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<p>Political behavior research increasingly employs electroencephalography (EEG) to study implicit cognitive-affective processes in response to political stimuli. However, the divergence between neural responses and self-reported political attitudes poses methodological challenges. This article presents a framework to interpret neural–verbal discrepancies, emphasizing three principles: the non-hierarchical nature of neural and verbal evidence, the necessity to consider temporal differences, and the recognition that such discrepancies are significant methodological signals. A mixed-methods approach demonstrates the framework’s utility in exploring emotion regulation, rationalization, and political engagement dynamics. The framework is operationalized through explicit decision rules for neural classification using standardized qEEG Z-score thresholds and regional activation criteria, as well as systematic thematic coding protocols for verbal data, with mismatches treated as theoretically meaningful analytical findings rather than measurement errors. This contribution aims to provide a clear inference logic for integrating neural data with verbal reports in political behavior studies, enhancing EEG methodologies in political science and psychology.</p>

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Neural–verbal mismatch as inferential signal: a methodological framework for EEG research in political behavior

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Ali Sahab,
  • Muhammad Asfar,
  • Abdulloh Machin,
  • I. Gede Wahyu Wicaksana,
  • Mochamad Kevin Romadhona

摘要

Political behavior research increasingly employs electroencephalography (EEG) to study implicit cognitive-affective processes in response to political stimuli. However, the divergence between neural responses and self-reported political attitudes poses methodological challenges. This article presents a framework to interpret neural–verbal discrepancies, emphasizing three principles: the non-hierarchical nature of neural and verbal evidence, the necessity to consider temporal differences, and the recognition that such discrepancies are significant methodological signals. A mixed-methods approach demonstrates the framework’s utility in exploring emotion regulation, rationalization, and political engagement dynamics. The framework is operationalized through explicit decision rules for neural classification using standardized qEEG Z-score thresholds and regional activation criteria, as well as systematic thematic coding protocols for verbal data, with mismatches treated as theoretically meaningful analytical findings rather than measurement errors. This contribution aims to provide a clear inference logic for integrating neural data with verbal reports in political behavior studies, enhancing EEG methodologies in political science and psychology.