Proceedings of the Annual Meeting of the Cognitive Science Society

This study explores human capabilities in distinguishing and recognizing entities that change over time from those that do not. We specifically investigate the linguistic distinction between "individual-level predicates" (ILPs) and "stage-level predicates" (SLPs). Our empirical approach focuses on how humans visually distinguish these two types. We performed a corpus analysis, in which a set of image captions were randomly extracted and annotated by experts with either SLP or ILP labels. The findings indicated a predominance of SLPs over ILPs in the image captions. We then performed automatic annotation on a large dataset of image captions and conducted a machine-learning experiment on image classification based on ILSs and SLPs. Our results demonstrated that SLPs were identified with high accuracy, while ILPs were identified with about chance level, substantially lower than human capabilities. Given the analyses, we discuss what features of the image contribute to distinguishing between ILPs and SLPs.