Why does surprisal from larger transformer-based language models provide a poorer fit to human reading times?

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

A Mathematical Theory of Communication

Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486 · see citing works

Fitting Linear Mixed-Effects Models Using <b>lme4</b>

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653 · see citing works

Mental models : Towards a cognitive science of language, inference, and consciousness. By Philip N. Johnson-Laird (Cognitive science series, 6.) Cambridge, MA: Harvard University Press, 1983. Pp. 528. $25.00.

Ford, M. (1985) · Language · doi:10.2307/414498 · Cited by 4,419 · see citing works

Expectation-based syntactic comprehension

Lévy, R. (2007) · Cognition · doi:10.1016/j.cognition.2007.05.006 · Cited by 2,220 · see citing works

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

(2021) · doi:10.18653/v1/2021.emnlp-main · Cited by 1,685 · see citing works

Proceedings of the 28th International Conference on Computational Linguistics

(2020) · doi:10.18653/v1/2020.coling-main · Cited by 1,436 · see citing works

A probabilistic earley parser as a psycholinguistic model

Hale, J. (2001) · Second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies 2001 - NAACL '01 · doi:10.3115/1073336.1073357 · Cited by 1,150 · see citing works

Neural Machine Translation of Rare Words with Subword Units

Sennrich, R., Haddow, B., & Birch, A. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1162 · Cited by 974 · see citing works

The Dependency Locality Theory: A Distance-Based Theory of Linguistic Complexity

Gibson, E. A. F. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/3654.003.0008 · Cited by 952 · see citing works

Computational principles of working memory in sentence comprehension

Lewis, R. L., Vasishth, S., & Dyke, J. A. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.08.007 · Cited by 854 · see citing works

The effect of word predictability on reading time is logarithmic

Smith, N. J., & Levy, R. (2013) · Cognition · doi:10.1016/j.cognition.2013.02.013 · Cited by 725 · see citing works

Data from eye-tracking corpora as evidence for theories of syntactic processing complexity

Demberg, V., & Keller, F. (2008) · Cognition · doi:10.1016/j.cognition.2008.07.008 · Cited by 572 · see citing works

Recurrent Neural Network Grammars

Dyer, C., Kuncoro, A., Ballesteros, M., & Smith, N. A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1024 · Cited by 517 · see citing works

The neural architecture of language: Integrative modeling converges on predictive processing

Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105646118 · Cited by 517 · see citing works

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Black, S., Biderman, S., Hallahan, E., Anthony, Q. G., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., et al. (2022) · doi:10.18653/v1/2022.bigscience-1.9 · Cited by 407 · see citing works

GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow

Black, S., Leo, G., Wang, P., Leahy, C., & Biderman, S. (2021) · Zenodo · doi:10.5281/zenodo.5297715 · Cited by 324 · see citing works

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Shain, C., Blank, I. A., van Schijndel, M., Schuler, W., & Fedorenko, E. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107307 · Cited by 215 · see citing works

Image, language, brain : papers from the First Mind Articulation Project Symposium

Marantz, A., 宮下, 保., & O'Neil, W. A. (2000) · Cited by 209 · see citing works

Quantifying Memorization Across Neural Language Models

Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., & Zhang, C. (2022) · arXiv · doi:10.48550/arxiv.2202.07646 · Cited by 204 · see citing works

Predictive power of word surprisal for reading times is a linear function of language model quality

Goodkind, A., & Bicknell, K. (2018) · Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · doi:10.18653/v1/w18-0102 · Cited by 190 · see citing works

Proceedings of the 21st Annual Meeting of the Cognitive Science Society

Sturt, P., Branigan, H. P., & Matsumoto-Sturt, Y. (1999) · Cited by 178 · see citing works

Distributed Representations, Simple Recurrent Networks, and Grammatical Structure

Elman, J. L. (1991) · Springer eBooks · doi:10.1007/978-1-4615-4008-3_5 · Cited by 173 · see citing works

