Harding, E. E. | bioRxiv Science

bioRxiv2023

Unravelling individual rhythmic abilities using machine learning

Humans can easily extract the rhythm of a complex sound, like music, and move to its regular beat, for example in dance. These abilities are modulated by musical training and vary significantly in untrained individuals. The causes of this variability are multidimensional and typically hard to grasp with single tasks. To date we lack a comprehensive model capturing the rhythmic fingerprints of both musicians and non-musicians. Here we harnessed machine learning to extract a parsimonious model of rhythmic abilities, based on the behavioral testing (with perceptual and motor tasks) of individuals with and without formal musical training (n = 79). We demonstrate that the variability of rhythmic abilities, and their link with formal and informal music experience, can be successfully captured by profiles including a minimal set of behavioral measures. These profiles can shed light on individual variability in healthy and clinical populations, and provide guidelines for personalizing rhythm-based interventions.

bioinformatics↗

bioRxiv2023

The syntax-meter interface in spoken language and music: Same, different, or individually variable?

Considerable debate surrounds syntactic processing similarities in language and music. Yet few studies have investigated how syntax interacts with meter considering that metrical regularity varies across domains. Furthermore, there are reports on individual differences in syntactic and metrical structure processing in music and language. Thus, a direct comparison of individual variation in syntax and meter processing across domains is warranted. In a behavioral (Experiment 1) and EEG study (Experiment 2), participants engaged in syntactic processing tasks with sentence- and melody stimuli that were more or less metrically regular, and followed a preferred or non-preferred (but correct) syntactic structure. We further employed a range of cognitive diagnostic tests, parametrically indexed verbal- and musical abilities using a principal component analysis, and correlated cognitive factors with the behavioral and ERP results (Experiment 3). Based on previous results in the language domain, we expected that a regular meter would facilitate the syntactic integration of non-preferred syntax. While syntactic discrimination was better in regular than irregular meter conditions in both domains (Experiment 1), a P600 effect indicated different integration costs during the processing of syntactic complexities in the two domains (Experiment 2). Metrical regularity altered the P600 response to preferred syntax in language while it modulated non-preferred syntax processing in music. Moreover, experimental results yielded within-domain individual differences, and identified continuous metrics of musical ability more beneficial than grouping musicians or non-musicians (Experiment 3). These combined results suggest that the meter-syntax interface differs uniquely in how it forms syntactic preferences in language and music.

neuroscience↗