How humans learn and represent networks

TL;DR

This paper reviews how humans learn and represent networks underlying sequences of items, emphasizing the importance of network structure beyond simple transition probabilities, and discusses computational models and open questions in the field.

Contribution

It introduces the interdisciplinary field of graph learning, summarizes recent experimental findings, and discusses computational models explaining human network learning and representation.

Findings

Humans detect differences in transition probabilities in sequences.

Behavior depends on the abstract network structure of transitions.

Computational models explain how network structure influences cognition.

Abstract

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.