mlsanityData to 7 Oct 2026

Computational Models to Study Language Processing in the Human Brain: A Survey

arXiv 2403.13368 ↗First posted 20 Mar 2024cs.CL cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 113 references that are in the map. First posted on arXiv 20 Mar 2024.

Builds on

32 of 113 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Navigating Brain Language Representations: A Comparative Analysis of Neural Language Models and Psychologically Plausible Modelscited by 4
  2. 2026Linguistics and Human Brain: A Perspective of Computational Neurosciencecited by 3
  3. 2024MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activitiescited by 2
  4. 2025Discovering Semantic Subdimensions through Disentangled Conceptual Representationscited by 1