Metacognition and Metacat in AI: System Reflexivity

The article analyzes the transition from first-order intelligence to metacognition using the example of the Metacat system from Douglas Hofstadter's concepts. The author explains that the key to advanced AI is not merely increasing the knowledge base, but the system's ability to treat its own cognitive process as an object of analysis. By introducing a temporal trace and episodic memory, the system can monitor its errors, recognize futile strategies, and calibrate the confidence of its answers. The text emphasizes the difference between declarative and procedural knowledge and indicates that functional metacognition serves to optimize problem-solving strategies, which is crucial for contemporary challenges regarding the reliability of large language models.