On 16 December Dr. Cassia Low Manting (Karolinska institutet) will giva a talk on How musicality enhances top-down and bottom-up selective attention: Insights from MEG frequency-tagging
Time: 10.15 - 12.00
Location: SOL H402
Zoom link: lu-se.zoom.us/j/62491331134
Abstract
Listening seems effortless, yet beneath it lies extraordinary complexity. Everyday listening environments, from crowded conversations to orchestras, require the brain to extract target sounds from complex mixtures. Few activities challenge it more beautifully than music: What happens in your brain when you focus on a single melody in a noisy concert hall? Why can musicians follow multiple instruments effortlessly, while others struggle to pick one voice out of a crowd? These questions open a window into how the brain controls attention and how musical experience refines it.
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. Across magnetoencephalography (MEG) experiments of varying complexity, behavioral task performance showed strong positive correlations with individual musicality, with the relationship strengthening as task complexity increased. Musicality and task performance also 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 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. We will also discuss the potential of using Magnetic Resonance Spectroscopy (MRS) to probe the neurochemical mechanisms underlying these electrophysiological effects, by measuring concentrations of excitatory and inhibitory neurotransmitters (i.e. GABA/Glx). Finally, we will consider broader questions: can musical experience inspire new strategies for education, rehabilitation, or even brain stimulation to enhance focus and learning?