Biology subjects

Gong, X. L.

Publications and source records attributed to Gong, X. L..

3 recordsLinked to original sources

bioRxiv2026

Representations of semantic relations in the human cerebral cortex

An essential aspect of human cognition is the ability to explicitly think about semantic relations between concepts. Neuroimaging studies have found that individual concepts are encoded by distributed patterns of cortical activity, but relatively little is known about how semantic relations between concepts are encoded in the brain. Some theoretical models suggest that relation representations are embedded within concept representations, while others suggest that relation representations are independent of any specific concept pair. We designed a study to compare how semantic relations and concepts are encoded across the cerebral cortex. To characterize how relations are encoded across cortex, fMRI was used to record brain activity while six participants each answered over one thousand questions about different semantic relations. We find that relations are encoded independently of the specific concepts that are connected in any particular instance of the relation. Our results further suggest that relations and concepts are represented in the same set of cortical regions, and that, within these regions, each location is preferentially selective for specific relations. Overall, these results suggest that in the human cerebral cortex, relations and concepts may have the same type of functional representation.

neuroscience↗

bioRxiv2025

Concreteness shapes semantic representations in bilingual brains

Behavioral studies show that humans process concrete words more similarly across languages than abstract words. This suggests that in bilingual brains, semantic representations may be more similar across languages for concrete concepts than for abstract concepts, but existing neuroimaging evidence is inconclusive. Here, we analyzed functional magnetic resonance imaging (fMRI) data from fluent Chinese-English bilinguals who read several hours of naturalistic narratives in both languages. We used encoding models to estimate voxelwise tuning towards concrete and abstract concepts in each language separately. We then quantified the similarity of cortical semantic representations across languages. First, we find that the cortical organization of concreteness tuning is consistent across languages. Second, semantic representations are similar across languages for both concrete-tuned voxels and abstract-tuned voxels. Third, we find that for abstract concepts, cross-language similarity of semantic representations may be driven by emotionality. Overall, these findings reveal how concreteness affects semantic representations in bilingual brains.

neuroscience↗

bioRxiv2025

Optimizing Language Model Embeddings to Voxel Activity Improves Brain Activity Predictions

Recent studies have shown that contextual semantic embeddings from language models can accurately predict human brain activity during language processing. However, most studies use contextual embeddings with the same context length and model layer for all voxels, potentially overlooking meaningful variations across the brain. In this study, we investigate whether optimizing contextual embeddings for individual voxels improves their ability to predict brain activity during reading. We optimize embeddings for each voxel by selecting the best-predicting context length, model layer, or both. We perform this optimization with two different types of stimuli (isolated sentences and narratives), and quantify the performance gains of optimized embeddings over standard fixed embeddings. Our results show that voxel-specific optimization substantially improves the prediction accuracy of contextual semantic embeddings. These findings demonstrate that voxel-specific contextual tuning provides a more accurate and nuanced account of how the contextual semantic information is represented across the cortex.

neuroscience↗