Proceedings of the 25th Conference on Computational Natural Language Learning

(2021) · doi:10.18653/v1/2021.conll-1 · Cited by 150 · see citing works

Finding syntax in human encephalography with beam search

Hale, J., Dyer, C., Kuncoro, A., & Brennan, J. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1254 · Cited by 138 · see citing works

On the Predictive Power of Neural Language Models for Human Real-Time Comprehension BehaviorPreprint

Wilcox, E., Gauthier, J., Hu, J., Qian, P., & Lévy, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01912 · Cited by 125 · see citing works

Human Sentence Processing: Recurrence or Attention?

Merkx, D., & Frank, S. L. (2021) · Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · doi:10.18653/v1/2021.cmcl-1.2 · Cited by 100 · see citing works

Single‐Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty

van Schijndel, M., & Linzen, T. (2021) · Cognitive Science · doi:10.1111/cogs.12988 · Cited by 74 · see citing works

A resource-rational model of human processing of recursive linguistic structure

Hahn, M., Futrell, R., Lévy, R., & Gibson, E. A. F. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2122602119 · Cited by 68 · see citing works

ERP indexes of functional differences in brain activation during proper and common names retrieval

Proverbio, A. M., Lilli, S., Semenza, C., & Zani, A. (2001) · Neuropsychologia · doi:10.1016/s0028-3932(01)00003-3 · Cited by 63 · see citing works

The Natural Stories corpus: a reading-time corpus of English texts containing rare syntactic constructions

Futrell, R., Gibson, E. A. F., Tily, H. J., Blank, I., Vishnevetsky, A., Piantadosi, S. T., & Fedorenko, E. G. (2020) · Language Resources and Evaluation · doi:10.1007/s10579-020-09503-7 · Cited by 63 · see citing works

Lower Perplexity is Not Always Human-Like

Kuribayashi, T., Oseki, Y., Ito, T., Yoshida, R., Asahara, M., & Inui, K. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.405 · Cited by 58 · see citing works

Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators

Oh, B.-D., Clark, C., & Schuler, W. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.777963 · Cited by 51 · see citing works

Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling

Hao, Y., Mendelsohn, S., Sterneck, R., Martinez, R., & Frank, R. (2020) · Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · doi:10.18653/v1/2020.cmcl-1.10 · Cited by 48 · see citing works

Accurate Unbounded Dependency Recovery using Generalized Categorial Grammars

Nguyen, L. V., van Schijndel, M., & Schuler, W. (2012) · International Conference on Computational Linguistics · Cited by 42 · see citing works

Context Limitations Make Neural Language Models More Human-Like

Kuribayashi, T., Oseki, Y., Brassard, A., & Inui, K. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.712 · Cited by 40 · see citing works

Accounting for Agreement Phenomena in Sentence Comprehension with Transformer Language Models: Effects of Similarity-based Interference on Surprisal and Attention

Ryu, S. H., & Lewis, R. L. (2021) · Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · doi:10.18653/v1/2021.cmcl-1.6 · Cited by 34 · see citing works

Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities

Arehalli, S., Dillon, B., & Linzen, T. (2022) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2022.conll-1.20 · Cited by 30 · see citing works

Comparing gated and simple recurrent neural network architectures as models of human sentence processingPreprint

Aurnhammer, C., & Frank, S. L. (2018) · doi:10.31234/osf.io/wec74 · Cited by 30 · see citing works

Continuous-time deconvolutional regression for psycholinguistic modeling

Shain, C., & Schuler, W. (2021) · Cognition · doi:10.1016/j.cognition.2021.104735 · Cited by 28 · see citing works

Relative Importance in Sentence Processing

Hollenstein, N., & Beinborn, L. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-short.19 · Cited by 23 · see citing works

