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title: "Neuroscience — papers, page 2"
description: "Neuroscience: 79 papers of the Elamur Research library · page 2 of 2 · 26–79, the most cited first — our summary of each, its ideas, references and who…"
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# 🧠 Neuroscience

79 papers · page 2 of 2 · 26–79 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

The brain and the nervous system, from molecules to cognition.

## Papers

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26. [**Causal Interventions Reveal Shared Structure Across English Filler–Gap Constructions**Sasha Boguraev, Christopher Potts, Kyle Mahowald*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/boguraev-2025-causal-interventions-reveal-shared-structure/)
27. [**The Impact of Token Granularity on the Predictive Power of Language Model Surprisal**Byung-Doh Oh, William Schuler*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/oh-2025-impact-token-granularity-predictive-power/)
28. [**Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models**Giulia Rambelli, Emmanuele Chersoni, Marco S. G. Senaldi, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/rambelli-2023-are-frequent-phrases-directly-retrieved/)
29. [**How Are Idioms Processed Inside Transformer Language Models?**Ye Tian, Isobel James, Hye Son*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-how-are-idioms-processed-inside/)
30. [**Functional structure in the noun phrase: revisiting Hebrew nominals**Omer Preminger*2020 · 10 citations*](https://md.research.elamur.ai/free/en/papers/preminger-2020-functional-structure-noun-phrase-revisiting/)
31. [**Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition**Masato Mita, Ryo Yoshida, Yohei Oseki*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/mita-2025-developmentally-plausible-working-memory-shapes/)
32. [**Can language models capture syntactic associations without surface cues? A case study of reflexive anaphor licensing in English control constructions**Soo-Hwan Lee, Sebastian Schuster*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/lee-2022-can-language-models-capture-syntactic/)
33. [**Anaphoricity in Connectives: A Case Study on German**Manfred Stede, Yulia Grishina*2016 · 9 citations*](https://md.research.elamur.ai/free/en/papers/stede-2016-anaphoricity-connectives-case-study-german/)
34. [**Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability**Xufeng Duan, Xinyu Zhou, Bei Xiao, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/duan-2025-unveiling-language-competence-neurons-psycholinguistic/)
35. [**Differentiating dislocations, topicalisation, and extraposition in Biblical Hebrew: Evidence from negation**Cynthia L. Miller-Naudé, Jacobus A. Naudé*2019 · 7 citations*](https://md.research.elamur.ai/free/en/papers/miller-naude-2019-differentiating-dislocations-topicalisation-extraposition-biblical/)
36. [**The theory and syntactic representation of control structures: An analysis from Amharic**Tommi Leung, Girma Halefom*2017 · 5 citations*](https://md.research.elamur.ai/free/en/papers/leung-2017-theory-syntactic-representation-control-structures/)
37. [**The head the construct: Construct state nominals as a novel window to syntactic movement difficulties in hearing impairment**Naama Friedmann, Ronit Szterman, Adriana Belletti, et al.*2018 · 3 citations*](https://md.research.elamur.ai/free/en/papers/friedmann-2018-head-construct-construct-state-nominals/)
38. [**Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task**Mengyang Qiu, Zoe Brisebois, Siena Sun*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2026-can-llms-simulate-human-behavioral/)
39. [**ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters**Hazem Darwish, Abdalrahman Al Malah, Khloud Al Jallad, et al.*2024 · 2 citations*](https://md.research.elamur.ai/free/en/papers/darwish-2024-areeg-chars-dataset-envisioned-speech/)
40. [**Conjunct Lengths in English, Dependency Length Minimization, and Dependency Structure of Coordination**Adam Przepiórkowski, Michał Woźniak*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/przepiorkowski-2023-conjunct-lengths-english-dependency-length/)
41. [**On Quick Kisses and How to Make Them Count: A Study on Event Construal in Light Verb Constructions with BERT**Chenxin Liu, Emmanuele Chersoni*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-quick-kisses-how-make-them/)
