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Towards neuroadaptive chatbots: a feasibility study.

Diana E Gherman1,2, Thorsten O Zander1,2

  • 1Chair of Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany.

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|October 31, 2025
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Summary
This summary is machine-generated.

This study shows passive brain-computer interfaces (pBCIs) can decode mental states from brain data during text interaction. This research paves the way for neuroadaptive chatbots by using implicit human feedback for large-language model alignment.

Keywords:
AI alignmentLLMerror-processingmoral judgmentpBCIpassive brain-computer interfaces

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Large-language models (LLMs) require better alignment with human values.
  • Implicit human feedback via passive brain-computer interfaces (pBCIs) offers a novel approach.
  • Understanding users' cognitive and affective states is crucial for advanced chatbot development.

Purpose of the Study:

  • Investigate the feasibility of using pBCIs to decode mental states from brain activity in response to text.
  • Lay the groundwork for developing neuroadaptive chatbots that respond to implicit user feedback.
  • Explore the potential of neural-based implicit feedback for LLM alignment.

Main Methods:

  • Utilized electroencephalography (EEG) with 64 electrodes to record brain data during reading tasks.
  • Developed paradigms to elicit moral judgment and error processing using text stimuli.
  • Employed offline analysis with a windowed-means approach and linear discriminant analysis (LDA) for mental state classification.

Main Results:

  • Successfully decoded moral salience at a single-trial level with 78% accuracy.
  • Decoded error processing related to factual inaccuracy with 66% accuracy.
  • Event-related potentials (ERPs) partially aligned with existing findings in cognitive neuroscience.

Conclusions:

  • Demonstrated the feasibility of using pBCIs to differentiate mental states from brain data at a single-trial level.
  • Highlighted the need for further research transitioning to online BCI investigations in realistic settings.
  • Marked preliminary steps toward utilizing neural-based implicit feedback for LLM alignment.