mlsanityData to 7 Oct 2026

Connecting Neural Response measurements & Computational Models of language: a non-comprehensive guide

arXiv 2203.05300 ↗First posted 10 Mar 2022cs.CLcited by 6

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Abstract (arXiv)

Understanding the neural basis of language comprehension in the brain has been a long-standing goal of various scientific research programs. Recent advances in language modelling and in neuroimaging methodology promise potential improvements in both the investigation of language's neurobiology and in the building of better and more human-like language models. This survey traces a line from early research linking Event Related Potentials and complexity measures derived from simple language models to contemporary studies employing Artificial Neural Network models trained on large corpora in combination with neural response recordings from multiple modalities using naturalistic stimuli.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 22 of its 144 references that are in the map. First posted on arXiv 10 Mar 2022.

Builds on

22 of 144 references.

References · 22

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10 more
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Cited by year

Citing papers per year: 2023: 4; 2024: 2.

6 papers.

  1. 2023Coupling Artificial Neurons in BERT and Biological Neurons in the Human Braincited by 9
  2. 2023Divergences between Language Models and Human Brainscited by 3
  3. 2024Brain-like Functional Organization within Large Language Modelscited by 3
  4. 2023Language Cognition and Language Computation -- Human and Machine Language Understandingcited by 1
  5. 2023Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processingcited by 0
  6. 2024LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Modelscited by 0