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Updated: May 19, 2026
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Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
Published on: June 29, 2021
EEG Based Decoding of the Perception and Regulation of Taboo Words
Parisa Ahmadi Ghomroudi1, Michele Scaltritti1, Bianca Monachesi2
1DiPSCo - Department of Psychology and Cognitive Sciences, University of Trento, Rovereto, Italy.
Psychophysiology
|May 18, 2026
Summary
Neural processing of taboo words can be predicted using EEG signals, even when individuals attempt emotion regulation. Affective word information persists at the neural level despite regulation efforts.
Area of Science:
- Neuroscience
- Psycholinguistics
- Cognitive Science
Background:
- Emotional language processing is crucial for social interaction.
- Taboo words elicit strong affective responses, necessitating emotion regulation.
- Neural underpinnings of taboo word processing and emotion regulation are not fully understood.
Purpose of the Study:
- To investigate if electroencephalography (EEG) signals can predict neutral, negative, and taboo words.
- To determine if this prediction is maintained during emotion regulation.
- To explore the neural persistence of affective word information.
Main Methods:
- Forty participants viewed neutral, negative, and taboo words.
- EEG data were recorded during passive observation (Look condition) and emotion regulation (Accept condition).
- Support vector machine (SVM) classifiers analyzed event-related potentials (ERPs), specifically the late positive potential (LPP).
Main Results:
- Word categories were reliably decoded from the LPP (450–850 ms) in specific brain regions.
- Classification accuracy decreased but remained above chance during the Accept condition.
- Affective word information was decodable even when emotion regulation was applied.
Conclusions:
- EEG signals, particularly LPP, can decode affective word categories.
- Neural representation of affective word information persists despite emotion regulation attempts.
- Machine learning techniques are valuable for decoding neural representations in emotional language processing.
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