To study the requirements needed for a human-like language to develop, Language Emergence research uses jointly trained artificial agents which communicate to solve a task, the most popular of which is a referential game. The targets that agents refer to typically involve a single entity, which limits their ecological validity and the complexity of the emergent languages. Here, we present a simple multi-entity game in which targets include multiple entities that are spatially related. We ask whether agents dealing with multi-entity targets benefit from the use of graph representations, and explore four different graph schemes. Our game requires more sophisticated analyses to capture the extent to which the emergent languages are compositional, and crucially, what the decomposed features are. We find that emergent languages from our setup exhibit a considerable degree of compositionality, but not over all features.

Fluent speakers make implicit predictions about forthcoming linguistic items while processing sentences, possibly to increase efficiency in real-time comprehension. However, the extent to which prediction is the primary mode of processing human language is widely debated. The human language processor may also gain efficiency by integrating new linguistic information with prior knowledge and the preceding context, without actively predicting. At present, the role of probabilistic integration, as well as its computational foundation, remains relatively understudied. Here, we explored whether a Delayed Recurrent Neural Network (d-RNN, Turek et al., 2020), as an implementation of both prediction and integration, can explain patterns of human language processing over and above the contribution of a purely predictive RNN model. We found that incorporating integration contributes to explaining variability in eye-tracking data for English and Hindi.