Woman or tennis player? Visual typicality and lexical frequency affect variation in object naming.

Speakers often use different names to refer to the same entity (e.g., “woman” vs. “tennis player”). We here explore factors that affect naming variation for visually presented objects. We analyze a large dataset of object names with realistic images and focus on two factors: visual typicality (of both objects and the contexts they appear in) and name frequency. We develop a novel computational approach to estimate visual typicality, using image representations from Computer Vision models. Specifically, we compute visual typicality as similarity between the representation of an object/context to the average representation of other objects/contexts of its nominal class. In contrast to previous studies, we not only study the name used by most annotators for a given object (top name), but also the second most frequently used (alternative name). Our results show that the top name and the alternative name pull in opposite directions. People’s naming choices are more varied for objects that are less typical for their top name, or more typical for their alternative name. They are also more varied when the top name has relatively low frequency (for alternative names, the opposite effect may be present but the data are not conclusive). Context typicality instead does not show a general effect in our analysis. Overall, our results show that visual and lexical characteristics relating to name candidates beyond the top name are informative for predicting variability in object naming. On a methodological level, we demonstrate the potential of using large scale datasets with realistic images in conjunction with computational methods to inform models of human object naming.