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

arXiv: https://arxiv.org/abs/2403.13368 · HTML: https://mlsanity.com/paper/2403.13368/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 113 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 30 of 32)

- [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)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Transformers learn in-context by gradient descent](https://mlsanity.com/paper/2212.07677/) (2022)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Data Distributional Properties Drive Emergent In-Context Learning in Transformers](https://mlsanity.com/paper/2205.05055/) (2022)
- [Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)](https://mlsanity.com/paper/1905.11833/) (2019)
- [Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency](https://mlsanity.com/paper/2006.16736/) (2020)
- [Explaining black box text modules in natural language with language models](https://mlsanity.com/paper/2305.09863/) (2023)
- [Human Sentence Processing: Recurrence or Attention?](https://mlsanity.com/paper/2005.09471/) (2020)
- [Cognitive computational neuroscience](https://mlsanity.com/paper/1807.11819/) (2018)
- [On the proper role of linguistically-oriented deep net analysis in linguistic theorizing](https://mlsanity.com/paper/2106.08694/) (2021)
- [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)
- [Neither hype nor gloom do DNNs justice](https://mlsanity.com/paper/2312.05355/) (2023)
- [Joint processing of linguistic properties in brains and language models](https://mlsanity.com/paper/2212.08094/) (2022)
- [Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses](https://mlsanity.com/paper/2106.05426/) (2021)
- [Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?](https://mlsanity.com/paper/2205.01404/) (2022)
- [A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends](https://mlsanity.com/paper/1905.13294/) (2019)
- [Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps](https://mlsanity.com/paper/2207.03380/) (2022)
- [Training language models to summarize narratives improves brain alignment](https://mlsanity.com/paper/2212.10898/) (2022)
- [Misspelling Oblivious Word Embeddings](https://mlsanity.com/paper/1905.09755/) (2019)
- [Modeling structure-building in the brain with CCG parsing and large language models](https://mlsanity.com/paper/2210.16147/) (2022)
- [MulCogBench: A Multi-modal Cognitive Benchmark Dataset for Evaluating Chinese and English Computational Language Models](https://mlsanity.com/paper/2403.01116/) (2024)
- [Fine-tuned vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations?](https://mlsanity.com/paper/2310.01854/) (2023)
- [Human Attention during Goal-directed Reading Comprehension Relies on Task Optimization](https://mlsanity.com/paper/2107.05799/) (2021)

## Built on by (most cited first, 4 of 4)

- [Navigating Brain Language Representations: A Comparative Analysis of Neural Language Models and Psychologically Plausible Models](https://mlsanity.com/paper/2404.19364/) (2024)
- [Linguistics and Human Brain: A Perspective of Computational Neuroscience](https://mlsanity.com/paper/2602.08275/) (2026)
- [MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities](https://mlsanity.com/paper/2403.17516/) (2024)
- [Discovering Semantic Subdimensions through Disentangled Conceptual Representations](https://mlsanity.com/paper/2508.21436/) (2025)

## 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.