基于神经心理学的大语言模型认知能力评估

Computer Science > Artificial Intelligence arXiv:2603.02540 (cs) [Submitted on 3 Mar 2026 (v1), last revised 17 Sep 2026 (this version, v2)] Title:A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities Authors:Faiz Ghifari Haznitrama, Faeyza Rishad Ardi, Alice Oh View a PDF of the paper titled A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities, by Faiz Ghifari Haznitrama and 1 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) display a unified "general factor" of capability across 10 benchmarks (a finding confirmed by our factor analysis of 156 models), yet they still struggle with simple, trivial tasks for humans. This is because current benchmarks focus on task completion, failing to probe the foundational cognitive abilities that highlight these behaviors. We address this by introducing the NeuroCognition benchmark, grounded in three adapted neuropsychological tests targeting distinct foundational cognitive components: Raven's Progressive Matrices (abstract relational reasoning), Spatial Working Memory (goal-directed spatial updating), and the Wisconsin Card Sorting Test (cognitive flexibility). Our evaluation reveals that while models perform strongly on text, their performance degrades for images and with increased complexity. Comparison with a human baseline shows that LLMs and humans fail at different parts of the same tasks. Furthermore, we observe that complex reasoning is not universally beneficial, whereas simple, human-like strategies yield partial gains. We also find that NeuroCognition correlates positively with standard general-capability benchmarks, while still measuring distinct cognitive abilities beyond them. Overall, NeuroCognition emphasizes where current LLMs align with human-like intelligence and where they lack core adaptive cognition, showing the potential to serve as a verifiable, scalable source for improving LLMs. Comments: Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.02540 [cs.AI] (or arXiv:2603.02540v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.02540 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Faiz Ghifari Haznitrama [view email] [v1] Tue, 3 Mar 2026 02:54:58 UTC (198 KB) [v2] Thu, 17 Sep 2026 07:47:17 UTC (589 KB) Full-text links: Access Paper: View a PDF of the paper titled A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities, by Faiz Ghifari Haznitrama and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)