  Both empirical and mathematical demonstrations of the importance of
chance-corrected measures are discussed, and a new model of learning is
proposed based on empirical psychological results on association learning. Two
forms of this model are developed, the Informatron as a chance-corrected
Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational
results presented show chance correction facilitates learning.
