Co-evolution of language and mindreading: A computational exploration
Marieke Woensdregt (CLE, University of Edinburgh)
Monday April 1
10:00-10:30am
Room S1, 7 George Square
Language relies on mindreading (a.k.a. theory of mind), as language users have to entertain and recognise communicative intentions. Mindreading skills in turn profit from language, as language provides a means for expressing mental states explicitly, and for talking about mental states. Given this interdependence, it has been hypothesised that language and mindreading have co-evolved. I will present an agent-based model to formalise this hypothesis, which combines referential signalling with perspective-taking.
This model treats communicative behaviour as an outcome of an interplay between the context in which communication occurs, the agent’s individual perspective on the world, and the agent’s lexicon. However, each agent’s perspective and lexicon are private mental representations, not directly observable by other agents. Language learners are therefore confronted with the task of jointly inferring both the lexicon and the perspective of their cultural parent. Simulation results show that Bayesian learners can solve this task by bootstrapping one from the other, but only if the speaker uses a lexicon that is at least somewhat informative.
This leads to the question under what circumstances a population of agents can evolve such an informative lexicon from scratch. In this talk I will explore the effects of two different selection pressures: a pressure for successful communication and a pressure for accurate perspective-inference. I will also compare two different types of agents: literal communicators and pragmatic communicators. Pragmatic speakers optimise their communication behaviour by maximising the probability that their interlocutor will interpret their signals correctly. Iterated learning results show that populations of literal agents evolve an informative lexicon not just when they’re under a pressure to communicate, but also when they’re under a pressure to infer each other’s perspectives. Populations of pragmatic agents show similar evolutionary dynamics, except that they can achieve improvements in communication and perspective-inference while maintaining more ambiguous lexicons.