Neural Correlates of Information Processing in Human-AI Interactions: A Multi-Modal Analysis of Cogn
Abstract
This study investigates the neural mechanisms underlying human information processing during interactions with artificial intelligence systems, specifically examining how individuals evaluate and respond to varying degrees of information accuracy. Through a multi-modal approach combining electroencephalography (EEG), eye-tracking, and peripheral physiological measurements, we aim to identify distinct neural signatures associated with truth detection, cognitive load, and decision-making processes in human-AI interactions.
Research Objectives
Identify specific EEG markers associated with processing true versus false information during human-AI interactions
Characterize the relationship between cognitive load and information validation processes
Establish potential neural predictors of successful misinformation detection
Develop a comprehensive model of the neurophysiological correlates of human-AI trust dynamics
Methodology
Participants (n=TBD) will engage in structured interactions with AI chatbots programmed with distinct behavioral profiles. Using a randomized controlled design, subjects will be exposed to varying levels of information accuracy while undergoing continuous physiological monitoring. Key measurements include:
High-density EEG recording (focus on alpha, theta, and gamma band activity)
Eye-tracking metrics (fixation patterns, pupil dilation, saccadic movements)
Heart rate variability and galvanic skin response
Response timing and interaction patterns
Expected Outcomes
This research aims to advance our understanding of:
Neural mechanisms underlying information verification processes
Cognitive load patterns during human-AI interactions
Potential biomarkers for successful misinformation detection
Applications for computational neuroscience in human-AI trust dynamics
Broader Impact
Findings will contribute to both theoretical understanding of human information processing and practical applications in AI interface design, digital literacy education, and misinformation resistance training.
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