# How humans learn and represent networks

arXiv: https://arxiv.org/abs/1909.07186 · HTML: https://mlsanity.com/paper/1909.07186/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 10 of 10)

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- [Explainable AI in Speaker Recognition -- Attention Map Visualisation and Evaluation](https://mlsanity.com/paper/2606.22901/) (2026)
- [Emergent Computations in Trained Artificial Neural Networks and Real Brains](https://mlsanity.com/paper/2212.04938/) (2022)
- [Quantifying Human Priors over Social and Navigation Networks](https://mlsanity.com/paper/2402.18651/) (2024)
- [Network psychometrics and cognitive network science open new ways for detecting, understanding and tackling the complexity of math anxiety: A review](https://mlsanity.com/paper/2108.13800/) (2021)
- [Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignment](https://mlsanity.com/paper/2511.08399/) (2025)

## Abstract (arXiv)

Humans communicate, receive, and store information using sequences of items -- from words in a sentence or notes in music to abstract concepts in lectures and books. The networks formed by these items (nodes) and the sequential transitions between them (edges) encode important structural features of human communication and knowledge. But how do humans learn the networks of probabilistic transitions that underlie sequences of items? Moreover, what do people's internal maps of these networks look like? Here, we introduce graph learning, a growing and interdisciplinary field focused on studying how humans learn and represent networks in the world around them. We begin by describing established results from statistical learning showing that humans are adept at detecting differences in the transition probabilities between items in a sequence. We next present recent experiments that directly control for differences in transition probabilities, demonstrating that human behavior also depends critically on the abstract network structure of transitions. Finally, we present computational models that researchers have proposed to explain the effects of network structure on human behavior and cognition. Throughout, we highlight a number of exciting open questions in the study of graph learning that will require creative insights from cognitive scientists and network scientists alike.