42. [**A Corpus of Joint EEG and Self-Paced Reading of Natural Dutch Texts**Sara Møller Østergaard, Lenneke Doris Lichtenberg, Laura Boon, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ostergaard-2026-corpus-joint-eeg-self-paced/)
43. [**An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal**Ryo Yoshida, Shinnosuke Isono, Taiga Someya, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yoshida-2026-existence-proof-neural-language-models/)
44. [**Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal**Sathvik Nair, Byung-Doh Oh*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nair-2026-clozing-gap-exploring-why-language/)
45. [**Digital Harf: A Clinically Integrated Multimodal AI System for Pervasive Arabic Speech and Language Therapy**Asif Azad, Mohammad Sadat Hossain, MD Sadik Hossain Shanto, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/azad-2026-digital-harf-clinically-integrated-multimodal/)
46. [**Human-like Fleeting Memory Improves Language Learning but Impairs Reading Time Prediction in Transformer Language Models**Abishek Thamma, Micha Heilbron*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/thamma-2026-human-like-fleeting-memory-improves/)
47. [**Reading Dynamics and Comprehension in Cognitive Aging: A Multimodal Language Resource**Claudia Marzi, Noemi Boni, Alice Todesco, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/marzi-2026-reading-dynamics-comprehension-cognitive-aging/)
48. [**Assessing the influence of attractor-verb distance on grammatical agreement in humans and language models**Christos Zacharopoulos, Théo Desbordes, Mathias Sablé-Meyer*2023 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zacharopoulos-2023-assessing-influence-attractor-verb-distance/)
49. [**Morphosyntactic aspects of agrammatism in Palestinian Arabic: Findings from a Semitic language**Hisham Adam*2023 · 1 citation*](https://md.research.elamur.ai/free/en/papers/adam-2023-morphosyntactic-aspects-agrammatism-palestinian-arabic/)
50. [**Thematic role assignment in non-default verb classes: A cross-linguistic comparison of English and German**Louise Kyriaki, Matthias Schlesewsky, Ina Bornkessel-Schlesewsky*2021 · 1 citation*](https://md.research.elamur.ai/free/en/papers/kyriaki-2021-thematic-role-assignment-non-default/)
51. [**A Feature-Driven Tensor Semantics for Minimalist Grammars**John Paulson, Aniello De Santo, Jonathan Rawski*2026*](https://md.research.elamur.ai/free/en/papers/paulson-2026-feature-driven-tensor-semantics-minimalist/)
52. [**A Transparent Model of Syntactic and Semantic Cue-based Retrieval**Shisen Yue, John T. Hale*2026*](https://md.research.elamur.ai/free/en/papers/yue-2026-transparent-model-syntactic-semantic-cue/)
53. [**Aggregated Transformer Attention Measures Predict Reading Times Beyond Surprisal**Lukas Mielczarek, Laura Kallmeyer*2026*](https://md.research.elamur.ai/free/en/papers/mielczarek-2026-aggregated-transformer-attention-measures-predict/)
54. [**Causal Inferences Are Driven by Noun Concept Specificity Evidence from Self-Paced Reading and Large Language Model Surprisal**Fabian Schlotterbeck, Raphael Barth*2026*](https://md.research.elamur.ai/free/en/papers/schlotterbeck-2026-causal-inferences-are-driven-noun/)
55. [**Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks**Claire Hobbs, R. Thomas McCoy*2026*](https://md.research.elamur.ai/free/en/papers/hobbs-2026-collocational-bootstrapping-hypothesis-about-learning/)
56. [**Determinants of Hesitations and Repetitions in Hindi Spontaneous Speech**Eashani Sharma, Ishita Arun, Samar Husain*2026*](https://md.research.elamur.ai/free/en/papers/sharma-2026-determinants-hesitations-repetitions-hindi-spontaneous/)
57. [**Is Cross-Lingual Transfer in Bilingual Models Human-Like? A Study with Overlapping Word Forms in Dutch and English**Iza Škrjanec, Irene Elisabeth Winther, Vera Demberg, et al.*2026*](https://md.research.elamur.ai/free/en/papers/skrjanec-2026-is-cross-lingual-transfer-bilingual/)
58. [**Modeling semantic association in self-paced reading with language model embeddings**Sara Møller Østergaard, Kenneth Enevoldsen, Afra Alishahi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ostergaard-2026-modeling-semantic-association-self-paced/)
59. [**Modeling the Memory-Surprisal Trade-Off over Time: Communicative Efficiency Decreases with Lexico-Grammatical Change in Scientific English**Julius Steuer, Marie-Pauline Krielke, Stefania Degaetano-Ortlieb, et al.*2026*](https://md.research.elamur.ai/free/en/papers/steuer-2026-modeling-memory-surprisal-trade-off/)