Recognizing the emotional valence of names: An ERP study

Wang, L., Zhu, Z., BASTIAANSEN, M. C. M., Peter Hagoort, A., & Yang, Y. (2013) · Brain and Language · doi:10.1016/j.bandl.2013.01.006 · Cited by 22 · see citing works

Surprisal Estimators for Human Reading Times Need Character Models

Oh, B.-D., Clark, C., & Schuler, W. (2021) · doi:10.18653/v1/2021.acl-long.290 · Cited by 16 · see citing works

Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 SurprisalPreprint

Oh, B.-D., & Schuler, W. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.11185 · Cited by 3 · see citing works

OPT: Open Pre-trained Transformer Language Models

Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.01068

Dissociating language and thought in large language models

Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.011 · Cited by 345 · see citing works

Testing the Predictions of Surprisal Theory in 11 Languages

Wilcox, E., Pimentel, T., Meister, C., Cotterell, R., & Lévy, R. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00612 · Cited by 87 · see citing works

Language in Brains, Minds, and Machines

Tuckute, G., Kanwisher, N., & Fedorenko, E. G. (2024) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-120623-101142 · Cited by 69 · see citing works

Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training

Hosseini, E. A., Schrimpf, M., Zhang, Y., Bowman, S., Zaslavsky, N., & Fedorenko, E. (2024) · Neurobiology of Language · doi:10.1162/nol_a_00137 · Cited by 63 · see citing works

Large-scale benchmark yields no evidence that language model surprisal explains syntactic disambiguation difficultyPreprint

Huang, K., Arehalli, S., Kugemoto, M., Muxica, C., Prasad, G., Dillon, B., & Linzen, T. (2023) · Journal of Memory and Language · doi:10.1016/j.jml.2024.104510 · Cited by 70 · see citing works

On the Effect of Anticipation on Reading Times

Pimentel, T., Meister, C., Wilcox, E., Lévy, R., & Cotterell, R. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00603 · Cited by 29 · see citing works

Prediction in reading: A review of predictability effects, their theoretical implications, and beyond

Wong, R., Reichle, E. D., & Veldre, A. (2024) · Psychonomic Bulletin & Review · doi:10.3758/s13423-024-02588-z · Cited by 28 · see citing works

Clinical efficacy of pre-trained large language models through the lens of aphasia

Cong, Y., LaCroix, A. N., & Lee, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-66576-y · Cited by 25 · see citing works

Covert Prompt Transmission for Secure Large Language Model Services

Zhang, R., Liu, Y., Tang, S., Wang, J., Niyato, D. T., Sun, G., Li, Y., & Sun, S. (2025) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2025.3647412 · Cited by 25 · see citing works

The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing

Hoover, J. L., Sonderegger, M., Piantadosi, S. T., & O’Donnell, T. J. (2023) · Open Mind · doi:10.1162/opmi_a_00086 · Cited by 23 · see citing works

Cloze probability, predictability ratings, and computational estimates for 205 English sentences, aligned with existing EEG and reading time data

de Varda, A. G., Marelli, M., & Amenta, S. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02261-8 · Cited by 30 · see citing works

Scaling in Cognitive Modelling: a Multilingual Approach to Human Reading Times

de Varda, A. G., & Marelli, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.14 · Cited by 24 · see citing works

Dense and Disconnected: Analyzing the Sedimented Style of ChatGPT-Generated Text at Scale

Markey, B., Brown, D. W., Laudenbach, M., & Kohler, A. (2024) · Written Communication · doi:10.1177/07410883241263528 · Cited by 27 · see citing works

Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens

Oh, B.-D., & Schuler, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.128 · Cited by 21 · see citing works

Driving and suppressing the human language network using large language modelsPreprint

Tuckute, G., Sathe, A., Srikant, S., Taliaferro, M., Wang, M., Schrimpf, M., Kay, K., & Fedorenko, E. G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.16.537080 · Cited by 20 · see citing works

