Scene descriptions in our study did not tap into precise compositional processes, such as symmetric relationships (Hafri et al.,), thematic role assignment in agentive verbs (Koring, Mak & Reuland,), or ergative verbs, which emphasize the object role in an action (see Khatin‐Zadeh, Hu, Eskandari, Banaruee, Yanjiao, Farsani & He for an example of gestures). Furthermore, since production and comprehension are not distinct and likely share predictive forward models (e.g., Kempen, Olsthoorn & Sprenger; Pickering & Gambi), a visual world version of our study may be able to better elucidate the moment‐by‐moment online integration of overt attention and sentence understanding (Snedeker & Trueswell,), and thus examine whether the evidence of semantic dominance observed in our data still holds. Future research could, for example, develop experimental designs that elicit descriptions involving actions, agents, and patients across languages, yielding a more comprehensive understanding of the LoV underpinning event representation and its reflection in overt attention. Additionally, by systematically manipulating syntactic constructions in their complexity, it is plausible to map their cartographic distances as a function of gaze alignment. While we expect semantic similarity to remain dominant, this controlled setting could isolate structural distinctions linked to predictable gaze shifts, effectively testing the comprehension–production interface (Pickering & Gambi,). [...] Computationally, the current study did not directly investigate real‐time alignment between visual and linguistic processing; instead, we focused on the association between the two information streams, using sentence similarity as a predictor of scan pattern similarity. However, our reliance on global similarity metrics may overlook fine‐grained individual differences in attentional strategies. Future research could benefit from network‐based scan‐path analyses, where eye movements are modeled as transition networks to reveal structural metrics (e.g., centrality, density) capable of distinguishing processing styles (Ma, Liu, Clariana, Gu & Li,). Additionally, recent advances in deep semantic gaze embeddings (Castner, Kuebler, Scheiter, Richter, Eder, Hüttig, Keutel & Kasneci,) and personalized scan‐path prediction models using observer encoders (Chen, Jiang & Zhao,; Xue, Xu, Mondal, Le, Zelinsky, Hoai & Samaras,) may offer higher granularity in distinguishing between participants who rely on top‐down linguistic guidance versus those driven by bottom‐up visual saliency. In parallel, the past decade has seen rapid advances in representing visual information using formalisms previously applied to linguistic information (e.g., dependency grammars, Elliott & Keller), leading to new developments in multimodal modeling (Koh, Fried & Salakhutdinov,), with applications to tasks such as image captioning (Elliott & de Vries,), text‐to‐image generation (Rombach, Blattmann, Lorenz, Esser & Ommer,), or sign language recognition (Li, Duan, Fang, Gong & Jiang,) (see Wu, Gan, Chen, Wan & Philip for a review of state of the art).