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Finding Syntax in Human Encephalography with Beam Search

arXiv 1806.04127 ↗First posted 11 Jun 2018cs.CLcited by 41

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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.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2018.

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Citing papers per year: 2021: 9; 2022: 9; 2023: 7; 2024: 8; 2025: 3; 2026: 5.

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  6. 2021Disentangling Syntax and Semantics in the Brain with Deep Networkscited by 39
  7. 2023Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokenscited by 39
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  11. 2025Large Language Models Are Human-Like Internallycited by 28
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  11. 2021Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammarscited by 5
  12. 2021Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Modelscited by 5
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  18. 2026Dual Alignment Between Language Model Layers and Human Sentence Processingcited by 2
  19. 2026MTA: Multi-Granular Trajectory Alignment for Large Language Model Distillationcited by 2
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  23. 2022Multilingual Syntax-aware Language Modeling through Dependency Tree Conversioncited by 0
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  26. 2024Investigating the Timescales of Language Processing with EEG and Language Modelscited by 0
  27. 2026Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesiscited by 0
  28. 2026Syntactic Belief Update as the Driver of Garden Path Processing Difficultycited by 0
  29. 2026Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languagescited by 0