Biology subjects

Farbood, M. M.

Publications and source records attributed to Farbood, M. M..

2 recordsLinked to original sources

bioRxiv2026

Neural Decoding of Musical Engagement Reveals Tension-Release Dynamics

Music listening is one of the most compelling and rewarding activities humans engage in spontaneously. But what exactly catches people"s attention when listening to music remains unclear. Musicologists have argued that tension-release dynamics in music constitute crucial features of the listening experience. They arise from the intertwining of different low-level and high-level features and sit at the core of music enjoyment by engaging listeners dynamically. This study aims to characterize the relationship between tension dynamics and engagement during naturalistic music listening. Using canonical correlation analysis, we decoded the music envelope from EEG and ECoG responses and found that musical tension patterns, as reported by listeners, were predictive of fluctuations in the coupling between the music and the neural response. Importantly, tension dynamics were significantly correlated with neural measures of envelope tracking even after controlling for loudness and musical expectations, confirming the specific and crucial role of musical tension in engaging listeners. These results shed new light on how musical structure gives rise to internal response modulations that, in turn, dynamically reflect musical engagement. This interplay may underlie the pervasive and emotionally rewarding nature of music. Significance StatementThis work addresses a fundamental question in cognition: how engagement dynamically modulates complex auditory input processing. We investigate this issue within the well-controlled yet ecologically valid context of music perception. At the core of the musical experience lies the perception of tension - the shifting sense of expectancy, instability, and resolution that guides listening across time. We show that fluctuations in musical tension, shaped by musical structure, reliably predict modulations of auditory engagement, thereby illuminating an understudied phenomenon: what makes music engaging.

neuroscience↗

bioRxiv2022

Neural basis of melodic learning explains cross-cultural regularities in musical scales

SummarySeeking exposure to unfamiliar experiences constitutes an essential aspect of the human condition, and the brain must adapt to the constantly changing environment by learning the evolving statistical patterns emerging from it. Cultures are shaped by norms and conventions and therefore novel exposure to an unfamiliar culture induces a type of learning that is often described as implicit: when exposed to a set of stimuli constrained by unspoken rules, cognitive systems must rapidly build a mental representation of the underlying grammar. Music offers a unique opportunity to investigate this implicit statistical learning, as sequences of tones forming melodies exhibit structural properties learned by listeners during short- and long-term exposure. Understanding which specific structural properties of music enhance learning in naturalistic learning conditions reveals hard-wired properties of cognitive systems while elucidating the prevalence of these features across cultural variations. Here we provide behavioral and neural evidence that the prevalence of non-uniform musical scales may be explained by their facilitating effects on melodic learning. In this study, melodies were generated using an artificial grammar with either a uniform (rare) or non-uniform (prevalent) scale. After a short exposure phase, listeners had to detect ungrammatical new melodies while their EEG responses were recorded. Listeners performance on the task suggested that the extent of statistical learning during music listening depended on the musical scale context: non-uniform scales yielded better syntactic learning. This behavioral effect was mirrored by enhanced encoding of musical syntax in the context of non-uniform scales, which further suggests that their prevalence stems from fundamental properties of learning.

neuroscience↗