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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

The neural architecture of language: Integrative modeling converges on predictive processing
Martin Schrimpf, Idan Blank, Greta Tuckute, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 45
Open Access | Times Cited: 359

Showing 1-25 of 359 citing articles:

Over-reliance on English hinders cognitive science
Damián E. Blasí, Joseph Henrich, Evangelia Adamou, et al.
Trends in Cognitive Sciences (2022) Vol. 26, Iss. 12, pp. 1153-1170
Open Access | Times Cited: 229
A hierarchy of linguistic predictions during natural language comprehension
Micha Heilbron, Kristijan Armeni, Jan‐Mathijs Schoffelen, et al.
Proceedings of the National Academy of Sciences (2022) Vol. 119, Iss. 32
Open Access | Times Cited: 223
Semantic reconstruction of continuous language from non-invasive brain recordings
Jerry Tang, Amanda LeBel, Shailee Jain, et al.
Nature Neuroscience (2023) Vol. 26, Iss. 5, pp. 858-866
Open Access | Times Cited: 200
Brains and algorithms partially converge in natural language processing
Charlotte Caucheteux, Jean-Rémi King
Communications Biology (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 198
An investigation across 45 languages and 12 language families reveals a universal language network
Saima Malik-Moraleda, Dima Ayyash, Jeanne Gallée, et al.
Nature Neuroscience (2022) Vol. 25, Iss. 8, pp. 1014-1019
Open Access | Times Cited: 184
Evidence of a predictive coding hierarchy in the human brain listening to speech
Charlotte Caucheteux, Alexandre Gramfort, Jean-Rémi King
Nature Human Behaviour (2023) Vol. 7, Iss. 3, pp. 430-441
Open Access | Times Cited: 144
Symbols and mental programs: a hypothesis about human singularity
Stanislas Dehaene, Fosca Al Roumi, Yair Lakretz, et al.
Trends in Cognitive Sciences (2022) Vol. 26, Iss. 9, pp. 751-766
Open Access | Times Cited: 122
Dissociating language and thought in large language models
Kyle Mahowald, Anna A. Ivanova, Idan Blank, et al.
Trends in Cognitive Sciences (2024) Vol. 28, Iss. 6, pp. 517-540
Open Access | Times Cited: 112
Using artificial neural networks to ask ‘why’ questions of minds and brains
Nancy Kanwisher, Meenakshi Khosla, Katharina Dobs
Trends in Neurosciences (2023) Vol. 46, Iss. 3, pp. 240-254
Open Access | Times Cited: 108
Deep problems with neural network models of human vision
Jeffrey S. Bowers, Gaurav Malhotra, Marin Dujmović, et al.
Behavioral and Brain Sciences (2022) Vol. 46
Open Access | Times Cited: 106
Probabilistic atlas for the language network based on precision fMRI data from >800 individuals
Benjamin Lipkin, Greta Tuckute, Josef Affourtit, et al.
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 88
High-resolution image reconstruction with latent diffusion models from human brain activity
Yu Takagi, Shinji Nishimoto
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023), pp. 14453-14463
Open Access | Times Cited: 88
Can language models learn from explanations in context?
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, et al.
(2022)
Open Access | Times Cited: 85
The language network as a natural kind within the broader landscape of the human brain
Evelina Fedorenko, Anna A. Ivanova, Tamar I. Regev
Nature reviews. Neuroscience (2024) Vol. 25, Iss. 5, pp. 289-312
Closed Access | Times Cited: 66
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?
Byung-Doh Oh, William Schuler
Transactions of the Association for Computational Linguistics (2023) Vol. 11, pp. 336-350
Open Access | Times Cited: 52
Do Large Language Models Know What Humans Know?
Sean Trott, Cameron R. Jones, Tyler H. Chang, et al.
Cognitive Science (2023) Vol. 47, Iss. 7
Open Access | Times Cited: 50
Symbols and grounding in large language models
Ellie Pavlick
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2023) Vol. 381, Iss. 2251
Open Access | Times Cited: 49
Does ChatGPT resemble humans in language use?
Zhenguang G. Cai, David A. Haslett, Xufeng Duan, et al.
(2023)
Open Access | Times Cited: 41
Language is primarily a tool for communication rather than thought
Evelina Fedorenko, Steven T. Piantadosi, Edward Gibson
Nature (2024) Vol. 630, Iss. 8017, pp. 575-586
Closed Access | Times Cited: 33
Driving and suppressing the human language network using large language models
Greta Tuckute, Aalok Sathe, Shashank Srikant, et al.
Nature Human Behaviour (2024) Vol. 8, Iss. 3, pp. 544-561
Open Access | Times Cited: 30
Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns
Ariel Goldstein, Avigail Grinstein-Dabush, Mariano Schain, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 23
Large-scale evidence for logarithmic effects of word predictability on reading time
Cory Shain, Clara Meister, Tiago Pimentel, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 10
Open Access | Times Cited: 22
Data science opportunities of large language models for neuroscience and biomedicine
Danilo Bzdok, Andrew Thieme, Oleksiy Levkovskyy, et al.
Neuron (2024) Vol. 112, Iss. 5, pp. 698-717
Open Access | Times Cited: 17
Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training
Eghbal A. Hosseini, Martin Schrimpf, Yian Zhang, et al.
Neurobiology of Language (2024) Vol. 5, Iss. 1, pp. 43-63
Open Access | Times Cited: 16
Large language models can segment narrative events similarly to humans
Sebastian Michelmann, M. Kumar, Kenneth A. Norman, et al.
Behavior Research Methods (2025) Vol. 57, Iss. 1
Open Access | Times Cited: 3
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