Identifying and interpreting non-aligned human conceptual representations using language modeling

arXiv:2403.06204v1 Announce Type: new
Abstract: The question of whether people's experience in the world shapes conceptual representation and lexical semantics is longstanding. Word-association, feature-listing and similarity rating tasks aim to address this question but require a subjective interpretation of the latent dimensions identified. In this study, we introduce a supervised representational-alignment method that (i) determines whether two groups of individuals share the same basis of a certain category, and (ii) explains in what respects they differ. In applying this method, …

abstract aim arxiv association cs.cl dimensions experience feature human interpretation language modeling people question representation semantics study tasks type word world