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

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arxiv 2403.13368 v1 pith:2YZUMK3X submitted 2024-03-20 cs.CL cs.AI

classification cs.CLcs.AI
keywords modelscomputationallanguagebraindatasetshumanprocessingalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Linguistics and Human Brain: A Perspective of Computational Neuroscience

    q-bio.NC 2026-02 unverdicted novelty 2.0 of 10

    A narrative review arguing that computational neuroscience, powered by LLM-based model–brain alignment, serves as the bridge between linguistic theory and neural data.

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