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A Neural Model of Adaptation in Reading

arXiv 1808.09930 ↗First posted 29 Aug 2018cs.CLcited by 18

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Abstract (arXiv)

It has been argued that humans rapidly adapt their lexical and syntactic expectations to match the statistics of the current linguistic context. We provide further support to this claim by showing that the addition of a simple adaptation mechanism to a neural language model improves our predictions of human reading times compared to a non-adaptive model. We analyze the performance of the model on controlled materials from psycholinguistic experiments and show that it adapts not only to lexical items but also to abstract syntactic structures.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Aug 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 6; 2023: 2; 2024: 5; 2025: 0; 2026: 1.

18 papers.

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  1. 2021Language Model Evaluation Beyond Perplexitycited by 78
  2. 2021Structural Persistence in Language Models: Priming as a Window into Abstract Language Representationscited by 44
  3. 2023Modeling rapid language learning by distilling Bayesian priors into artificial neural networkscited by 42
  4. 2021From partners to populations: A hierarchical Bayesian account of coordination and conventioncited by 27
  5. 2022Language model acceptability judgements are not always robust to contextcited by 22
  6. 2024Do Language Models Exhibit Human-like Structural Priming Effects?cited by 21
  7. 2022On the Effect of Anticipation on Reading Timescited by 19
  8. 2022A Property Induction Framework for Neural Language Modelscited by 9
  9. 2021Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammarscited by 5
  10. 2022Construction Repetition Reduces Information Rate in Dialoguecited by 5
  11. 2023Can training neural language models on a curriculum with developmentally plausible data improve alignment with human reading behavior?cited by 5
  12. 2024Is In-Context Learning a Type of Error-Driven Learning? Evidence from the Inverse Frequency Effect in Structural Primingcited by 4
6 more
  1. 2024Modeling Bilingual Sentence Processing: Evaluating RNN and Transformer Architectures for Cross-Language Structural Primingcited by 3
  2. 2022Discourse Context Predictability Effects in Hindi Word Ordercited by 2
  3. 2022Dual Mechanism Priming Effects in Hindi Word Ordercited by 1
  4. 2024Investigating Idiomaticity in Word Representationscited by 1
  5. 2024Does Dependency Locality Predict Non-canonical Word Order in Hindi?cited by 0
  6. 2026Patterns of Priming in Production: Lexical, Semantic and Structural Alignment in Language Model Generationcited by 0