CONCEPT
Perceptual Learning
The progressive differentiation of the perceptual system through active engagement with the environment — not the accumulation of stored representations, but the tuning of attention to detect invariants that were always present but previously unnoticed.
Perceptual learning, in the framework developed by Eleanor and J.J. Gibson across decades of empirical research, is not the acquisition of new information to be stored and retrieved. It is the progressive
differentiation of the perceptual system — the organism learns to make finer distinctions in what was previously an undifferentiated field. The wine taster who distinguishes Burgundy from Bordeaux has not memorized a rule; she has differentiated her perceptual system through hundreds of hours of active engagement with the stimulus. The experienced
builder who feels that a codebase is unstable before she can articulate why has undergone the same process. Her perceptual system has been tuned through thousands of hours of engagement with the
affordance structure of codebases, and the differentiation allows her to detect invariants that a novice cannot see. The mechanism matters urgently for the AI transition: if
differentiation requires active engagement with the environment's structure, and if AI-augmented environments reduce the occasions for such engagement, then the pipeline