Teach cases to solidify and test your own patterns
Explaining your recognition logic to others forces the implicit to become explicit and exposes gaps.
Why it works
Teaching a domain requires generating explicit, communicable criteria — the same process as cue articulation, but with an external audience providing real-time feedback on whether the explanation makes sense. The generation process also deepens encoding of the pattern, and the questions a learner asks reliably surface the edge cases and exceptions the teacher’s pattern handles poorly. This is the Feynman technique applied to judgment rather than factual knowledge.
How to do it
- Identify a domain decision you make fluently and volunteer to walk a less experienced person through a real case.
- Explain specifically what you noticed, what it told you, and what would have told you something different.
- Pay attention to the questions where you can’t give a crisp answer — those indicate shallow pattern coverage.
- Update your cue list based on what you could not explain clearly.
Evidence
Teaching-for-learning effects are well documented in educational psychology: generating explanations improves retention and comprehension relative to passive review. Application to tacit expert knowledge is a standard method in cognitive task analysis and professional mentorship, though direct RCT evidence for this specific use is limited. (observational)
Teaching-for-learning is best studied in academic knowledge domains; transfer to tacit perceptual expertise is plausible but less directly tested.
Sources
- Fiorella & Mayer (2013), "The relative benefits of learning by teaching and teaching expectancy," Contemporary Educational Psychology
- Fiorella, L., & Mayer, R. E. (2013). The relative benefits of learning by teaching and teaching expectancy. Contemporary Educational Psychology, 38(4), 281–288.
Common mistake
Teaching the outcome decision ("I went left") rather than the recognition cues that led to it (" I went left because the smoke was low-velocity and the color was off") — the cues are what transfers.
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More practices for Recognition-Primed Decision Making
- Build pattern libraries through deliberate case exposure
Systematically expose yourself to varied cases in your domain to grow the pattern library recognition draws from.
- Run a mental simulation before committing to a course of action
Before acting on a recognized situation, mentally run through how your intended response plays out.
- Articulate the cues that triggered your recognition
Name what you noticed that made the situation feel familiar — this tests the recognition and transfers it.
- Know when intuition is unreliable
Expert intuition is valid only when the domain has regular patterns and you’ve had feedback-rich experience in it.
- Conduct premortems on your past recognition failures
Review cases where pattern recognition led you wrong to find the shared structural feature that fools you.
- Override recognition and deliberate when the situation is genuinely novel
Flag situations that don’t quite fit a familiar pattern and switch from intuitive to analytical processing.