Process reveals structure: How a network is traversed mediates expectations about its architecture

TL;DR

This study investigates how the sequence and predictability of information exposure influence human learning of network structures, revealing that temporal organization significantly affects the acquisition of architectural knowledge.

Contribution

It demonstrates that the temporal pattern of information sampling impacts how humans learn and anticipate network structures, highlighting the role of predictability and sampling density.

Findings

Rich sampling from communities increases processing time at community boundaries.

Regular, exhaustive sampling minimizes uncertainty and reduces processing delays.

Temporal organization critically influences network architecture learning.

Abstract

Network science has emerged as a powerful tool through which we can study the higher-order architectural properties of the world around us. How human learners exploit this information remains an essential question. Here, we focus on the temporal constraints that govern such a process. Participants viewed a continuous sequence of images generated by three distinct walks on a modular network. Walks varied along two critical dimensions: their predictability and the density with which they sampled from communities of images. Learners exposed to walks that richly sampled from each community exhibited a sharp increase in processing time upon entry into a new community. This effect was eliminated in a highly regular walk that sampled exhaustively from images in short, successive cycles (i.e., that increasingly minimized uncertainty about the nature of upcoming stimuli). These results demonstrate that temporal organization plays an essential role in how robustly knowledge of network architecture is acquired.