TUNING THE BRAIN: SEPARATING SIMULTANEOUS RESPONSES TO REVEAL HOW MUSICALITY ENHANCES SELECTIVE ATTENTION

Everyday listening environments, from crowded conversations to orchestras, require the brain to extract target sounds from complex mixtures. A central challenge is that neural responses evoked by simultaneous sounds are difficult to isolate and analyze separately. Combining frequency tagging and machine learning, we achieved high-precision separation of neural responses to mixed melodies, classifying them by selective attention towards specific melodies with different pitch and timing. Behaviorally, task performance showed a strong positive correlation with individual musicality, with the relationship strengthening as task complexity increased. Magnetoencephalography results consistently demonstrated that individual musicality and task performance heavily influenced the attentional recruitment of cortical regions, correlating positively with top-down attention in the left parietal cortex but negatively with bottom-up attention in the right. Temporal dynamics of prefrontal activity further revealed that participants with higher musicality and performance maintained selective attention for longer periods over the tone. Together, these findings demonstrate that musical training enhances frontoparietal neural mechanisms, improving performance by strengthening top-down attention, suppressing bottom-up distractions, and sustaining selective attention over time. Beyond music and attention, this work establishes the combination of frequency-tagging with machine learning as a sensitive approach for detecting subtle cognitive and behavioral effects with high stimulus precision in complex environments. Key publication: How musicality enhances top-down and bottom-up selective attention: Insights from precise separation of simultaneous neural responses.Sci. Adv.11,eadz0510(2025).DOI:10.1126/sciadv.adz0510