Oscillatory and aperiodic neural activity jointly predict language learning

Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli; considerably less is known about the oscillatory correlates of complex rule learning, as in language. Further, recent work has shown that non-oscillatory (1/ f ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 females, 18 males), we show for the first time that 1/ f and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Flexible word order rules were associated with a steeper 1/ f slope, while fixed word order rules were associated with a shallower slope. We also show that increased theta and alpha power predicts fixed relative to flexible word order rule learning and behavioural performance. Together, these results suggest that 1/ f activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word order permutations, which manifest in distinct oscillatory profiles.