  Learning to read words aloud is a major step towards becoming a reader. Many
children struggle with the task because of the inconsistencies of English
spelling-sound correspondences. Curricula vary enormously in how these patterns
are taught. Children are nonetheless expected to master the system in limited
time (by grade 4). We used a cognitively interesting neural network
architecture to examine whether the sequence of learning trials could be
structured to facilitate learning. This is a hard combinatorial optimization
problem even for a modest number of learning trials (e.g., 10K). We show how
this sequence optimization problem can be posed as optimizing over a time
varying distribution i.e., defining probability distributions over words at
different steps in training. We then use stochastic gradient descent to find an
optimal time-varying distribution and a corresponding optimal training
sequence. We observed significant improvement on generalization accuracy
compared to baseline conditions (random sequences; sequences biased by word
frequency). These findings suggest an approach to improving learning outcomes
in domains where performance depends on ability to generalize beyond limited
training experience.
