Human Learning of Hierarchical Graphs

TL;DR

This study investigates how humans learn hierarchical graph structures, revealing that finer-level transitions are learned more readily than coarser levels, with implications for understanding hierarchical sequence learning.

Contribution

The paper demonstrates, through simulations and experiments, that humans more effectively learn finer hierarchical transitions, and identifies a trade-off in learning different hierarchical levels.

Findings

Stronger surprisal effects at finer hierarchy levels.

Difficulty detecting surprisal at coarser levels with limited data.

Trade-off in learning finer versus coarser hierarchical structures.

Abstract

Humans are constantly exposed to sequences of events in the environment. Those sequences frequently evince statistical regularities, such as the probabilities with which one event transitions to another. Collectively, inter-event transition probabilities can be modeled as a graph or network. Many real-world networks are organized hierarchically and understanding how humans learn these networks is an ongoing aim of current investigations. While much is known about how humans learn basic transition graph topology, whether and to what degree humans can learn hierarchical structures in such graphs remains unknown. We investigate how humans learn hierarchical graphs of the Sierpi\'nski family using computer simulations and behavioral laboratory experiments. We probe the mental estimates of transition probabilities via the surprisal effect: a phenomenon in which humans react more slowly to less expected transitions, such as those between communities or modules in the network. Using mean-field predictions and numerical simulations, we show that surprisal effects are stronger for finer-level than coarser-level hierarchical transitions. Surprisal effects at coarser levels of the hierarchy are difficult to detect for limited learning times or in small samples. Using a serial response experiment with human participants (n=$100$), we replicate our predictions by detecting a surprisal effect at the finer-level of the hierarchy but not at the coarser-level of the hierarchy. To further explain our findings, we evaluate the presence of a trade-off in learning, whereby humans who learned the finer-level of the hierarchy better tended to learn the coarser-level worse, and vice versa. Our study elucidates the processes by which humans learn hierarchical sequential events. Our work charts a road map for future investigation of the neural underpinnings and behavioral manifestations of graph learning.