p412fig12.09 How a Vector Associative Map, or VAM, model uses mismatch learning during its development to calibrate inputs from a target position vector (T) and a present position vector (P) via mismatch learning of adaptive weights at the difference vector (D).  See the text for details.<BR>||  Vector Associative Map model (VAP).  During critical period, Endogenous Random Generator (ERG+) tirns on, activates P, and causes random movements that sample workspace.  When ERG+ shuts off, posture occurs.  ERG- then turns on (rebound) and opens Now Print (NP) gate, that dumps P into T.  Mismatch learning enables adaptive weights between T and D to change until D (the mismatch) appoaches 0.  Then T and P are both correctly calibrated to represent the same positions.