Abstract:

Language provides humans a remarkably flexible and abstract way to transmit information. Here, we tested the hypothesis that language allows cultural evolution to improve our representation – or model – of the world. To do so, we investigated the role of language in cultural evolution from a reinforcement learning perspective that proposes that adaptative behavior is based on learning from rewards and punishments. Using a combination of experiments, computational models, and large language models, we aimed to uncover the processes through which language fuels cultural evolution. We implemented a sequential decision-making task, requiring a mental model of the task structure within an online transmission chain design (n = 1000, 10 generations). Trough an evolutionary selection algorithm, best performing participant were sampled and provided written advice about their model of the task for the next generation. Consistent with the idea that language is important for cultural evolution, we observe an increase in performance and model knowledge over generations. Analysis using large-language models reveal an intergeneration improvement in the model knowledge communicated via written advice. Overall, these results suggest that language allows CE to grow better representations of the world by successive combination and refinement of individual world models.

Speaker's social media:

Twitter: @BlubluDoScience