Proceedings of the Annual Meeting of the Cognitive Science Society

Language models have traditionally been trained on massive digitized text corpora. However, alternative data sources exist that may increase the representational alignment between language models and human knowledge. We contribute to the assessment of the role of data sources on language model representations. Specifically, we present work aimed at understanding differences in the content of language representations ('embeddings') trained from text, behavioral data (e.g., free associations), and brain data (e.g., fMRI). Using a method from neuroscience known as 'representational similarity analysis', we show that embeddings derived from behavioral and brain data encode different information than their text-derived cousins. Furthermore, using an interpretability method that we term, 'representational content analysis,' we find that, in particular, behavioral embeddings better encode dimensions relating to dominance, valence, and arousal, which are likely critical for the representational alignment of language models.