The iterated learning model is an agent model which simulates the transmission of of language from generation to generation. It is used to study how the language adapts to pressures imposed by transmission. In each iteration, a language tutor exposes a na\"ive pupil to a limited training set of utterances, each pairing a random meaning with the signal that conveys it. Then the pupil becomes a tutor for a new na\"ive pupil in the next iteration. The transmission bottleneck ensures that tutors must generalize beyond the training set that they experienced. Repeated cycles of learning and generalization can result in a language that is expressive, compositional and stable. Previously, the agents in the iterated learning model mapped signals to meanings using an artificial neural network but relied on an unrealistic and computationally expensive process of obversion to map meanings to signals. Here, both maps are neural networks, trained separately through supervised learning and together through unsupervised learning in the form of an autoencoder. This avoids the computational burden entailed in obversion and introduces a mixture of supervised and unsupervised learning as observed during language learning in children. The new model demonstrates a linear relationship between the dimensionality of meaning-signal space and effective bottleneck size and suggests that internal reflection on potential utterances is important in language learning and evolution.

翻译：迭代学习模型是一种模拟语言代际传递的智能体模型，用于研究语言如何适应传递过程中的压力。在每次迭代中，语言导师向新手学习者展示有限的训练话语集，每个话语将随机意义与其传递的信号配对。随后，该学习者成为下一轮迭代中新手的导师。传递瓶颈确保导师必须对其所经历的训练集进行泛化。学习与泛化的重复循环可产生具有表达性、组合性和稳定性的语言。先前迭代学习模型中的智能体使用人工神经网络将信号映射到意义，但依赖一种不切实际且计算成本高昂的反向转换过程来实现意义到信号的映射。本文中，两个映射均采用神经网络，分别通过监督学习进行训练，并通过自编码器形式的无监督学习进行联合训练。这避免了反向转换带来的计算负担，并引入了儿童语言学习中观察到的监督与无监督学习的混合机制。新模型展示了意义-信号空间维度与有效瓶颈大小之间的线性关系，并表明对潜在话语的内部反思在语言学习与演化中具有重要作用。