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.

通过使用神经网络进行监督学习和自监督学习，本文提出了一种新的模型，旨在解决语言转换中存在的计算负担问题，并引入了人类发展过程中观察到的监督和非监督学习的混合。

一个混合监督和无监督学习的语言演变迭代学习模型