Dual-EEG Reveals Adaptive Bilingual Language Control During Active and Observational Learning: Evidence from a Reinforcement Learning Model.

Language control is a cognitive ability that bilinguals use to suppress interference from the language they are not currently using to accurately select and use the intended language. Adaptive language control underpins language switching and enables bilinguals to flexibly switch between languages according to context. Reinforcement learning, which models how individuals update their strategies based on reward prediction errors, provides a computational framework for studying adaptive behavior in changing environments. To investigate how bilingual language control is shaped by reward signals in social interactions, we used dual-electroencephalography (EEG) to measure the performance of bilinguals who alternated between active and observational learner roles in voluntary language switching tasks. Computational modeling results indicated that the dual-sensitivity model best captured behavior which showed that bilinguals adaptively updated values by assigning distinct weights to feedback from themselves and others. EEG analyses revealed that bilinguals relied on expected values during active learning and on prediction errors during observational learning to modulate delta band activity. Taken together, these findings reveal how rewards dynamically modulate language control through expected values and prediction errors, providing new evidence for the adaptability of bilingual control during social interaction.