An EEG correlation framework to study state anxiety and learning under uncertainty.

Area of Science:

• Computational neuroscience
• Neuroscience
• Cognitive science

Background:

• Anxiety is linked to altered decision-making under uncertainty.
• Impaired encoding of precision-weighted prediction errors (pwPEs) contributes to these disruptions.
• Hierarchical Bayesian models describe how pwPEs guide belief updating.

Purpose of the Study:

• Introduce a gamified paradigm for decision-making data collection.
• Develop a framework for extracting EEG features linked to computational variables.
• Target brain networks involved in decision-making for potential neurofeedback.

Main Methods:

• Forty healthy participants completed a volatile decision-making task in an immersive game.
• EEG data were analyzed for theta and alpha band power correlations with pwPEs and state anxiety.
• Both intra-subject (trial-wise pwPEs) and inter-subject (state anxiety) analyses were performed.

Main Results:

• pwPEs positively correlated with theta power and negatively with alpha power, supporting belief updating mechanisms.
• Higher state anxiety associated with reduced theta and increased alpha power.
• Findings align with Bayesian inference models and anxiety's impact on uncertainty processing.

Conclusions:

• The EEG framework successfully identifies neural markers for belief updating and anxiety-related learning impairments.
• This approach provides a foundation for personalized neurofeedback targeting maladaptive decision-making in anxiety.
• Immersive task paradigms enhance engagement and translational potential for real-world applications.