# Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning

arXiv: https://arxiv.org/abs/2508.10057 · HTML: https://mlsanity.com/paper/2508.10057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 29 of its 72 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 29 of 29)

- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [The Platonic Representation Hypothesis](https://mlsanity.com/paper/2405.07987/) (2024)
- [Layer by Layer: Uncovering Hidden Representations in Language Models](https://mlsanity.com/paper/2502.02013/) (2025)
- [On the Measure of Intelligence](https://mlsanity.com/paper/1911.01547/) (2019)
- [Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning](https://mlsanity.com/paper/2401.06805/) (2024)
- [How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study](https://mlsanity.com/paper/2402.16061/) (2024)
- [The Curse of Depth in Large Language Models](https://mlsanity.com/paper/2502.05795/) (2025)
- [Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks](https://mlsanity.com/paper/2311.09247/) (2023)
- [Large Language Models Are Not Strong Abstract Reasoners](https://mlsanity.com/paper/2305.19555/) (2023)
- [The SWE-Bench Illusion: When State-of-the-Art LLMs Remember Instead of Reason](https://mlsanity.com/paper/2506.12286/) (2025)
- [Core Knowledge Deficits in Multi-Modal Language Models](https://mlsanity.com/paper/2410.10855/) (2024)
- [Does Representation Matter? Exploring Intermediate Layers in Large Language Models](https://mlsanity.com/paper/2412.09563/) (2024)
- [Evaluating the Robustness of Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2411.14215/) (2024)
- [Easy Problems That LLMs Get Wrong](https://mlsanity.com/paper/2405.19616/) (2024)
- [Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders](https://mlsanity.com/paper/2410.06981/) (2024)
- [LLMs as Models for Analogical Reasoning](https://mlsanity.com/paper/2406.13803/) (2024)
- [Visual representations in the human brain are aligned with large language models](https://mlsanity.com/paper/2209.11737/) (2022)
- [Layers at Similar Depths Generate Similar Activations Across LLM Architectures](https://mlsanity.com/paper/2504.08775/) (2025)
- [What Are Large Language Models Mapping to in the Brain? A Case Against Over-Reliance on Brain Scores](https://mlsanity.com/paper/2406.01538/) (2024)
- [Can Large Language Models generalize analogy solving like children can?](https://mlsanity.com/paper/2411.02348/) (2024)
- [ARN: Analogical Reasoning on Narratives](https://mlsanity.com/paper/2310.00996/) (2023)
- [Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning](https://mlsanity.com/paper/2412.05586/) (2024)
- [Layerwise Recall and the Geometry of Interwoven Knowledge in LLMs](https://mlsanity.com/paper/2502.10871/) (2025)
- [Do Large Language Models Think Like the Brain? Sentence-Level Evidences from Layer-Wise Embeddings and fMRI](https://mlsanity.com/paper/2505.22563/) (2025)
- [Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation](https://mlsanity.com/paper/2506.02648/) (2025)
- [A large-scale evaluation of commonsense knowledge in humans and large language models](https://mlsanity.com/paper/2505.10309/) (2025)
- [Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning](https://mlsanity.com/paper/2502.14086/) (2025)
- [The Representational Alignment between Humans and Language Models is implicitly driven by a Concreteness Effect](https://mlsanity.com/paper/2505.15682/) (2025)

## Built on by (most cited first, 2 of 2)

- [Large Language Models Align with the Human Brain during Creative Thinking](https://mlsanity.com/paper/2604.03480/) (2026)
- [Which Attention Heads are like the Human Head? Not the Ones that Compute](https://mlsanity.com/paper/2609.37991/) (2026)

## Abstract (arXiv)

This study investigates whether large language models (LLMs) mirror human neurocognition during abstract reasoning. We compared the performance and neural representations of human participants with those of eight open-source LLMs on an abstract-pattern-completion task. We leveraged pattern type differences in task performance and in fixation-related potentials (FRPs) as recorded by electroencephalography (EEG) during the task. Our findings indicate that only the largest tested LLMs (~70 billion parameters) achieve human-comparable accuracy, with Qwen-2.5-72B and DeepSeek-R1-70B also showing similarities with the human pattern-specific difficulty profile. Critically, every LLM tested forms representations that distinctly cluster the abstract pattern categories within their intermediate layers, although the strength of this clustering scales with their performance on the task. Moderate positive correlations were observed between the representational geometries of task-optimal LLM layers and human frontal FRPs. These results consistently diverged from comparisons with other EEG measures (response-locked ERPs and resting EEG), suggesting a potential shared representational space for abstract patterns. This indicates that LLMs might mirror human brain mechanisms in abstract reasoning, offering preliminary evidence of shared principles between biological and artificial intelligence.