  Language models and humans are two types of learning systems. Finding or
facilitating commonalities could enable major breakthroughs in our
understanding of the acquisition and evolution of language. Many theories of
language evolution rely heavily on learning biases and learning pressures. Yet
due to substantial differences in learning pressures, it is questionable
whether the similarity between humans and machines is sufficient for insights
to carry over and to be worth testing with human participants. Here, we review
the emergent communication literature, a subfield of multi-agent reinforcement
learning, from a language evolution perspective. We find that the emergent
communication literature excels at designing and adapting models to recover
initially absent linguistic phenomena of natural languages. Based on a short
literature review, we identify key pressures that have recovered initially
absent human patterns in emergent communication models: communicative success,
efficiency, learnability, and other psycho-/sociolinguistic factors. We argue
that this may serve as inspiration for how to design language models for
language acquisition and language evolution research.
