p357fig10.04 Laminar Computing achieves its properties by computing in a new way that sythesizes the best properties of feedforward and feedback interactions, analog and digital computations, and preattentive and attentive learning.  The property of analog coherence enables coherent groupings and decisions to form without losing sensitivity to the amount of evidence that supports them.<BR>||  Laminar Computing: a new way to compute.  1. Feedforward and feedback: a) Fast feedforward processing when data are unambiguous (eg Thorpe etal), b) slower feedback chooses among ambiguous alternatives [self-normalizing property, real-time probabiligy theory], c) A self-organizing system that trades certainty against speed: Goes beyond Bayesian models!  2. Analog and Digital: Analog Coherence combines the stability of digital with the sensitivity of analog. 3. Preattentive and Attentive Learning: Reconciles the differences of (eg) Helmholtz and Kanizsa, "A preattentive grouping is its own 'attentional' prime"