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

arXiv: https://arxiv.org/abs/2203.05300 · HTML: https://mlsanity.com/paper/2203.05300/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 144 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)](https://mlsanity.com/paper/1905.11833/) (2019)
- [Recurrent Neural Network Grammars](https://mlsanity.com/paper/1602.07776/) (2016)
- [ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation](https://mlsanity.com/paper/1912.00903/) (2019)
- [Finding Syntax in Human Encephalography with Beam Search](https://mlsanity.com/paper/1806.04127/) (2018)
- [Disentangling Syntax and Semantics in the Brain with Deep Networks](https://mlsanity.com/paper/2103.01620/) (2021)
- [Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains](https://mlsanity.com/paper/1906.01539/) (2019)
- [Inducing brain-relevant bias in natural language processing models](https://mlsanity.com/paper/1911.03268/) (2019)
- [Linking artificial and human neural representations of language](https://mlsanity.com/paper/1910.01244/) (2019)
- [Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses](https://mlsanity.com/paper/2106.05426/) (2021)
- [Advancing NLP with Cognitive Language Processing Signals](https://mlsanity.com/paper/1904.02682/) (2019)
- [Does injecting linguistic structure into language models lead to better alignment with brain recordings?](https://mlsanity.com/paper/2101.12608/) (2021)
- [Does the brain represent words? An evaluation of brain decoding studies of language understanding](https://mlsanity.com/paper/1806.00591/) (2018)
- [Robust Evaluation of Language-Brain Encoding Experiments](https://mlsanity.com/paper/1904.02547/) (2019)
- [Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity](https://mlsanity.com/paper/1711.09285/) (2017)

## Built on by (most cited first, 6 of 6)

- [Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain](https://mlsanity.com/paper/2303.14871/) (2023)
- [Divergences between Language Models and Human Brains](https://mlsanity.com/paper/2311.09308/) (2023)
- [Brain-like Functional Organization within Large Language Models](https://mlsanity.com/paper/2410.19542/) (2024)
- [Language Cognition and Language Computation -- Human and Machine Language Understanding](https://mlsanity.com/paper/2301.04788/) (2023)
- [Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing](https://mlsanity.com/paper/2311.10431/) (2023)
- [LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models](https://mlsanity.com/paper/2410.20053/) (2024)

## 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.