--> Computer Science > Artificial Intelligence arXiv:2609.21259 (cs) [Submitted on 18 Sep 2026] Title: CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition Authors: Lance Ying , Jinzhou Wu , Yingshan Susan Wang , Shivam Aarya , Luca M. Schulze Buschoff , Harry Chen , Katherine M. Collins , Andrea de Varda , Shuhao Fu , Sean Dae Houlihan , Akshay K.
Jagadish , Guangyuan Jiang , Samuel Kiegeland , Tetsu Kurumisawa , Rongzhi Liu , Ryan Liu , Ningshan Ma , Kathryn McGregor , Younes Strittmatter , Polina Tsvilodub , Jacob Hoover Vigly , Sarah Wu , Enjie Xu , Yiling Yun , Kelsey Allen , Tyler Brooke-Wilson , Brian Christian , Evelina Fedorenko , Michael C. Frank , Michael Franke , Tao Gao , Samuel J.
Gershman , Robert D. Hawkins , Jennifer Hu , Julian Jara-Ettinger , Max Kleiman-Weiner , Sydney Levine , Tal Linzen , Hongjing Lu , Timothy O Donnell , Desmond C. Ong , Steven T. Piantadosi , Rebecca Saxe , Eric Schulz , Tianmin Shu , Felix A. Sosa , Ilia Sucholutsky , Tan Zhi-Xuan , Tomer Ullman , Fei Xu , Ilker Yildirim , Jian-Qiao Zhu , Thomas L.
Griffiths , Tobias Gerstenberg , Kevin Smith , Joshua B. Tenenbaum View a PDF of the paper titled CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition, by Lance Ying and 55 other authors View PDF HTML (experimental) Abstract: Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science.
As AI systems grow increasingly capable, in what ways do model responses resemble human responses, and where do they systematically diverge? The sheer breadth and diversity of the tasks humans can perform and think about pose a challenge for scalable and rigorous comparison between humans and models. We introduce CogGym, a scalable, unified framework grounded in cognitive science for systematically comparing model and human behavior on matched experimental trials.
CogGym uses a semi-automated, human-in-the-loop pipeline to standardize diverse experimental paradigms into a task-agnostic Experiment Markup Language (EML), enabling reproducible and faithful comparison at scale. For initial release, we curate and standardize 258 cognitive experiments from 100 papers that focuses on human commonsense reasoning, and evaluate 50 large language models against human responses.
We find a clear scaling trend where larger and more recent AI models better reproduce human judgments. Yet AI models improvement on such common reasoning tasks is considerably slower than the gains observed on formal-reasoning benchmarks like math and coding, and model--human fit remains well below human splithalf reliability ($R^2 = 0.
93$ on text, $0.95$ on image, and $0.92$ on video) with the best models achieving $R^2 = 0.59$ on text, $0.58$ on image, and $0.43$ on video experiments. We intend for CogGym to provide a living evaluation framework that continually incorporates new cognitive science experiments to characterize where model behavior resembles human behavior, where it systematically diverges, and how those patterns change as models and experiments evolve.
Comments: Project website -- this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.21259 [cs.AI] (or arXiv:2609.21259v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.21259 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Lance Ying [ view email ] [v1] Fri, 18 Sep 2026 03:18:04 UTC (1,951 KB) Full-text links: Access Paper: View a PDF of the paper titled CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition, by Lance Ying and 55 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.
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