Identifying and interpreting non-aligned human conceptual representations using language modeling
Wanqian Bao, Uri Hasson

TL;DR
This paper introduces a supervised alignment method to compare how different groups, such as sighted and blind individuals, conceptualize words, revealing how blindness influences semantic representations across various verb categories.
Contribution
It presents a novel supervised approach for analyzing intergroup differences in conceptual representations using language models, specifically applied to blindness-related semantic shifts.
Findings
Blind individuals associate more social and cognitive meanings to motion verbs.
Blind individuals have sparser representations for amodal verbs.
Some verb representations are highly similar between blind and sighted groups.
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, we show that congenital blindness induces conceptual reorganization in both a-modal and sensory-related verbal domains, and we identify the associated semantic shifts. We first apply supervised feature-pruning to a language model (GloVe) to optimize prediction accuracy of human similarity judgments from word embeddings. Pruning identifies one subset of…
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Taxonomy
TopicsNatural Language Processing Techniques
MethodsPruning · GloVe Embeddings
