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

1. Identify a domain decision you make fluently and volunteer to walk a less experienced person through a real case.
2. Explain specifically what you noticed, what it told you, and what would have told you something different.
3. Pay attention to the questions where you can’t give a crisp answer — those indicate shallow pattern coverage.
4. 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

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.