Analysis of cognitive mechanisms in phoneme perception and pronunciation errors among Korean language learners

Cognitive load tracking in Korean phoneme recognition presents significant changes due to the intricate spatiotemporal dynamics of EEG signals and the inherent variability in cognitive states. Traditional methods often struggle with these complexities, leading to suboptimal performance in accurately modeling cognitive load. This paper introduces an innovative framework, the Adaptive EEG Attention Trac, designed to overcome these limitations by leveraging attention-augmented EEG signals.

The proposed methodology comprises three integral components: the Manifold Constrained Signal Encoding, the Agent-driven Temporal Attention Routing, and the Uncertainty-aware Cognitive Load Prediction. The encoder is responsible for transforming raw EEG signals into a compact latent representation while adhering to manifold constraints, thereby ensuring structural fidelity. The attention router dynamically allocates focus across temporal segments, enhancing both interpretability and relevance of the signals. The predictor incorporates uncertainty quantification, which is crucial for providing robust estimations of cognitive load. Furthermore, the Uncertainty Propagation Adjustment strategy is introduced to explicitly model and propagate uncertainty throughout the computational pipeline, thereby refining predictions and enhancing reliability.

Experimental results substantiate the efficacy of the proposed framework, demonstrating its capability to accurately track cognitive load during Korean phoneme recognition tasks. This advancement significantly contributes to the field of EEG-based cognitive modeling, offering a more reliable and interpretable approach to understanding cognitive processes.