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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:

Performance vs. competence in human–machine comparisons
Chaz Firestone
Proceedings of the National Academy of Sciences (2020) Vol. 117, Iss. 43, pp. 26562-26571
Open Access | Times Cited: 125

Showing 1-25 of 125 citing articles:

Deepfake detection by human crowds, machines, and machine-informed crowds
Matthew Groh, Ziv Epstein, Chaz Firestone, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 119, Iss. 1
Open Access | Times Cited: 148
The neuroconnectionist research programme
Adrien Doerig, Rowan P. Sommers, Katja Seeliger, et al.
Nature reviews. Neuroscience (2023) Vol. 24, Iss. 7, pp. 431-450
Open Access | Times Cited: 133
The Fallacy of AI Functionality
Inioluwa Deborah Raji, I. Elizabeth Kumar, Aaron Horowitz, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2022), pp. 959-972
Open Access | Times Cited: 122
The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences
Jake Quilty‐Dunn, Nicolas Porot, Eric Mandelbaum
Behavioral and Brain Sciences (2022) Vol. 46
Open Access | Times Cited: 121
Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging
Andrés Anaya-Isaza, Leonel Mera-Jiménez
IEEE Access (2022) Vol. 10, pp. 23217-23233
Open Access | Times Cited: 87
On Logical Inference over Brains, Behaviour, and Artificial Neural Networks
Olivia Guest, Andrea E. Martin
Computational Brain & Behavior (2023) Vol. 6, Iss. 2, pp. 213-227
Open Access | Times Cited: 57
A Survey of Adversarial Defenses and Robustness in NLP
Shreya Goyal, Sumanth Doddapaneni, Mitesh M. Khapra, et al.
ACM Computing Surveys (2023) Vol. 55, Iss. 14s, pp. 1-39
Closed Access | Times Cited: 56
Testing theory of mind in large language models and humans
James W. A. Strachan, Dalila Albergo, Giulia Borghini, et al.
Nature Human Behaviour (2024) Vol. 8, Iss. 7, pp. 1285-1295
Open Access | Times Cited: 55
AI Psychometrics: Assessing the Psychological Profiles of Large Language Models Through Psychometric Inventories
Max Pellert, Clemens M. Lechner, Claudia Wagner, et al.
Perspectives on Psychological Science (2024) Vol. 19, Iss. 5, pp. 808-826
Open Access | Times Cited: 26
Virtual humans as social actors: Investigating user perceptions of virtual humans’ emotional expression on social media
Jeongmin Ham, Sitan Li, Jiemin Looi, et al.
Computers in Human Behavior (2024) Vol. 155, pp. 108161-108161
Closed Access | Times Cited: 24
Enhancing the imitation game: a trust-based model for distinguishing human and machine participants
Tanisha Gupta, Akarsh Tripathi, Ashutosh Kumar Dubey, et al.
Applied Intelligence (2025) Vol. 55, Iss. 6
Closed Access | Times Cited: 2
AI and the Future of Skills, Volume 1

Educational research and innovation (2021)
Open Access | Times Cited: 97
The Perception of Relations
Alon Hafri, Chaz Firestone
Trends in Cognitive Sciences (2021) Vol. 25, Iss. 6, pp. 475-492
Open Access | Times Cited: 82
Deep Nets: What have They Ever Done for Vision?
Alan Yuille, Chenxi Liu
International Journal of Computer Vision (2020) Vol. 129, Iss. 3, pp. 781-802
Closed Access | Times Cited: 72
Five points to check when comparing visual perception in humans and machines
Christina M. Funke, Judy Borowski, Karolina Stosio, et al.
Journal of Vision (2021) Vol. 21, Iss. 3, pp. 16-16
Open Access | Times Cited: 68
A framework for rigorous evaluation of human performance in human and machine learning comparison studies
Hannah Cowley, Mandy Natter, Karla Gray-Roncal, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 50
The signature-testing approach to mapping biological and artificial intelligences
Alex H. Taylor, Amalia P. M. Bastos, Rachael L. Brown, et al.
Trends in Cognitive Sciences (2022) Vol. 26, Iss. 9, pp. 738-750
Closed Access | Times Cited: 40
Artificial intelligence in government: Concepts, standards, and a unified framework
Vincent J. Straub, Deborah Morgan, Jonathan Bright, et al.
Government Information Quarterly (2023) Vol. 40, Iss. 4, pp. 101881-101881
Open Access | Times Cited: 34
Fairness Perceptions of Artificial Intelligence: A Review and Path Forward
Devesh Narayanan, Mahak Nagpal, Jack McGuire, et al.
International Journal of Human-Computer Interaction (2023) Vol. 40, Iss. 1, pp. 4-23
Closed Access | Times Cited: 29
Machine-learning-assisted classification of construction and demolition waste fragments using computer vision: Convolution versus extraction of selected features
Václav Nežerka, Tomáš Zbíral, Jan Trejbal
Expert Systems with Applications (2023) Vol. 238, pp. 121568-121568
Closed Access | Times Cited: 24
Embers of autoregression show how large language models are shaped by the problem they are trained to solve
R. Thomas McCoy, Shunyu Yao, Dan Friedman, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 41
Open Access | Times Cited: 16
(Ir)rationality and cognitive biases in large language models
Olivia Macmillan-Scott, Mirco Musolesi
Royal Society Open Science (2024) Vol. 11, Iss. 6
Open Access | Times Cited: 9
Comparing Object Recognition in Humans and Deep Convolutional Neural Networks—An Eye Tracking Study
Leonard E. van Dyck, Roland Kwitt, Sebastian Jochen Denzler, et al.
Frontiers in Neuroscience (2021) Vol. 15
Open Access | Times Cited: 48
Comparative Analysis of Human Operators and Advanced Technologies in the Visual Inspection of Aero Engine Blades
Jonas Aust, Dirk J. Pons
Applied Sciences (2022) Vol. 12, Iss. 4, pp. 2250-2250
Open Access | Times Cited: 34
Prompting is not a substitute for probability measurements in large language models
Jennifer Hu, Roger Lévy
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2023)
Open Access | Times Cited: 17
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