60. [**Non-literal Meaning Representation in the Brain during Naturalistic Listening**Zhengwu Ma, Yuhan Huang, Chengcheng Wang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ma-2026-non-literal-meaning-representation-brain/)
61. [**Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT Models**Gyu-Ho Shin, Soo-Hwan Lee, Chanyoung Lee*2026*](https://md.research.elamur.ai/free/en/papers/shin-2026-open-access-dataset-acceptability-ratings/)
62. [**Probing the Attention Representation of Filler-Gap Dependency in Transformers**Ruoqing Yao, Pranav Anand*2026*](https://md.research.elamur.ai/free/en/papers/yao-2026-probing-attention-representation-filler-gap/)
63. [**Quantifying the cross-linguistic effects of syncretism on agreement attraction**Utku Turk, Eva Neu*2026*](https://md.research.elamur.ai/free/en/papers/turk-2026-quantifying-cross-linguistic-effects-syncretism/)
64. [**Similar Predictions, Different Processes: A Multi-Level Comparison of Human and Multimodal LLM Language Prediction**Shuqi Wang, Zhenguang Cai*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-similar-predictions-different-processes-multi/)
65. [**Subject to change: Agreement and case**Nofar Rimon, Tal Siloni*2026*](https://md.research.elamur.ai/free/en/papers/rimon-2026-subject-change-agreement-case/)
66. [**The Frequency Confound in Language-Model Surprisal and Metaphor Novelty**Omar Momen, Sina Zarrieß*2026*](https://md.research.elamur.ai/free/en/papers/momen-2026-frequency-confound-language-model-surprisal/)
67. [**The Sensorimotor Norms for the Chinese Classifiers**Yimei Shao, Yu-Yin Hsu, Chu-Ren Huang*2026*](https://md.research.elamur.ai/free/en/papers/shao-2026-sensorimotor-norms-chinese-classifiers/)
68. [**Topic-context Dependency on Continuous Semantic Reconstruction of Language from fMRI Signals**Fermin Travi, Agustin Delmagro, Diego Fernandez Slezak, et al.*2026*](https://md.research.elamur.ai/free/en/papers/travi-2026-topic-context-dependency-continuous-semantic/)
69. [**Toward Cognitive Alignment in Large Language Models: Integrating Linguistic Theory and Human Data**Wajdi Zaghouani*2026*](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-cognitive-alignment-large-language-models/)
70. [**Transformer Attention as a Unified Model of Encoding and Retrieval in Human Sentence Processing**Dan Parker*2026*](https://md.research.elamur.ai/free/en/papers/parker-2026-transformer-attention-as-unified-model/)
71. [**What Brain Data Adds to Language Model Training**Gabriele Merlin, Omer Moussa, Mariya Toneva*2026*](https://md.research.elamur.ai/free/en/papers/merlin-2026-what-brain-data-adds-language/)
72. [**Word predictability estimates from language models are not robust to tokenizer vocabulary**Kien Nguyen, Suhas Arehalli*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-word-predictability-estimates-language-models/)
73. [**A Phase-Theoretic Approach to Serial Verb Constructions in Semitic**Mansour Ibrahim Altamimi, Jacob Willson*2025*](https://md.research.elamur.ai/free/en/papers/altamimi-2025-phase-theoretic-approach-serial-verb/)
74. [**Uniform Information Density and Syntactic Reduction: Revisiting that-Mentioning in English Complement Clauses**Hailin Hao, Elsi Kaiser*2025*](https://md.research.elamur.ai/free/en/papers/hao-2025-uniform-information-density-syntactic-reduction-revisiting/)
75. [**ArEEG_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words**Hazem Darwish, Abdalrahman Al Malah, Khloud Al Jallad, et al.*2024*](https://md.research.elamur.ai/free/en/papers/darwish-2024-areeg-words-dataset-envisioned-speech/)
76. [**A Paradigm Change for Formal Syntax: Computational Algorithms in the Grammar of English**Anat Ninio*2022*](https://md.research.elamur.ai/free/en/papers/ninio-2022-paradigm-change-formal-syntax-computational/)
77. [**Frozen by context: Pragmatic factors of syntactic freezing**Gouming Martens*2021*](https://md.research.elamur.ai/free/en/papers/martens-2021-frozen-context-pragmatic-factors-syntactic/)
78. [**Predicate Catenae: A Dependency Grammar Analysis of It-Clefts**Timothy Osborne*2019*](https://md.research.elamur.ai/free/en/papers/osborne-2019-predicate-catenae-dependency-grammar-analysis/)
79. [**The spontaneous use of Hebrew verb forms by Israeli preschool children with and without sli The spontaneous use of Hebrew verb forms by Israeli preschool children with and without sli**Esther Dromi, Anat Blass*2008*](https://md.research.elamur.ai/free/en/papers/dromi-2008-spontaneous-use-hebrew-verb-forms/)

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