On the role of object knowledge in reference production: 
effects of color typicality on content determination

In two language production experiments, we investigated whether stored knowledge of the typical color of objects affects spoken reference. In experiment 1, human speakers referred to objects with colors ranging from very typical (e.g., red tomato) to very atypical (e.g., blue pepper). The probability that speakers redundantly include color in their descriptions was almost linearly predicted by the degree of atypicality. In experiment 2, we extended this finding to references to objects for which color is inherently a less salient property in stored knowledge (i.e., objects with a highly characteristic shape, making color less important for recognition). Following these findings that typicality affects reference production, we conclude that speakers utilize stored knowledge about everyday objects they refer to. We discuss the implications of our findings for artificial agents that generate natural language, arguing that computational models fall short in capturing general knowledge about typical properties of objects.