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Sketchpad Series

Investigating electrophysiological brain network dynamics and mechanisms underlying executive deployment during novel learning

Poster Session F - Tuesday, April 1, 2025, 8:00 – 10:00 am EDT, Back Bay Ballroom/Republic Ballroom

Julia K Dabrowska¹ (julia.dabrowska@univ.ox.ac.uk), Alexander Fraser¹, Bethan Grimes¹, Mats WJ van Es¹, Mark W Woolrich¹, Gaia Scerif¹; ¹University of Oxford

Existing research has found that the efficiency of novel learning is heavily dependent on cognitive control and executive function (EF). Consequently, increased ability to cope with cognitive demands is a consistent predictor of learning ability across various tasks and populations. However, the roles of particular brain networks in this interplay remain to be understood. As cognitive control and learning performance have been linked to differential network composition and dynamic reconfigurations, our preliminary hypotheses focus on establishing networks present during a novel learning task over time, differences between attentional demands on a group level, and linking this to individual performance. Here, we use an artificial symbol learning paradigm with concurrent 32-channel EEG recording in 25 adult participants: first, teaching them an ordinal sequence of symbols, followed by a magnitude comparison task to test successful learning. Dynamic network modelling, previously applied to MEG data, is then used to identify networks of oscillatory activity involved with the tasks. Further planned work will investigate similar hypotheses applied to EF task data and the interplay between network characteristics and performance between learning and EF. Besides uncovering cognitive mechanisms, such findings can facilitate effective learning in educational contexts, as recognising general patterns may be beneficial for individuals affected by learning or neurodevelopmental disorders impacting their ability to learn new information. Additionally, this project is crucial for validating the efficacy of novel machine learning methods for the analysis of lower-density EEG data, subsequently opening possibilities for cost-effective and simple experimental procedures to investigate brain network dynamics.

Topic Area: METHODS: Electrophysiology