Rational decision making (human and robotic) depends critically on the availability of comprehensible internal models of the decision making world. Recognition of the spatio-temporal patterns of numerically or linguistically quantified attributes and relations by assessment of conformity/resemblance to wholistic perceptual elastic constraints of selected norms, is essential for the formulation of comprehensible internal models. The "formal description schema" (fdsk)- model has been proposed for perceptual recognition and inferential capture of real time experiential knowledge from underconstrained and often indeterminate sensory data . In this paper, the man-machine interactive paradigmatic approach to modeling human perception of conformity/resemblance is reviewed and algorithms for real-time recognition are presented. Fully parallel implementation using neural networks is also discussed.