mlsanityData to 7 Oct 2026

How well does surprisal explain N400 amplitude under different experimental conditions?

arXiv 2010.04844 ↗First posted 9 Oct 2020cs.CL cs.AI cs.ITcited by 18

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Abstract (arXiv)

We investigate the extent to which word surprisal can be used to predict a neural measure of human language processing difficulty - the N400. To do this, we use recurrent neural networks to calculate the surprisal of stimuli from previously published neurolinguistic studies of the N400. We find that surprisal can predict N400 amplitude in a wide range of cases, and the cases where it cannot do so provide valuable insight into the neurocognitive processes underlying the response.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Oct 2020.

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Citing papers per year: 2021: 4; 2022: 5; 2023: 5; 2024: 3; 2025: 1.

18 papers.

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  2. 2022Neural reality of argument structure constructionscited by 25
  3. 2021How is BERT surprised? Layerwise detection of linguistic anomaliescited by 24
  4. 2024Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Modelscited by 24
  5. 2021So Cloze yet so Far: N400 Amplitude is Better Predicted by Distributional Information than Human Predictability Judgementscited by 15
  6. 2021Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude?cited by 14
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  10. 2023Probing Quantifier Comprehension in Large Language Models: Another Example of Inverse Scalingcited by 5
  11. 2022Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifierscited by 4
  12. 2024Decomposition of surprisal: Unified computational model of ERP components in language processingcited by 2
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  2. 2023Are words equally surprising in audio and audio-visual comprehension?cited by 1
  3. 2023Plausibility Processing in Transformer Language Models: Focusing on the Role of Attention Heads in GPTcited by 1
  4. 2025Uniform Information Density and Syntactic Reduction: Revisiting textitthat-Mentioning in English Complement Clausescited by 1
  5. 2022A unified information-theoretic model of EEG signatures of human language processingcited by 0
  6. 2024Animate, or Inanimate, That is the Question for Large Language Modelscited by 0