We present a computational model of interactions between a Speaker and a Hearer in a signalling game. The partly cooperative/partly competitive interaction is intended to reproduce essential aspects of human communication, with the goal of approximating human language use and analysing it by means of simulation studies. This was accomplished by implementing a language that accommodates for compositional signals, allowing agents to express infinitely many meanings with a finite set of signals. Personal attributes integral to human decision making such as sympathy and trust were implemented as adjustable parameters, providing the opportunity to create and study individuals with different 'personalities'. Over the course of several rounds, agents learn their optimal strategies using the Moran algorithm. The model was able to substantiate widely confirmed notions about human communication such as correlations between truthfulness and trust, demonstrating the possibility of correlation between aspects of linguistic cognition and social aspects of language use.