Human–Robot Interactive Performance Analysis with Enhanced Metacognition

This study investigates integrating metacognition into human–robot interaction (HRI) systems to improve reliability, efficiency, and trust. As HRI deployments expand, performance regulation under uncertainty becomes critical. Prior work has examined metacognition largely through behavioral observation, with limited system-level models that quantify operational impact. We address this gap by proposing a representative Markov chain model to evaluate metacognitive effects under deterministic and stochastic conditions. Using statistical analysis and probabilistic modeling, we measure impacts on machine accuracy and efficiency over extended operation. Experiments show metacognitive-enabled systems achieve a 59.29% reduction in errors and inefficiencies, with an 18.67% increase in execution time, indicating a favorable performance tradeoff. We further conceptualize metacognition as a quantifiable decision-regulation layer rather than an interface-level construct. The framework offers a scalable analytical basis for designing more dependable and trustworthy HRI systems in complex, information-intensive environments.