Distributional Semantics: Meaning Through Culture and Interaction

Contreras Kallens, P., & Christiansen, M. H. (2025) · Topics in Cognitive Science · doi:10.1111/tops.12771 · Cited by 17 · see citing works

Language models outperform cloze predictability in a cognitive model of reading

Rego, A. T. L., Snell, J., & Meeter, M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012117 · Cited by 17 · see citing works

Got It! Prompting Readability Using ChatGPT to Enhance Academic Texts for Diverse Learning Needs

Hedlin, E., Estling, L., Wong, J., Epp, C. D., & Viberg, O. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706483 · Cited by 16 · see citing works

Surprisal does not explain syntactic disambiguation difficulty: evidence from a large-scale benchmarkPreprint

Huang, K., Arehalli, S., Kugemoto, M., Muxica, C., Prasad, G., Dillon, B., & Linzen, T. (2023) · doi:10.31234/osf.io/z38u6 · Cited by 14 · see citing works

Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship?

Nair, S., & Resnik, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.752 · Cited by 17 · see citing works

The Linearity of the Effect of Surprisal on Reading Times across Languages

Xu, W., Chon, J., Liu, T., & Futrell, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1052 · Cited by 11 · see citing works

Modeling Brain Representations of Words' Concreteness in Context Using GPT‐2 and Human Ratings

Bruera, A., Tao, Y., Anderson, A. J., Çokal, D., Haber, J., & Poesio, M. (2023) · Cognitive Science · doi:10.1111/cogs.13388 · Cited by 10 · see citing works

Bigger is not always better: The importance of human-scale language modeling for psycholinguistics

Wilcox, E. G., Hu, M. Y., Mueller, A., Warstadt, A., Choshen, L., Zhuang, C., Williams, A., Cotterell, R., & Linzen, T. (2025) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104650 · Cited by 9 · see citing works

Language Model Quality Correlates with Psychometric Predictive Power in Multiple Languages

Wilcox, E., Meister, C., Cotterell, R., & Pimentel, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.466 · Cited by 9 · see citing works

Characterizing Learning Curves During Language Model Pre-Training: Learning, Forgetting, and Stability

Chang, T. A., Tu, Z., & Bergen, B. K. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00708 · Cited by 13 · see citing works

Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models

Michaelov, J. A., Arnett, C., Chang, T. A., & Bergen, B. K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.227 · Cited by 13 · see citing works

Expert-adapted language models improve the fit to reading times

Škrjanec, I., Broy, F. Y., & Demberg, V. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.344 · Cited by 9 · see citing works

Information Value: Measuring Utterance Predictability as Distance from Plausible Alternatives

Giulianelli, M., Wallbridge, S., & Fernández, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.343 · Cited by 7 · see citing works

On the limits of LLM surprisal as a functional explanation of the N400 and P600

Krieger, B., Brouwer, H., Aurnhammer, C., & Crocker, M. W. (2025) · Brain Research · doi:10.1016/j.brainres.2025.149841 · Cited by 7 · see citing works

Large Language Models Are Human-Like Internally

Kuribayashi, T., Oseki, Y., Taieb, S. B., Inui, K., & Baldwin, T. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.58 · Cited by 19 · see citing works

Are Language Models Sensitive to Semantic Attraction? A Study on Surprisal

Cong, Y., Chersoni, E., Hsu, Y.-Y., & Lenci, A. (2023) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2023.starsem-1.13 · Cited by 7 · see citing works

On the influence of discourse connectives on the predictions of humans and language models

Britton, J., Cong, Y., Hsu, Y.-Y., Chersoni, E., & Blache, P. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1363120 · Cited by 6 · see citing works

Expectations modulate retrieval interference during ellipsis resolution

Tung, T.-Y., & Brennan, J. (2023) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2023.108680 · Cited by 6 · see citing works

