--- title: "Cognitive Plausibility in Natural Language Processing (2024)" description: "This item is a book review, not an original study. 4 references, cited by 12 papers here. Among them: 0." authors: ["Yevgen Matusevych"] year: 2024 venue: "Computational Linguistics" source: "https://aclanthology.org/2024.cl-3.6/" doi: "10.1162/coli_r_00517" licence: "CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)" licence_url: "https://creativecommons.org/licenses/by-nc-nd/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/matusevych-2024-cognitive-plausibility-natural-language-processing/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/matusevych-2024-cognitive-plausibility-natural-language-processing/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/11/) / Cognitive Plausibility in Natural Language Processing # Cognitive Plausibility in Natural Language Processing **Authors:** [Yevgen Matusevych](https://md.research.elamur.ai/free/en/authors/yevgen-matusevych/) · **Year:** 2024 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2024](https://md.research.elamur.ai/free/en/venues/cl/2024/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)](https://creativecommons.org/licenses/by-nc-nd/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2024.cl-3.6/) · [PDF](https://aclanthology.org/2024.cl-3.6.pdf) · [DOI 10.1162/coli\_r\_00517](https://doi.org/10.1162/coli_r_00517) · [OpenAlex](https://openalex.org/W4393162708) **Citations:** 0 citations (OpenAlex, 2026-10-14) · [12](https://md.research.elamur.ai/free/en/papers/matusevych-2024-cognitive-plausibility-natural-language-processing/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary This item is a book review, not an original study. Yevgen Matusevych (University of Groningen) evaluates *Cognitive Plausibility in Natural Language Processing* by Lisa Beinborn and Nora Hollenstein (Springer, Synthesis Lectures on Human Language Technologies, 2024). The book asks how closely modern NLP systems resemble human language processing. This criterion is usually neglected in favour of accuracy, fairness or efficiency. The authors treat plausibility as graded and multidimensional and study three aspects: whether models and people make matching decisions, use comparable internal representations, and follow similar processing strategies. They draw on behavioral and neural datasets of language comprehension, though not production. They also survey interpretability tools such as test suites, probing classifiers, attention analysis and mapping to brain responses. They find no universal recipe but propose concrete directions: difficulty-aware evaluation, context-sensitive representation analysis, multimodal input and genuinely multilingual design. The reviewer praises the gradual, accessible structure and the ethics section closing each chapter. He highlights the lesson that human data is noisy and graded, and suggests NLP should learn to accommodate this. ## Key points - The book has seven chapters. Chapters 2–3 introduce language modeling basics and cognitive data sources (behavioral and brain signals). Chapters 4–6 cover behavioral patterns, representational structure and procedural strategies. - Curriculum learning is presented as promising, but in the 2023 BabyLM challenge it brought only modest gains (Warstadt et al. 2023). - Multi-head attention in transformers is judged cognitively implausible. However, attention weights can still be compared with human data such as reading gaze patterns. - The authors call for evaluation that is finer-grained and less English-centric. They also propose integrating human problem-solving strategies through multi-task and transfer learning. - The review appeared in Computational Linguistics 50(3), 2024 (doi:10.1162/coli_r_00517) under a CC BY-NC-ND 4.0 license. The book's DOI is 10.1007/978-3-031-43260-6. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [University of Groningen](https://md.research.elamur.ai/free/en/universities/university-of-groningen/) — University, Groningen, Netherlands · 74 works · 5,142 citations 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [🧠 Neuroscience](https://md.research.elamur.ai/free/en/shelves/neuroscience/) · 186 works · [🧠 Cognitive Neuroscience](https://md.research.elamur.ai/free/en/shelves/neuroscience/cognitive-neuroscience/) · 185 works ## Cite Cite as: Yevgen Matusevych. 2024. Cognitive Plausibility in Natural Language Processing. Computational Linguistics. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @article{matusevych2024cognitive, title = {{Cognitive Plausibility in Natural Language Processing}}, author = {Yevgen Matusevych}, journal = {Computational Linguistics}, year = {2024}, doi = {10.1162/coli\_r\_00517}, url = {https://doi.org/10.1162/coli_r_00517} } ``` - [← previous Chatting with Bots: AI, Speech Acts, and the Edge of Assertion](https://md.research.elamur.ai/free/en/papers/williams-2024-chatting-bots-ai-speech-acts/) - [next → Discourse Relation Prediction and Discourse Parsing in Dialogues with Minimal Supervision](https://md.research.elamur.ai/free/en/papers/li-2024-discourse-relation-prediction-discourse-parsing/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/11/) ## References 4 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Baan, Joris, Nico Daheim, Evgenia Ilia, Dennis Ulmer, Haau-Sing Li, Raquel Fernández, Barbara Plank, Rico Sennrich, Chrysoula Zerva, and Wilker Aziz. 2023. Uncertainty in natural language generation: From theory to applications. arXiv preprint arXiv:2307.15703. — [arXiv:2307.15703](https://arxiv.org/abs/2307.15703) [find](https://scholar.google.com/scholar?q=Baan%2C+Joris%2C+Nico+Daheim%2C+Evgenia+Ilia%2C+Dennis+Ulmer%2C+Haau%2DSing+Li%2C+Raquel+Ferna%CC%81ndez%2C+Barbara+Plank%2C+Rico+Sennrich%2C+Chrysoula+Zerva%2C+and+Wilker+Aziz%2E+2023%2E+Uncertainty+in+natural+language+generation%3A) 2. Liu, Alisa, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah Smith, and Yejin Choi. 2023. [We’re afraid language models aren’t modeling ambiguity](https://md.research.elamur.ai/free/en/papers/liu-2023-we-re-afraid-language-models/). In Proceedings of the 2023 3. Conference on Empirical Methods in Natural Language Processing, pages 790–807. Warstadt, Alex, Aaron Mueller, Leshem Choshen, Ethan Wilcox, Chengxu Zhuang, Juan Ciro, Rafael Mosquera, Bhargavi Paranjabe, Adina Williams, Tal Linzen, and Ryan Cotterell. 2023. Findings of the BabyLM Challenge: Sample-e… [find](https://scholar.google.com/scholar?q=Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+790%E2%80%93807%2E+Warstadt%2C+Alex%2C+Aaron+Mueller%2C+Leshem+Choshen%2C+Ethan+Wilcox%2C+Chengxu+Zhuang%2C+Juan+Ciro%2C+Rafael+Mosquera%2C+Bhargavi+Paranja) 4. Computational Linguistics Volume 50, Number 3 [find](https://scholar.google.com/scholar?q=Computational+Linguistics+Volume+50%2C+Number+3) *Updated 30 October 2026*