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

• URL: http://arxiv.org/abs/2606.13607v2
• Date: Sat, 13 Jun 2026 20:58:19 GMT
• Title: Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning
• Abstract summary: Large language models (LLMs) fail to generalize or make haphazard errors in reasoning.<n>The implication is that people's behavior does not exhibit the same types of failures because human reasoning uses principled and abstract world models.<n>We evaluate human participants and 25 LLMs on their ability to engage in common-sense reasoning about a variety of everyday situations.
• Score: 0.13368662482451263
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Thinking effort aligns between humans and reasoning models in abductive reasoning [0.0]
  Large language models (LRMs) are optimized with reinforcement learning from verifiable rewards, encouraging correct solutions to reasoning tasks.<n>Recent work investigates the cost of thinking in humans and LRMs by comparing human reaction times with model reasoning traces.<n>We find further evidence of alignment between LRM and human reasoning effort, as well as evidence that models and humans tend to make similar errors.
  arXiv  Detail & Related papers  (2026-09-01T21:02:18Z)
• To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks [56.11584171938381]
  Theory of Mind (ToM) assesses whether models can infer hidden mental states such as beliefs, desires, and intentions.<n>Recent progress in Large Reasoning Models (LRMs) has boosted step-by-step inference in mathematics and coding.<n>We present a systematic study of nine advanced Large Language Models (LLMs) comparing reasoning models with non-reasoning models.
  arXiv  Detail & Related papers  (2026-02-11T08:16:13Z)
• MORABLES: A Benchmark for Assessing Abstract Moral Reasoning in LLMs with Fables [50.29407048003165]
  We present MORABLES, a human-verified benchmark built from fables and short stories drawn from historical literature.<n>The main task is structured as multiple-choice questions targeting moral inference, with carefully crafted distractors that challenge models to go beyond shallow, extractive question answering.<n>Our findings show that, while larger models outperform smaller ones, they remain susceptible to adversarial manipulation and often rely on superficial patterns rather than true moral reasoning.
  arXiv  Detail & Related papers  (2025-09-15T19:06:10Z)
• Can Reasoning Help Large Language Models Capture Human Annotator Disagreement? [84.32752330104775]
  Variation in human annotation (i.e., disagreements) is common in NLP.<n>We evaluate the influence of different reasoning settings on Large Language Model disagreement modeling.<n>Surprisingly, our results show that RLVR-style reasoning degrades performance in disagreement modeling.
  arXiv  Detail & Related papers  (2025-06-24T09:49:26Z)
• Do Large Language Models Reason Causally Like Us? Even Better? [7.749713014052951]
  Large language models (LLMs) have shown impressive capabilities in generating human-like text.<n>We compared causal reasoning in humans and four LLMs using tasks based on collider graphs.
  arXiv  Detail & Related papers  (2025-02-14T15:09:15Z)
• Failure Modes of LLMs for Causal Reasoning on Narratives [51.19592551510628]
  We investigate the interaction between world knowledge and logical reasoning.<n>We find that state-of-the-art large language models (LLMs) often rely on superficial generalizations.<n>We show that simple reformulations of the task can elicit more robust reasoning behavior.
  arXiv  Detail & Related papers  (2024-10-31T12:48:58Z)
• RULEBREAKERS: Challenging LLMs at the Crossroads between Formal Logic and Human-like Reasoning [3.0648414540406703]
  We create RULEBREAKERS, the first dataset for rigorously evaluating the ability of large language models to recognize and respond to rulebreakers in a human-like manner.<n>We find that most models, including GPT-4o, achieve mediocre accuracy on RULEBREAKERS and exhibit some tendency to over-rigidly apply logical rules unlike what is expected from typical human reasoners.
  arXiv  Detail & Related papers  (2024-10-21T20:48:16Z)
• Large Language Models Assume People are More Rational than We Really are [10.857040292234984]
  In order for AI to communicate effectively with people, they must understand how we make decisions.<n>Previous empirical evidence seems to suggest that these implicit models are accurate.<n>We find that this is actually not the case when both simulating and predicting people's choices.
  arXiv  Detail & Related papers  (2024-06-24T18:15:27Z)
• (Ir)rationality and Cognitive Biases in Large Language Models [2.9008806248012333]
  We evaluate seven language models using tasks from the cognitive psychology literature. We find that, like humans, LLMs display irrationality in these tasks. When incorrect answers are given by LLMs to these tasks, they are often incorrect in ways that differ from human-like biases.
  arXiv  Detail & Related papers  (2024-02-14T14:17:21Z)
• A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning [73.77088902676306]
  We take a closer look at the self-verification abilities of large language models (LLMs) in the context of logical reasoning. Our main findings suggest that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self-verification methods.
  arXiv  Detail & Related papers  (2023-11-14T07:13:10Z)
• Exposing Attention Glitches with Flip-Flop Language Modeling [55.0688535574859]
  This work identifies and analyzes the phenomenon of attention glitches in large language models. We introduce flip-flop language modeling (FFLM), a family of synthetic benchmarks designed to probe the extrapolative behavior of neural language models. We find that Transformer FFLMs suffer from a long tail of sporadic reasoning errors, some of which we can eliminate using various regularization techniques.
  arXiv  Detail & Related papers  (2023-06-01T17:44:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.