Schreiber, Paul Vinzenz

Congruence between brand names and other flagships of a brand are important tools to effectively communicate to, and set the right expectations in consumers. In this study, we explore congruence of brand features as a problem that could potentially be evaluated by computational models trained on cross-modal stimuli such as CLIP. As a by-product we explore to what extent these models capture sound symbolic associations in our stimuli set, and analyse the relationship between (sub-) lexical information and shape dimensions in brand logos. Instead of human participants, we employ CLIP- and BERT-based computational models to make decisions about similarity between a multitude of brand names, brand descriptions and brand logos. We show that our results support proposals of statistical co-occurrence as an underlying mechanism of sound symbolic associations, and argue that CLIP can be carefully used as a tool for maneuvering through naming and logo design decisions.