| Brain embeddings with shared geometry to artificial contextual embeddings, as a code for representing language in the human brain A Goldstein, A Dabush, B Aubrey, M Schain, SA Nastase, Z Zada, E Ham, ... BioRxiv, 2022.03. 01.482586, 2022 | 7 | 2022 |
| Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns A Goldstein, A Grinstein-Dabush, M Schain, H Wang, Z Hong, B Aubrey, ... Nature communications 15 (1), 2768, 2024 | 5 | 2024 |
| Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations A Goldstein, H Wang, L Niekerken, Z Zada, B Aubrey, T Sheffer, ... bioRxiv, 2023.06. 26.546557, 2023 | 5 | 2023 |
| Information-making processes in the speaker's brain drive human conversations forward A Goldstein, H Wang, T Sheffer, M Schain, Z Zada, L Niekerken, B Aubrey, ... bioRxiv, 2024.08. 27.609946, 2024 | | 2024 |
| Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language Z Hong, H Wang, Z Zada, H Gazula, D Turner, B Aubrey, L Niekerken, ... bioRxiv, 2024.06. 12.598513, 2024 | | 2024 |
| ALIGNING BRAINS INTO A SHARED SPACE IMPROVES THEIR ALIGNMENT TO LARGE LANGUAGE MODELS A Bhattacharjee, Z Zada, H Wang, B Aubrey, W Doyle, P Dugan, ... bioRxiv, 2024.06. 04.597448, 2024 | | 2024 |
| Larger Language Models Better Predict Neural Activity During Natural Language Processing Z Hong, H Wang, Z Zada, H Gazula, B Aubrey, W Doyle, S Devore, ... | | |
