# Finding Syntax in Human Encephalography with Beam Search

arXiv: https://arxiv.org/abs/1806.04127 · HTML: https://mlsanity.com/paper/1806.04127/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 30 of 41)

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- [Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times](https://mlsanity.com/paper/2402.02255/) (2024)
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- [Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars](https://mlsanity.com/paper/2109.04939/) (2021)
- [Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models](https://mlsanity.com/paper/2109.11058/) (2021)
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- [Computational Models to Study Language Processing in the Human Brain: A Survey](https://mlsanity.com/paper/2403.13368/) (2024)
- [If Attention Serves as a Cognitive Model of Human Memory Retrieval, What is the Plausible Memory Representation?](https://mlsanity.com/paper/2502.11469/) (2025)
- [Divergences between Language Models and Human Brains](https://mlsanity.com/paper/2311.09308/) (2023)
- [Sneaking Syntax into Transformer Language Models with Tree Regularization](https://mlsanity.com/paper/2411.18885/) (2024)
- [Dual Alignment Between Language Model Layers and Human Sentence Processing](https://mlsanity.com/paper/2604.18563/) (2026)

## Abstract (arXiv)

Recurrent neural network grammars (RNNGs) are generative models of (tree,string) pairs that rely on neural networks to evaluate derivational choices. Parsing with them using beam search yields a variety of incremental complexity metrics such as word surprisal and parser action count. When used as regressors against human electrophysiological responses to naturalistic text, they derive two amplitude effects: an early peak and a P600-like later peak. By contrast, a non-syntactic neural language model yields no reliable effects. Model comparisons attribute the early peak to syntactic composition within the RNNG. This pattern of results recommends the RNNG+beam search combination as a mechanistic model of the syntactic processing that occurs during normal human language comprehension.