Memory for prediction: A Transformer-based theory of sentence processing

Ryu, S. H., & Lewis, R. L. (2025) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104670 · Cited by 5 · see citing works

Constraint satisfaction in large language models

Jacobs, C. L., & MacDonald, M. C. (2024) · Language Cognition and Neuroscience · doi:10.1080/23273798.2024.2364339 · Cited by 5 · see citing works

Informativity enhances memory robustness against interference in sentence comprehension

Xu, W., & Futrell, R. (2025) · Journal of Memory and Language · doi:10.1016/j.jml.2024.104603 · Cited by 5 · see citing works

Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models

Rambelli, G., Chersoni, E., Senaldi, M. S. G., Blache, P., & Lenci, A. (2023) · Workshop on Multiword Expressions (MWE) · doi:10.18653/v1/2023.mwe-1.13 · Cited by 5 · see citing works

Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures

Steuer, J., Mosbach, M., & Klakow, D. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-babylm.12 · Cited by 2 · see citing works

Filtered Corpus Training (FiCT) Shows that Language Models Can Generalize from Indirect Evidence

Patil, A., Jumelet, J., Chiu, Y. Y., Lapastora, A., Shen, P. S., Wang, L., Willrich, C., & Steinert‐Threlkeld, S. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00720 · Cited by 8 · see citing works

The late frontal positivity reflects incremental mental model updating: Graded predictability effects during authentic text reading

Wang, W., Chen, L., & Perfetti, C. A. (2025) · Brain and Language · doi:10.1016/j.bandl.2025.105667 · Cited by 5 · see citing works

Humans and language models diverge when predicting repeating text

Vaidya, A. R., Turek, J. S., & Huth, A. G. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.5 · Cited by 4 · see citing works

Category Locality Theory: A unified account of locality effects in sentence comprehension

Isono, S. (2024) · Cognition · doi:10.1016/j.cognition.2024.105766 · Cited by 4 · see citing works

Family lexicon: Using language models to encode memories of personally familiar and famous people and places in the brain

Bruera, A., & Poesio, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0291099 · Cited by 4 · see citing works

A systematic evaluation of Dutch large language models’ surprisal estimates in sentence, paragraph and book reading

Boeve, S., & Bogaerts, L. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02774-4 · Cited by 4 · see citing works

Neural Networks as Cognitive Models of the Processing of Syntactic Constraints

Arehalli, S., & Linzen, T. (2024) · Open Mind · doi:10.1162/opmi_a_00137 · Cited by 4 · see citing works

Language models that match reader experience are better predictors of reading times

Škrjanec, I., & Demberg, V. (2025) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104677 · Cited by 3 · see citing works

Can Large Language Models Generate Useful Linguistic Corpora?: A Case Study of the Word Frequency Effect in Young German Readers

Schepens, J. J., Woloszyn, H., Marx, N., & Gagl, B. (2025) · Open Mind · doi:10.1162/opmi.a.30 · Cited by 3 · see citing works

A predictive coding model for online sentence processing

Ohams, C., Nair, S., Bhattasali, S., & Resnik, P. (2025) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104705 · Cited by 3 · see citing works

When Language Models Fall in Love: Animacy Processing in Transformer Language Models

Hanna, M., Belinkov, Y., & Pezzelle, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.744 · Cited by 3 · see citing works

On the Mathematical Relationship Between Contextual Probability and N400 Amplitude

Michaelov, J. A., & Bergen, B. K. (2024) · Open Mind · doi:10.1162/opmi_a_00150 · Cited by 3 · see citing works

Neural processing of children’s theory of mind in a naturalistic story-listening paradigm

Yu, C., Eggleston, R. L., Zhang, K., Nickerson, N., Sun, X., Marks, R. A., Hu, X., Brennan, J., Wellman, H. M., & Kovelman, I. (2025) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaf022 · Cited by 3 · see citing works

ScanDL 2.0: A Generative Model of Eye Movements in Reading Synthesizing Scanpaths and Fixation Durations

