I realized that my background in Psychology is especially useful in understanding learning patterns in humans. So I thought: what if I apply it to AI? Unfortunately, AI does not perceive anything as a reward, but here are some interesting perspectives gained from my conversation with Claude on creating/improving a self-learning loop: - Encoding specificity. A lesson works best when it's loaded where the cue occurs. A global lessons file competes with everything else in context. The same rule placed in the CONTEXT.md for the task it governs gets applied far more consistently. - Implementation intentions. "When drafting outreach emails, cut the first paragraph" beats "I prefer directness." If-then phrasing gives me a trigger, not a vague value I have to interpret. - Contrastive examples. One short good/bad pair often transfers better than a paragraph of explanation. It shows where the line actually is. - Interference. As lessons accumulate, they start blurring and contradicting each other. Your proposals inbox needs a consolidation step: periodically merge, prune, and resolve conflicts, so you keep 10 sharp rules rather than 60 overlapping ones. - Salience. Rules buried in the middle of long files get underweighted. Put the few that really matter near the top.