and affective responses, with low-to-moderate exercise intensities reliably influencing cognitive and affective outcomes (e.g., core affect). However, the combined effects of music and acute exercise on cognitive control and affective outcomes remain underexplored. Accordingly, this review and meta-analysis explores how music influences cognitive control and affective outcomes during acute exercise. 10 studies met the inclusion criteria, with nine providing data for effect size calculations across 21 intervention arms. Meta-analyses revealed significant effects of music on attention allocation (g = 1.05, 95% CI [0.03, 2.07]; p = 0.04), inhibitory control (g = 1.87, 95% CI [0.37, 3.37]; p = 0.01), and core affect (g = 0.86, 95% CI [0.24, 1.48]; p < 0.01). Exercise intensity significantly moderated outcomes (p = 0.036), suggesting that higher intensities diminish the effectiveness of music in elevating cognitive control and affective outcomes during acute exercise. Findings were limited by high heterogeneity (I² > 97%) across study protocols and outcome measures. Due to the aforementioned heterogeneity, the findings of this review must be interpreted cautiously.

Correspondences between the timing of motor behavior and that of musical performance are well-established. Motor behavior, however, is known to degrade across the adult lifespan due to neurobiological decay. In particular, performance on speed-dependent motor tasks deteriorates, spontaneous motor tempo (SMT) slows, and upper motor rate limit falls. Here, we examine whether this slowdown in motor behavior impacts tempo of musical performance as a function of age. We analyzed 14,556 songs released between 1956 and 2020 by artists with careers spanning at least 20 years. Generalized Additive Mixed Models (GAMMs) and Linear Mixed Models (LMMs) were employed to assess the effects of age, operationalized by subtracting birth year from release year of each track, on musical tempo. Results revealed a slight tempo increase from early adulthood to age 30, followed by a marked, linear slowdown with age across the remainder of the lifespan. From artists’ thirties to their eighties, tempo decreased by almost 10 bpm, averaging around 2 bpm per decade. This decrease aligns with the slowing-with-age hypothesis and mirrors rates of decline observed in studies of spontaneous motor tempo (SMT) and gait speed. Our findings highlight a significant gap in understanding of creative performance across the lifespan, particularly the role of age as a mediating factor in musical tempo. Moreover, that a discernible decrease in tempo is apparent even in commercial recordings further emphasizes the inescapable connection between dynamics of motor behavior and timing of musical performance.

Personalised Interactive Music Systems (PIMS) are emerging as promising devices for enhancing physical activity and exercise outcomes. By leveraging real-time data and adaptive technologies, PIMS align musical features, such as tempo and genre with users’ physical activity patterns, including frequency and intensity, enhancing their overall experience. This systematic review and exploratory meta-analysis evaluates the effectiveness of PIMS across physical, psychophysical, and affective domains. Searches across nine databases identified 18 eligible studies, of which six (comprising 17 intervention arms) contained sufficient data for meta-analysis. Random-effects meta-analyses and meta-regression were performed to assess outcomes for physical activity levels, physical exertion, ratings of perceived exertion (RPE), and affective valence. Results showed significant improvements in physical activity levels ( g = 0.49, CI [0.07, 0.91], p = .02, k = 4) and affective valence ( g = 1.68, CI [0.15, 3.20], p = .03, k = 4), with faster music tempo identified as a significant moderator ( p = 0.04). No significant effects were observed for RPE ( g = 0.72, CI [-0.14, 1.59], p = .10, k = 3) or physical exertion ( g = 0.79, CI [-0.64, 2.10], p = .28, k = 5). Substantial heterogeneity and limited study quality indicate the need for more robust, randomised controlled trials to establish the efficacy of PIMS in diverse populations.

Degradation of motor control across the adult lifespan due to neurobiological decay is well-established. Correspondences between the dynamics of motor behaviour and the timing of musical performance are also well-documented. In light of the former, the conspicuous absence of age as a mediating factor in investigation of the latter reveals a remarkable gap in our understanding of creative performance across the life course. To examine effects of ageing on musical timing, physical tempo of almost 2000 songs released by top-tier recording artists over their decades-long careers were annotated via a listening and tapping task. A series of regression analyses revealed i) an age-driven downward trend in performance tempo for all artists, ii) significant between-artist variation across time, and iii) within-artist variation that was independent of broader musical trends. Overall, tempo decreased by almost one and a half standard deviations from artists’ early twenties to their late fifties, a rate of decline comparable to that observed in studies of spontaneous motor tempo. Results are consistent with the slowing-with-age hypothesis, and reveal that, not only is such tempo decline discernible in commercial recordings, the impact of age on tempo is overwhelming for artists most physically connected with their music.

The author's goal in this paper is to explore how artificial intelligence (AI) has been utilised to inform our understanding of and ability to estimate at scale a critical aspect of musical creativity - musical tempo. The central importance of tempo to musical creativity can be seen in how it is used to express specific emotions (Eerola and Vuoskoski 2013), suggest particular musical styles (Li and Chan 2011), influence perception of expression (Webster and Weir 2005) and mediate the urge to move one's body in time to the music (Burger et al. 2014). Traditional tempo estimation methods typically detect signal periodicities that reflect the underlying rhythmic structure of the music, often using some form of autocorrelation of the amplitude envelope (Lartillot and Toiviainen 2007). Recently, AI-based methods utilising convolutional or recurrent neural networks (CNNs, RNNs) on spectral representations of the audio signal have enjoyed significant improvements in accuracy (Aarabi and Peeters 2022). Common AI-based techniques include those based on probability (e.g., Bayesian approaches, hidden Markov models (HMM)), classification and statistical learning (e.g., support vector machines (SVM)), and artificial neural networks (ANNs) (e.g., self-organising maps (SOMs), CNNs, RNNs, deep learning (DL)). The aim here is to provide an overview of some of the more common AI-based tempo estimation algorithms and to shine a light on notable benefits and potential drawbacks of each. Limitations of AI in this field in general are also considered, as is the capacity for such methods to account for idiosyncrasies inherent in tempo perception, i.e., how well AI-based approaches are able to think and act like humans.

Thirty rhythmic music excerpts were presented to 60 individuals. Dance movements to each excerpt were recorded using an optical motion-capture system, preference for each excerpt recorded on a 5-point Likert scale, and personality assessed using the 44-item version of the Big Five Inventory. From the movement data, a large number of postural, kinematic and kinetic features were extracted, a subset of which were chosen for further analysis using sequential backward elimination with variance inflation factor (VIF) selection. Multivariate analyses revealed significant effects on these 11 features of both preference and personality, as well as a number of interactions between the two. As regards preference, a U-shaped curvilinear relationship between excerpt preference and amount of movement was identified, hypothesized to relate to the role of emotional arousal in guiding music preference and dance moves. As regards personality, a different pattern of movement characteristics was associated with each of the Big Five dimensions, broadly supporting previous work.