---

1. How Open Must Language Models be to Enable Reliable Scientific Inference?
   arXiv.org2026-03-27alphaXiv arXiv S2
2. N-gram-like Language Models Predict Reading Time Best
   arXiv.org2026-03-10alphaXiv arXiv S2
3. Language Statistics and False Belief Reasoning: Evidence from 41 Open-Weight LMs
   Annual Meeting of the Association for Computational Linguistics2026-02-17alphaXiv arXiv S2
4. Better language models better model the N400, but not reading time
   Journal of Memory and Language2026S2
5. Disaggregation Reveals Hidden Training Dynamics: The Case of Agreement Attraction
   arXiv.org2025-10-28alphaXiv arXiv S2
6. Language Model Behavioral Phases are Consistent Across Architecture, Training Data, and Scale
   Neural Information Processing Systems2025-10-28alphaXiv arXiv S2
7. Not quite Sherlock Holmes: Language model predictions do not reliably differentiate impossible from improbable events
   Annual Meeting of the Association for Computational Linguistics2025-06-07alphaXiv arXiv S2
8. On the Acquisition of Shared Grammatical Representations in Bilingual Language Models
   Annual Meeting of the Association for Computational Linguistics2025-03-05alphaXiv arXiv S2
9. On the Mathematical Relationship Between Contextual Probability and N400 Amplitude
   Open Mind2024-06-01S2
10. Revenge of the Fallen? Recurrent Models Match Transformers at Predicting Human Language Comprehension Metrics
    arXiv.org2024-04-30alphaXiv arXiv S2
11. Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models
    Conference on Empirical Methods in Natural Language Processing2023-11-15alphaXiv arXiv S2
12. Crosslingual Structural Priming and the Pre-Training Dynamics of Bilingual Language Models
    arXiv.org2023-10-11alphaXiv arXiv S2
13. Ignoring the alternatives: The N400 is sensitive to stimulus preactivation alone.
    Cortex; a journal devoted to the study of the nervous system and behavior2023-08-01S2
14. Measuring Sentence Information via Surprisal: Theoretical and Clinical Implications in Nonfluent Aphasia
    Annals of Neurology2023-07-18S2
15. Emergent inabilities? Inverse scaling over the course of pretraining
    Conference on Empirical Methods in Natural Language Processing2023-05-24alphaXiv arXiv S2
16. Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects
    Neurobiology of Language2023-04-05S2
17. Can Peanuts Fall in Love with Distributional Semantics?
    Annual Meeting of the Cognitive Science Society2023-01-20alphaXiv arXiv S2
18. 'Rarely' a problem? Language models exhibit inverse scaling in their predictions following 'few'-type quantifiers
    Annual Meeting of the Association for Computational Linguistics2022-12-16alphaXiv arXiv S2
19. A computational approach for measuring sentence information via surprisal: theoretical implications in nonfluent primary progressive aphasia
    medRxiv2022-11-29S2
20. Collateral facilitation in humans and language models
    Conference on Computational Natural Language Learning2022-11-09alphaXiv arXiv S2
21. Do Large Language Models know what humans know?
    Cognitive Sciences2022-09-04alphaXiv arXiv S2
22. Do Language Models Make Human-like Predictions about the Coreferents of Italian Anaphoric Zero Pronouns?
    International Conference on Computational Linguistics2022-08-30alphaXiv arXiv S2
23. So Cloze Yet So Far: N400 Amplitude Is Better Predicted by Distributional Information Than Human Predictability Judgements
    IEEE Transactions on Cognitive and Developmental Systems2021-09-02alphaXiv arXiv S2
24. Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude?
    Annual Meeting of the Cognitive Science Society2021-07-20alphaXiv arXiv S2
25. How well does surprisal explain N400 amplitude under different experimental conditions?
    Conference on Computational Natural Language Learning2020-10-09alphaXiv arXiv S2
26. The Young and the Old: (t) Release in Elderspeak
    2017-03-26S2