[2606.13607] Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning

-->

Computer Science > Artificial Intelligence

arXiv:2606.13607 (cs)

[Submitted on 11 Jun 2026]

Title:Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning

Authors:Zach Studdiford, Gary Lupyan<br>View a PDF of the paper titled Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning, by Zach Studdiford and Gary Lupyan

View PDF<br>HTML (experimental)

Abstract:When large language models (LLMs) fail to generalize or make haphazard errors in reasoning, it is often taken as evidence that LLMs are not truly reasoning, but rather performing a kind of pattern matching. The implication is that people's behavior does not exhibit the same types of failures because human reasoning uses principled and abstract world models. We evaluate human participants and 25 LLMs on their ability to engage in common-sense reasoning about a variety of everyday situations and observe similar patterns of errors in both people and models. We then identify the set of attention heads driving LLM responses and find that these heads implement a form of pattern-matching. These attention heads allow us to predict seemingly inexplicable reasoning errors in people caused by ostensibly irrelevant prompt details. Taken together, our results suggest that everyday causal reasoning in people and LLMs is more consistent with a form of pattern-matching than with abstract world models.

Comments:<br>13 pages main text, 51 pages supplementary text

Subjects:

Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2606.13607 [cs.AI]

(or<br>arXiv:2606.13607v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.13607

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Zach Studdiford [view email]<br>[v1]<br>Thu, 11 Jun 2026 17:23:10 UTC (5,525 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning, by Zach Studdiford and Gary Lupyan<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.AI

next >

new<br>recent<br>| 2026-06

Change to browse by:

cs

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

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? |<br>Disable MathJax (What is MathJax?)