Decoding Motor Action Timing and Metacognitive Signals from Single-Trial EEG Using a Transformer-Based Model (EEG-ViT)

Abstract Metacognition refers to the capacity to monitor one’s own actions, internal states, and cognitive processes. A central question in cognitive neuroscience is whether metacognitive evaluation operates as a direct readout of performance signals or requires computationally independent neural mechanisms. Single-process theories propose that both arise from shared decision variables, while the Higher-Order Representation theory holds that metacognition requires re-representation through distinct computational processes. To test these frameworks, participants produced timed motor intervals and evaluated their own performance without external feedback, termed temporal error monitoring (TEM). Vision Transformer decoding applied to PCA-optimized single-trial EEG captured θ, α, and β dynamics during both task phases. First-order timing was decodable from any individual frequency band, whereas second-order metacognitive inference required simultaneous integration across all three bands before action termination. Individuals whose metacognitive states were more accurately decoded showed stronger TEM precision, with no equivalent relationship observed for first-order performance decoding. These findings establish metacognitive evaluation as a computationally distinct process requiring higher-order multi-band neural integration rather than a direct readout of first-order timing signals.