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title: "Psycholinguistics & language acquisition — papers, page 2"
description: "Psycholinguistics & language acquisition: 62 papers of the Elamur Research library · page 2 of 2 · 26–62, the most cited first."
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# 🧒 Psycholinguistics & language acquisition

62 papers · page 2 of 2 · 26–62 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/)

How people read, hear and learn language, and models that behave like them.

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26. [**A Survey of Idiom Datasets for Psycholinguistic and Computational Research**Michael Flor, Xinyi Liu, Anna Feldman*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/flor-2025-survey-idiom-datasets-psycholinguistic-computational/)
27. [**UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions**Xiulin Yang, Zhuoxuan Ju, Lanni Bu, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-ud-english-childes-collected-resource/)
28. [**Adapting Psycholinguistic Research for LLMs: Gender-inclusive Language in a Coreference Context**Marion Bartl, Thomas Brendan Murphy, Susan Leavy*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/bartl-2025-adapting-psycholinguistic-research-llms-gender/)
29. [**TafBERTa: Learning Grammatical Rules from Small-Scale Language Acquisition Data in Hebrew**Anita Gelboim, Elior Sulem*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/gelboim-2025-tafberta-learning-grammatical-rules-small/)
30. [**What did you say? Generating Child-Directed Speech Questions to Train LLMs**Whitney Poh, Michael Tombolini, Libby Barak*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/poh-2025-what-did-you-say-generating/)
31. [**A Language-agnostic Model of Child Language Acquisition**Louis Mahon, Omri Abend, Uri Berger, et al.*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/mahon-2024-language-agnostic-model-child-language/)
32. [**Recurrent Networks Are (Linguistically) Better? An (Ongoing) Experiment on Small-LM Training on Child-Directed Speech in Italian**Achille Fusco, Matilde Barbini, Maria Letizia Piccini Bianchessi, et al.*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/fusco-2024-recurrent-networks-are-linguistically-better/)
33. [**Are idioms surprising?**J. Nathanael Philipp, Michael Richter, Erik Daas, et al.*2023 · 3 citations*](https://md.research.elamur.ai/free/en/papers/philipp-2023-are-idioms-surprising/)
34. [**Measuring Language Development in Early Childhood Education: A Case Study of Grammar Checking in Child Language Transcripts**Khairun-nisa Hassanali, Yang Liu*2011 · 3 citations*](https://md.research.elamur.ai/free/en/papers/hassanali-2011-measuring-language-development-early-childhood/)
35. [**Using Curriculum Masking Based on Child Language Development to Train a Large Language Model with Limited Training Data**Evan Lucas, Dylan Gaines, Tagore Rao Kosireddy, et al.*2024 · 2 citations*](https://md.research.elamur.ai/free/en/papers/lucas-2024-curriculum-masking-based-child-language/)
36. [**Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech**Ida Szubert, Omri Abend, Nathan Schneider, et al.*2021 · 2 citations*](https://md.research.elamur.ai/free/en/papers/szubert-2021-cross-linguistically-consistent-semantic-syntactic/)
37. [**Surprisal and Satisfaction: Towards an Information-theoretic Characterization of Presuppositions with a Diachronic Application**Remus Gergel, Martin Kopf-Giammanco, Julia Masloh*2017 · 2 citations*](https://md.research.elamur.ai/free/en/papers/gergel-2017-surprisal-satisfaction-information-theoretic-characterization/)
38. [**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/)
39. [**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/)
40. [**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/)
41. [**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/)
42. [**Probing for Reading Times**Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tsipidi-2026-probing-reading-times/)
43. [**Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic Datasets**Omar Momen, Emilie Sitter, Berenike Herrmann, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/momen-2026-surprisal-metaphor-novelty-judgments-moderate/)
44. [**What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap Dependencies**Zhenghao Zhou, William Dai, Maya Viswanathan, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zhou-2026-what-exactly-do-children-receive/)
45. [**Batch-wise Convergent Pre-training: Step-by-Step Learning Inspired by Child Language Development**Ko Yoshida, Daiki Shiono, Kai Sato, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yoshida-2025-batch-wise-convergent-pre-training/)
46. [**Universal Dependencies and Semantics for English and Hebrew Child-directed Speech**Ida Szubert, Omri Abend, Nathan Schneider, et al.*2022 · 1 citation*](https://md.research.elamur.ai/free/en/papers/szubert-2022-universal-dependencies-semantics-english-hebrew/)
47. [**The Importance of Category Labels in Grammar Induction with Child-directed Utterances**Lifeng Jin, William Schuler*2020 · 1 citation*](https://md.research.elamur.ai/free/en/papers/jin-2020-importance-category-labels-grammar-induction/)
48. [**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/)
49. [**Big Five Personality Prediction through Emotion-Conditioned Representations and Learnable Psycholinguistic Mapping**Lorenzo Zangari, Antonin Schnyder, Davide Picca*2026*](https://md.research.elamur.ai/free/en/papers/zangari-2026-big-five-personality-prediction-through/)
50. [**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/)
51. [**Child-directed speech facilitates production, not comprehension, in BabyLMs**Bastian Bunzeck, Sina Zarrieß*2026*](https://md.research.elamur.ai/free/en/papers/bunzeck-2026-child-directed-speech-facilitates-production/)
52. [**From CHAT to Coded CoNLL-U: A Reproducible Pipeline for the Syntactic Annotation and Querying of Child Language Data**Achim Stein*2026*](https://md.research.elamur.ai/free/en/papers/stein-2026-chat-coded-conll-u-reproducible/)
53. [**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/)
54. [**Pragmatic Modelling in Language Learning: Caregiver Question-Answer Feedback in Child-Directed Dialogue**Maryam Bala, Johannes Heim, Elspeth Edelstein, et al.*2026*](https://md.research.elamur.ai/free/en/papers/bala-2026-pragmatic-modelling-language-learning-caregiver/)
55. [**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/)
56. [**Reading Time in the Wild: An Assessment of Readability Predictors Based on Naturally-Observed Reading Times**Sijbren van Vaals, Rik van Noord, Malvina Nissim*2026*](https://md.research.elamur.ai/free/en/papers/vaals-2026-reading-time-wild-assessment-readability/)
57. [**Semantic Information: A Difference That Makes a Difference**J. Nathanael Philipp, Max Kölbl, Michael Richter*2026*](https://md.research.elamur.ai/free/en/papers/philipp-2026-semantic-information-difference-that-makes/)
58. [**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/)
59. [**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/)
60. [**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/)
61. [**What Kind of Language is Easy to Language-Model Under Curriculum Learning?**Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe*2026*](https://md.research.elamur.ai/free/en/papers/naggar-2026-what-kind-language-is-easy/)
62. [**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/)

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