Questionnaires based on natural language processing elicit immersive ruminative thinking in ruminators: Evidence from...

Area of Science:

• Neuroscience
• Psychology
• Computational Linguistics

Background:

• Rumination is linked to mental disorders, making its detection crucial.
• Psychological selection faces challenges with masking and fabrication, impacting disorder detection.
• Natural Language Processing (NLP) can enhance psychological assessments by eliciting specific cognitive states.

Purpose of the Study:

• To develop and validate an NLP-based questionnaire for inducing ruminative thinking.
• To investigate the behavioral and neural correlates of induced rumination.
• To assess the potential of this tool for early mental disorder detection in psychological selection.

Main Methods:

• Utilized NLP to design a questionnaire aimed at eliciting ruminative thinking.
• Recorded Electroencephalography (EEG) signals to measure brain activity in response to the questionnaire.
• Analyzed behavioral data (arousal rates, reaction times) and EEG coherence patterns.

Main Results:

• Ruminators showed increased arousal and prolonged reaction times to ruminative questionnaire items.
• No significant EEG differences were observed in the resting state between groups.
• Significant alterations in whole-brain EEG coherence were found in ruminators when engaging with ruminative items.

Conclusions:

• The NLP-developed questionnaire successfully induced immersive ruminative thinking in ruminators.
• EEG coherence changes indicate a specific neural response to induced rumination.
• This NLP-based approach shows promise for the early detection of mental disorders through psychological selection.