The Neuro-Cognitive Mechanics of Error Monitoring in Human vs. AI Feedback

Error monitoring is a fundamental mechanism that enables adaptive behavior, learning, and decision-making in both biological and artificial systems. In humans, error monitoring is supported by complex neuro-cognitive processes involving distributed brain networks, electrophysiological markers, and feedback-driven learning. In artificial intelligence (AI), error monitoring is operationalized through algorithmic optimization, loss functions, and reinforcement signals. This paper provides a comparative analysis of error monitoring mechanisms in humans and AI systems, integrating insights from cognitive neuroscience, psychology, and machine learning. We examine neural correlates such as the anterior cingulate cortex (ACC), error-related negativity (ERN), and feedback-related negativity (FRN), alongside AI mechanisms including gradient-based learning, reinforcement learning reward signals, and uncertainty estimation. Similarities and differences are analyzed in terms of representation, adaptability, temporal dynamics, and contextual sensitivity. The implications for human–AI interaction, trust calibration, and hybrid cognitive systems are discussed, highlighting future research directions toward neuro-inspired AI and cognitively aligned feedback systems.

کلیدواژه ها:

Error monitoring ، feedback processing ، cognitive neuroscience ، artificial intelligence ، reinforcement learning