Mood modulations of emotional word processing: A predictive perspective view of EEG data.

Mood is a pervasive affective state that influences cognition, including language processing, where it functions as part of the pragmatic context. When linguistic expressions carry emotional valence, mood may amplify or attenuate their perceived emotional nature. Motivated by inconsistent findings on the timing and nature of mood and valence interactions, we conducted an EEG study to investigate whether induced moods alter the temporal profile of emotional word processing. Participants rated words of high, low, and neutral valence in a control condition and after positive and negative mood induction. Event-related potentials were analysed across three early processing windows using cluster-based permutation tests. Results showed that mood modulated early neural activity: in the positive mood, high-valence words reduced N1 amplitudes compared with control, reflecting diminished prediction error. In the subsequent P2 and EPN windows, reduced amplitudes for high- and neutral-valence words indicated a decreased need for model updating under mood-congruent expectations. By contrast, the negative mood exerted weaker and later effects. Behavioural responses corroborated these findings, with faster response times to valenced words. Within predictive coding frameworks, mood can be understood as a hyperprior that shapes precision weighting and error dynamics. Our results suggest that induced positive mood facilitates the processing of congruent words and broadens the range of input treated as predictable, whereas induced negative mood primarily reduces positive affect. These findings highlight the role of mood in dynamically tuning early language processing through the regulation of prediction error and model update.