  The Weber-Fechner Law observes that human perception scales as the logarithm
of the stimulus. We argue that learning algorithms for human concepts could
benefit from the Weber-Fechner Law. Specifically, we impose Weber-Fechner on
simple neural networks, with or without convolution, via the logarithmic power
series of their sorted output. Our experiments show surprising performance and
accuracy on the MNIST data set within a few training iterations and limited
computational resources, suggesting that Weber-Fechner can accelerate machine
learning of human concepts.
