  The iterated learning model is an agent-based model of language change in
which language is transmitted from a tutor to a pupil which itself becomes a
tutor to a new pupil, and so on. Languages that are stable, expressive, and
compositional arise spontaneously as a consequence of a language transmission
bottleneck. Previous models have implemented an agent's mapping from signals to
meanings using an artificial neural network decoder, but have relied on an
unrealistic and computationally expensive process of obversion to implement the
associated encoder, mapping from meanings to signals. Here, a new model is
presented in which both decoder and encoder are neural networks, trained
separately through supervised learning, and trained together through
unsupervised learning in the form of an autoencoder. This avoids the
substantial computational burden entailed in obversion and introduces a mixture
of supervised and unsupervised learning as observed during human development.
