COGNITIVE FEEDBACK LOOPS IN AI SYSTEMS

Abstract

In the field of psychology, cognitive structures can be defined as an information processing system that encompasses the acquisition of sensory information, its storage, retrieval, and use in making complex decisions in an individual's life. In psychological understanding, cognition or thinking involves mental activities such as comprehension, problem-solving, forward thinking, and decision-making. Cognition represents an individual's belief about something derived from the thinking process about a particular matter. Through cognitive processes, humans can acquire and manipulate knowledge by engaging in activities such as recalling, analysing, understanding, evaluating, and imagining a particular subject. The capacity and ability of cognition are generally defined as human intelligence. However, the presence of artificial intelligence (AI) systems in the field of psychology brings about a new transformation and new challenge. With continuous and unlimited two-way feedback interaction, humans can correct model errors, reduce bias, and inject domain expertise, while AI systems can support human thinking with real-time information and pattern recognition. Cognitive loops are heavily influenced by cognitive psychology principles, especially those related to perception, attention, memory, and learning. By modelling AI systems to reflect human cognitive processes, collaboration between humans and machines becomes more intuitive and effective. This paper explores the theoretical foundations, design strategies, and practical implications of cognitive feedback loops in AI systems. By fostering a symbiotic relationship between human cognition and machine intelligence, such systems hold the potential to create AI that is not only more accurate but also more aligned with human reasoning and values.