It is now common to employ evidence from human behaviour (e.g., child development) for the creation of computational models of this behaviour with a variety of applications (e.g., in developmental robotics). In this paper we address research in the comprehension and processing of figurative (non-literal) language in highly verbal individuals with autism in comparison with age- and language level-matched neuro-typical individuals and discuss critically what factors might account for the observed problems. Based on this evidence we try to outline the strategies used by human language users in understanding non-literal/non-compositional expressions and proceed to identifying possible solutions for automated language systems in the domain of idiomatic expressions.