The Visual Representation of Abstract Verbs: Merging Verb Classification with Iconicity in Sign Language

摘要

Theories like the picture superiority effect state that the visual modality has substantial advantage over the other human senses. This makes visual information vital in the acquisition of knowledge, such as in the learning of a language. Words can be graphically represented to illustrate the meaning of a message and facilitate its understanding. This method, however, becomes a limitation in the case of abstract words, like accept, belong, integrate and agree, which have no visual referent. The current research turns to sign languages to explore the common semantic elements that link words to each other. Such visual languages have been found to reveal enlightening patterns across signs of similar meanings, pointing towards the possibility of creating clusters of iconic meanings along with their respective graphic representation. By using sign language insight and VerbNet's organisation of verb predicates, this study presents a novel organisation of 506 English abstract verbs classified by visual shape. Graphic animation was used to visually represent the 20 classes of abstract verbs developed. To build confidence on the resulting product, which can be accessed on www.vroav.online, an online survey was created to achieve judgements on the visuals' representativeness. Considerable agreement between participants was found, suggesting a positive way forward for this work, which may be developed as a language learning aid in educational contexts or as a multimodal language comprehension tool for digital text.