How does our perception of the world influence the way we talk about it? Psycholinguistic studies have investigated whether visual salience correlates with entity mention and ordering, but often disregarded its effect on grammar or relied on simplistic images or artificial cues. In this study, we explore the use of generative AI to better control for salience in visual stimuli while keeping them realistic, and to serve as a proxy for human participants in studying how different types of salience impact image descriptions.We consider three salience types: *perceptual* (e.g. relative size in the image), *inherent* (e.g. animacy), and *relational* (e.g. human–object interaction). We first analyze human- and AI-generated captions for natural images to examine how salience correlates with how early, and in what grammatical role, an entity is mentioned. We find strong correlations between models and humans in this observational study, justifying the use of AI models alone in a further causal study. For this second study, we created datasets composed of pairs of images, where we used an image-editing model to intervene on the salience of a target entity. We show that relational and perceptual salience lead to the entity being mentioned earlier in captions and being mapped to more prominent grammatical roles. The magnitude of this effect varies across entity types, with animate entities (high inherent salience) showing a particularly distinct pattern.

Animacy is a semantic feature of nominals and follows a hierarchy: personal pronouns > human > animate > inanimate. In several languages, animacy imposes hard constraints on grammar. While it has been argued that these constraints may emerge from universal soft tendencies, it has been difficult to provide empirical evidence for this conjecture due to the lack of data annotated with animacy classes. In this work, we first propose a method to reliably classify animacy classes of nominals in 11 languages from 5 families, leveraging multilingual large language models (LLMs) and word sense disambiguation datasets. Then, through this newly acquired data, we verify that animacy displays consistent cross-linguistic tendencies in terms of preferred morphosyntactic constructions, although not always in line with received wisdom: animacy in nouns correlates with the alignment role of agent, early positions in a clause, and syntactic pivot (e.g., for relativisation), but not necessarily with grammatical subjecthood. Furthermore, the behaviour of personal pronouns in the hierarchy is idiosyncratic as they are rarely plural and relativised, contrary to high-animacy nouns.