  A recurrent neural network model of phonological pattern learning is
proposed. The model is a relatively simple neural network with one recurrent
layer, and displays biases in learning that mimic observed biases in human
learning. Single-feature patterns are learned faster than two-feature patterns,
and vowel or consonant-only patterns are learned faster than patterns involving
vowels and consonants, mimicking the results of laboratory learning
experiments. In non-recurrent models, capturing these biases requires the use
of alpha features or some other representation of repeated features, but with a
recurrent neural network, these elaborations are not necessary.
