Language Evolution and Computation Bibliography

We study sequential language games in which two players, each with private information, communicate to achieve a common goal. In such games, a successful player must (i) infer the partner’s private information from the partner’s messages, (ii) generate messages that are most likely to help with the goal, and (iii) reason pragmatically about the partner’s strategy. We propose a model that captures all three characteristics and demonstrate their importance in capturing human behavior on a new goal-oriented dataset we collected using crowdsourcing.

What cognitive mechanisms support the emergence of linguistic conventions from repeated interaction? We present results from a large-scale, multi-player replication of the classic tangrams task, focusing on three foundational properties of conventions: arbitrariness, stability, and reduction of utterance length over time. These results motivate a theory of convention-formation where agents, though initially uncertain about word meanings in context, assume others are using language with such knowledge. Thus, agents may learn about meanings by reasoning about a knowledgeable, informative partner; if all agents engage in such a process, they successfully coordinate their beliefs, giving rise to a conventional communication system. We formalize this theory in a computational model of language understanding as social inference and demonstrate that it produces all three properties in a simplified domain.

One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examined judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative and that listeners use Bayesian inference to recover speakers intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that the use of information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.

In coming to understand the world—in learning concepts, acquiring language, and grasping causal relations—our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?