Bolliger, L., Reich, D., & Jäger, L. (2025) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3725830 · Cited by 10 · see citing works

EMTeC: A corpus of eye movements on machine-generated texts

Bolliger, L. S., Haller, P., Cretton, I. C. R., Reich, D. R., Kew, T., & Jäger, L. A. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02677-4 · Cited by 5 · see citing works

Sentence processing by humans and machines: Large language models as a tool to better understand human reading

Kaye, N. G., & Gordon, P. C. (2025) · Psychonomic Bulletin & Review · doi:10.3758/s13423-025-02756-9 · Cited by 3 · see citing works

Neural Correlates of Object-Extracted Relative Clause Processing Across English and Chinese

Dunagan, D. G., Stanojević, M., Coavoux, M., Zhang, S., Bhattasali, S., Li, J., Brennan, J., & Hale, J. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00110 · Cited by 3 · see citing works

Dissociable frequency effects attenuate as large language model surprisal predictors improve

Oh, B.-D., & Schuler, W. (2025) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104645 · Cited by 5 · see citing works

Strategic resource allocation in memory encoding: An efficiency principle shaping language processing

Xu, W., & Futrell, R. (2025) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104706 · Cited by 2 · see citing works

Can Language Models Trained on Written Monologue Learn to Predict Spoken Dialogue?

Umair, M., Mertens, J. B., Warnke, L., & de Ruiter, J. P. (2024) · Cognitive Science · doi:10.1111/cogs.70013 · Cited by 3 · see citing works

Munich Sentence (MuSe) Database: Completion norms and audio recordings for 619 German sentences

Sterner, E., Stadler, M., & Knolle, F. (2026) · Behavior Research Methods · doi:10.3758/s13428-026-02993-3 · Cited by 2 · see citing works

Expert-adapted language models improve the fit to reading timesPreprint

Škrjanec, I., Broy, F. Y., & Demberg, V. (2023) · doi:10.31234/osf.io/dc8y6 · Cited by 2 · see citing works

Comprehenders' Error Correction Mechanisms Are Finely Calibrated to Language Production Statistics

Qian, P., & Lévy, R. (2026) · Cognitive Science · doi:10.31234/osf.io/e3v5b · Cited by 2 · see citing works

Scaling, but not instruction tuning, increases large language models’ alignment with language processing in the human brainPreprint

Gao, C., Ma, Z., Chen, J., Li, P., Huang, S., & Li, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.15.608196 · Cited by 2 · see citing works

Computational Sentence‐Level Metrics of Reading Speed and Its Ramifications for Sentence Comprehension

Sun, K., & Wang, R. (2025) · Cognitive Science · doi:10.1111/cogs.70092 · Cited by 4 · see citing works

AlphaDepLength: A New Measure of Syntactic Complexity Based on Communicative Efficiency and Its Prediction of Reading Time

Lei, L., & Shi, Y. (2025) · Journal of Quantitative Linguistics · doi:10.1080/09296174.2025.2559597 · Cited by 1 · see citing works

Scope interpretation in natural and artificial language processing

Fang, S., He, J., & Liu, X. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106527 · Cited by 1 · see citing works

Better language models better model the N400, but not reading time

Michaelov, J. A., & Bergen, B. K. (2026) · Journal of Memory and Language · doi:10.1016/j.jml.2026.104762 · Cited by 1 · see citing works

Implications of learning models for linguistic theory: the richness of memory, the importance of loss, and the emergence of structure

Kapatsinski, V. M., & Beguš, G. (2026) · Linguistics Vanguard · doi:10.1515/lingvan-2026-0030 · Cited by 1 · see citing works

Eyettention II: A dual-sequence architecture for modeling fixation location, within-word landing position, and fixation duration in reading

Deng, S., Ding, C., Reich, D. R., Prasse, P., & Jäger, L. A. (2026) · Behavior Research Methods · doi:10.3758/s13428-026-03118-6 · Cited by 1 · see citing works

