Computational Models to Study Language Processing in the Human Brain: A Survey
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911804741910528 |
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| author | Wang, Shaonan Sun, Jingyuan Zhang, Yunhao Lin, Nan Moens, Marie-Francine Zong, Chengqing |
| author_facet | Wang, Shaonan Sun, Jingyuan Zhang, Yunhao Lin, Nan Moens, Marie-Francine Zong, Chengqing |
| contents | Despite differing from the human language processing mechanism in implementation and algorithms, current language models demonstrate remarkable human-like or surpassing language capabilities. Should computational language models be employed in studying the brain, and if so, when and how? To delve into this topic, this paper reviews efforts in using computational models for brain research, highlighting emerging trends. To ensure a fair comparison, the paper evaluates various computational models using consistent metrics on the same dataset. Our analysis reveals that no single model outperforms others on all datasets, underscoring the need for rich testing datasets and rigid experimental control to draw robust conclusions in studies involving computational models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_13368 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computational Models to Study Language Processing in the Human Brain: A Survey Wang, Shaonan Sun, Jingyuan Zhang, Yunhao Lin, Nan Moens, Marie-Francine Zong, Chengqing Computation and Language Artificial Intelligence Despite differing from the human language processing mechanism in implementation and algorithms, current language models demonstrate remarkable human-like or surpassing language capabilities. Should computational language models be employed in studying the brain, and if so, when and how? To delve into this topic, this paper reviews efforts in using computational models for brain research, highlighting emerging trends. To ensure a fair comparison, the paper evaluates various computational models using consistent metrics on the same dataset. Our analysis reveals that no single model outperforms others on all datasets, underscoring the need for rich testing datasets and rigid experimental control to draw robust conclusions in studies involving computational models. |
| title | Computational Models to Study Language Processing in the Human Brain: A Survey |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.13368 |