Question
What is thinking about thinking?
Quick Answer
Thinking about thinking (metacognition) is the ability to observe, evaluate, and deliberately adjust your own cognitive processes — treating your mind as a system you can monitor and improve.
Thinking about thinking — formally called metacognition — is the skill of stepping back from your cognitive processes to observe them. Instead of just thinking, you notice how you're thinking: what assumptions you're making, what information you're prioritizing, what emotional state is influencing your reasoning.
John Flavell, who pioneered metacognition research in the 1970s, defined it as "cognition about cognition." But the practical definition is more useful: metacognition is the ability to catch yourself mid-thought and ask, "Is this the right way to think about this problem?"
Research consistently shows metacognition is trainable. Schraw and Dennison (1994) developed the Metacognitive Awareness Inventory and found that students who scored higher on metacognitive awareness performed better academically — not because they were smarter, but because they monitored their own understanding and adjusted their strategies when something wasn't working.
In personal epistemology, metacognition is the foundation skill. You can't improve a system you can't observe. Every technique in this curriculum — from cognitive defusion to externalization to structured decision-making — requires the prerequisite ability to watch your own thinking happening and notice when it's going off track.
Learn more in these lessons
- Thinking about thinking is a skill
Metacognition — the ability to monitor, evaluate, and regulate your own thinking — is not an innate gift. It is a trainable skill with measurable components, and the people who treat it as fixed are the ones most trapped by their own blind spots.
Go beyond the answer
This topic is part of Think With AI — a guided sequence of 16 lessons.
After completing this path you will use AI as a thinking partner without judgment atrophy: your mental models and assumptions externalized where AI can challenge them, recurring decisions routed through explicit frameworks, decisions captured with their reasoning so both you and the AI learn from outcomes, and a clear line around the judgments that stay yours.
Start the path →