A surprisal oracle for active curriculum language modeling

Hong, X., Loáiciga, S., & Sayeed, A. (2023) · BabyLM Challenge · doi:10.18653/v1/2023.conll-babylm.22 · Cited by 1 · see citing works

Optimizing Predictive Metrics for Human Reading BehaviorPreprint

Sun, K. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.03.556078 · Cited by 1 · see citing works

BLiMP-NL: A corpus of Dutch minimal pairs and acceptability judgements for language model evaluationPreprint

Suijkerbuijk, M., Prins, Z., Kloots, M. D. H., Zuidema, W. H., & Frank, S. L. (2024) · doi:10.31234/osf.io/mhjbx · Cited by 3 · see citing works

A neurophysiological alignment metric reveals a supportive role of unexpected stimuli in naturalistic language comprehensionPreprint

Platt, A. L. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.27.684752 · Cited by 1 · see citing works

Proceedings of the 19th Workshop on Multiword Expressions (MWE 2023)

(2023) · doi:10.18653/v1/2023.mwe-1

Procedural Strategies

Beinborn, L., & Hollenstein, N. (2023) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-43260-6_6

Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue

Молнар, А. А., Jumelet, J., Giulianelli, M., & Sinclair, A. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.18

Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention

Gao, C., Huang, S., Li, J., & Chen, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.868 · Cited by 2 · see citing works

Constraint satisfaction in large language modelsPreprint

Jacobs, C. L., & MacDonald, M. C. (2024) · doi:10.31234/osf.io/xfnpb

Large Language Models in Psycholinguistic Studies

Günther, F., & Cassani, G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-95504-1.00749-3 · Cited by 4 · see citing works

The time course of local coherence effects in German: Evidence from self-paced reading times and event-related potentials

Schoknecht, P., Paape, D., & Vasishth, S. (2026) · Cognitive Psychology · doi:10.1016/j.cogpsych.2026.101816

The Relationship Between Surprisal and Prosodic Prominence in Conversation Reflects Intelligibility‐Oriented Pressures

Clark, T. H., Poliak, M., Regev, T. I., Haskins, A. J., Robertson, C. E., & Gibson, E. A. F. (2025) · Cognitive Science · doi:10.1111/cogs.70134

Regressive Eye Movements in Reading Go from Easy to Difficult WordsPreprint

Rego, A. T. L., Melo, A. N., Snell, J., & Meeter, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5405257

Modeling Memory Effects in a Head-Final Language With Category Locality

Isono, S., Kajikawa, K., Oseki, Y., & Asahara, M. (2026) · Open Mind · doi:10.1162/opmi.a.384

Neural tracking of surprisal in Spanish-English bilingual children during naturalistic heritage language listening

Salig, L. K., Yu, C., Leachman, M. A., Purushotham, N. D., Ortiz-Villalobos, V., Flores-Gaona, Z., Negrón, V. V., Girardini, C., Fang, H.-Y., Baron, A., et al. (2026) · Brain and Language · doi:10.1016/j.bandl.2026.105833

Semantic-Graph-Driven AI for Predicting Reader Engagement in English Texts Across Multiple Digital Platforms

Mahesh, P., Tripathi, J., Mary, F. A., Durairaj, M., Bansode, G. S., & Vijayalakshmi, B. A. (2025) · International Conference on NexGen Networks and Cybernetics (IC2NC) · doi:10.1109/ic2nc67409.2025.11376099

Incremental alternative sampling as a lens into the temporal and representational resolution of linguistic prediction

Giulianelli, M., Wallbridge, S., Cotterell, R., & Fernández, R. (2026) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104715 · Cited by 1 · see citing works

Structural priming in humans and large language models

Sinclair, A., Klimovich‐Gray, A., Jumelet, J., Adamian, N., & Konopka, A. E. (2026) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104713

