Mood dynamically modulates early neural activity during emotional word processing : EEG evidence
Kopaeva, Ekaterina LU
and Roll, Mikael
LU
(2025)
MEG Nord 2025
- Abstract
- Mood, an individual’s emotional state, fundamentally shapes how the brain interprets sensory input by providing a continuous affective context for prediction and evaluation. In language processing, mood may bias the interpretation of emotionally valenced words, amplifying or dampening their perceived affect. Yet, the temporal dynamics of these mood-valence interactions remain poorly understood. To clarify inconsistent evidence on the timing and nature of mood-valence interactions, we examined how induced mood influences early stages of emotional word processing using EEG. Participants performed a valence-rating task for positive, negative, and neutral words in a baseline condition and following positive or negative mood induction.... (More)
- Mood, an individual’s emotional state, fundamentally shapes how the brain interprets sensory input by providing a continuous affective context for prediction and evaluation. In language processing, mood may bias the interpretation of emotionally valenced words, amplifying or dampening their perceived affect. Yet, the temporal dynamics of these mood-valence interactions remain poorly understood. To clarify inconsistent evidence on the timing and nature of mood-valence interactions, we examined how induced mood influences early stages of emotional word processing using EEG. Participants performed a valence-rating task for positive, negative, and neutral words in a baseline condition and following positive or negative mood induction. Event-related potentials were analysed across early processing windows (N1, P2, EPN) using cluster-based permutation statistics. Positive mood selectively attenuated N1 amplitudes for highly valenced words, consistent with reduced prediction error under mood-congruent expectations. Later components (P2, EPN) showed decreased amplitudes for both high and neutral valence, suggesting reduced model updating under mood-congruent expectations. Negative mood, in contrast, produced weaker and temporally delayed modulations. Behaviourally, participants responded more quickly to valenced words under induced mood conditions, supporting the neural findings. Interpreted within a predictive coding framework, these results support the theoretical view that mood functions as a hyperprior, tuning the precision of predictive models during language comprehension. Positive mood appears to enhance predictive flexibility and facilitate the processing of affectively congruent words, whereas induced negative mood reduces positive affect. Taken together, the findings highlight how affective states dynamically modulate early predictive mechanisms in emotional language processing. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/cf016102-8ebe-4bf6-adc3-424fc7a78b12
- author
- Kopaeva, Ekaterina
LU
and Roll, Mikael
LU
- organization
- publishing date
- 2025-11-20
- type
- Contribution to conference
- publication status
- unpublished
- subject
- keywords
- cognition, emotional words, mood, language processing, ERP, prediction, emotion theory
- conference name
- MEG Nord 2025
- conference location
- Aarhus, Denmark
- conference dates
- 2025-11-26 - 2025-11-28
- language
- English
- LU publication?
- yes
- id
- cf016102-8ebe-4bf6-adc3-424fc7a78b12
- date added to LUP
- 2026-03-26 11:47:04
- date last changed
- 2026-04-20 15:37:45
@misc{cf016102-8ebe-4bf6-adc3-424fc7a78b12,
abstract = {{Mood, an individual’s emotional state, fundamentally shapes how the brain interprets sensory input by providing a continuous affective context for prediction and evaluation. In language processing, mood may bias the interpretation of emotionally valenced words, amplifying or dampening their perceived affect. Yet, the temporal dynamics of these mood-valence interactions remain poorly understood. To clarify inconsistent evidence on the timing and nature of mood-valence interactions, we examined how induced mood influences early stages of emotional word processing using EEG. Participants performed a valence-rating task for positive, negative, and neutral words in a baseline condition and following positive or negative mood induction. Event-related potentials were analysed across early processing windows (N1, P2, EPN) using cluster-based permutation statistics. Positive mood selectively attenuated N1 amplitudes for highly valenced words, consistent with reduced prediction error under mood-congruent expectations. Later components (P2, EPN) showed decreased amplitudes for both high and neutral valence, suggesting reduced model updating under mood-congruent expectations. Negative mood, in contrast, produced weaker and temporally delayed modulations. Behaviourally, participants responded more quickly to valenced words under induced mood conditions, supporting the neural findings. Interpreted within a predictive coding framework, these results support the theoretical view that mood functions as a hyperprior, tuning the precision of predictive models during language comprehension. Positive mood appears to enhance predictive flexibility and facilitate the processing of affectively congruent words, whereas induced negative mood reduces positive affect. Taken together, the findings highlight how affective states dynamically modulate early predictive mechanisms in emotional language processing.}},
author = {{Kopaeva, Ekaterina and Roll, Mikael}},
keywords = {{cognition; emotional words; mood; language processing; ERP; prediction; emotion theory}},
language = {{eng}},
month = {{11}},
title = {{Mood dynamically modulates early neural activity during emotional word processing : EEG evidence}},
year = {{2025}},
}