# Charting trajectories of human thought using large language models

arXiv: https://arxiv.org/abs/2509.14455 · HTML: https://mlsanity.com/paper/2509.14455/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [Characterizing Human Semantic Navigation in Concept Production as Trajectories in Embedding Space](https://mlsanity.com/paper/2602.05971/) (2026)
- [AI Models Can Predict and Collaboratively Modulate Human Memory Search](https://mlsanity.com/paper/2608.26152/) (2026)
- [Multi-System Cognitive Steering for Generative Communication](https://mlsanity.com/paper/2610.19177/) (2026)

## Abstract (arXiv)

Language provides the most revealing window into the ways humans structure conceptual knowledge within cognitive maps. Harnessing this information has been difficult, given the challenge of reliably mapping words to mental concepts. Artificial Intelligence large language models (LLMs) now offer unprecedented opportunities to revisit this challenge. LLMs represent words and phrases as high-dimensional numerical vectors that encode vast semantic knowledge. To harness this potential for cognitive science, we introduce VECTOR, a computational framework that aligns LLM representations with human cognitive map organisation. VECTOR casts a participant's verbal reports as a geometric trajectory through a cognitive map representation, revealing how thoughts flow from one idea to the next. Applying VECTOR to narratives generated by 1,100 participants, we show these trajectories have cognitively meaningful properties that predict paralinguistic behaviour (response times) and real-world communication patterns. We suggest our approach opens new avenues for understanding how humans dynamically organise and navigate conceptual knowledge in naturalistic settings.