Information Theory in Translation and Interpreting Studies

Teich, E., Przybyl, H., & Lapshinova‐Koltunski, E. (2026) · Elsevier eBooks · doi:10.1016/b978-0-323-95504-1.01583-0

What drives regressions in reading? Insights from surprisal and saliency from language models

Rego, A. T. L., Melo, A. N., Snell, J., & Meeter, M. (2026) · Cognition · doi:10.1016/j.cognition.2026.106535

Quantitative syntax, formal syntax and information theory : bridging gaps by studying French variation

Liang, Y. (2023) · theses.fr (ABES) · doi:10.70675/bf4498a1z8454z4bb4zbf5ez6e4af27e1bc3

Temporal Dissociation of Syntactic Disambiguation and Memory Retrieval during Sentence Processing: Naturalistic MEG Evidence from Interpretable ModelsPreprint

Dunagan, D. G., Low, D. S., Yue, S., Meyer, L., & Hale, J. T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.20.719609

They saw it, onu , 它, coming: an information-theoretic study of cross-linguistic variation in personal pronouns

Talamo, L., Steuer, J., & Verkerk, A. (2026) · Linguistics · doi:10.1515/ling-2024-0122

Semantic Processing and Individual Variation: Experimental and Modeling Evidence from Quantifier Scope

Fang, S., Li, Y., & Cong, Y. (2026) · Languages · doi:10.3390/languages11060126

The Grain of Prediction: Character-Level Language Models Better Approximate Human Predictability in Chinese ReadingPreprint

Laubrock, J., & Yan, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6986543

“What we found was…” Syntactic prominence cues to information structure affect semantic predictionPreprint

Beier, E. J., Zhou, A., & Ferreira, F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6997361

Are language models models?

Resnik, P. (2026) · Behavioral and Brain Sciences · doi:10.1017/s0140525x26104762

Word Predictability as a Measure of Second Language Proficiency

Holmes, L., Crossley, S. A., Morris, W., & Choi, J. S. (2026) · Language Learning · doi:10.1111/lang.70037

Which Word Repetitions Do Readers Fail to Notice? Exploring Effects of Word Length, Word Class, Predictability, and Frequency

Bentley, K., & Staub, A. (2026) · Quarterly Journal of Experimental Psychology · doi:10.1177/17470218261473201

Linear asymmetries and incremental parsing

Abels, K., & Neeleman, A. (2026) · Language · doi:10.1017/s009785072600010x

Cognitive Processes of Probabilistic Prediction in Reading: Language Model Surprisal Across Model Sizes, Token Granularity, and Reading Paradigms

Liu, S., & Mei, Y. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14080171

Eye movements reveal a dissociation between prediction and structural processing difficulty in language comprehension

Timkey, W., Huang, K.-J., Oh, B.-D., Prasad, G., Arehalli, S., Linzen, T., & Dillon, B. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2532230123 · Cited by 4 · see citing works

Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens

Oh, B.-D., & Schuler, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.128 · Cited by 21 · see citing works

Frequency Explains the Inverse Correlation of Large Language Models’ Size, Training Data Amount, and Surprisal’s Fit to Reading Times

Oh, B.-D., Yue, S., & Schuler, W. (2024) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2024.eacl-long.162 · Cited by 10 · see citing works

Evaluating the Cognitive Plausibility of Transformer-Based Models: Predicting Articulation Rate in Read Speech from Surprisal Estimates

Giachi, M. (2025) · Uppsala University Publications (Uppsala University)

Transformer-Based Language Models and Their Alignment with Human Reading Comprehension

Jonsdottir, O., Solberg, F., & Toth, M. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22696830

Temperature-scaling surprisal estimates improve fit to human reading times – but does it do so for the “right reasons”?

Liu, T., Škrjanec, I., & Demberg, V. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.519 · Cited by 8 · see citing works