Sentence-level embeddings reveal dissociable word- and sentence-level cortical representation across coarse- and fine-grained levels of meaning

In this large-sample (N = 64) fMRI study, sentence embeddings (text-embedding-ada-002, OpenAI) and representational similarity analysis were used to contrast sentence-level and word-level semantic representation. Overall, sentence-level information resulted in a 20–25 % increase in the model's ability to captures neural representation when compared to word-level only information (word-order scrambled embeddings). This increase was relatively undifferentiated across the cortex. However, when coarse-grained (across thematic category) and fine-grained (within thematic category) combinatorial meaning were separately assessed, word- and sentence-level representations were seen to strongly dissociate across the cortex and to do so differently as a function of grain. Coarse-grained sentence-level representations were evident in occipitotemporal, ventral temporal and medial prefrontal cortex, while fine-grained differences were seen in lateral prefrontal and parietal cortex, middle temporal gyrus, the precuneus, and medial prefrontal cortex. This result indicates dissociable cortical substrates underly single concept versus combinatorial meaning and that different cortical regions specialise for fine- and coarse-grained meaning.