  Learning image transformations is essential to the idea of mental simulation
as a method of cognitive inference. We take a connectionist modeling approach,
using planar neural networks to learn fundamental imagery transformations, like
translation, rotation, and scaling, from perceptual experiences in the form of
image sequences. We investigate how variations in network topology, training
data, and image shape, among other factors, affect the efficiency and
effectiveness of learning visual imagery transformations, including
effectiveness of transfer to operating on new types of data.
