Optimizing the Human Learnability of Abstract Network Representations

TL;DR

This paper investigates how to optimize the presentation of network structures to improve human learning by reinforcing specific connections based on network topology.

Contribution

It introduces methods for enhancing human learnability of networks through targeted reinforcement of certain network parts, considering different structural features.

Findings

Reinforcing intra-module connections improves learnability.

Emphasizing peripheral edges aids learning in core-periphery networks.

Targeted emphasis can systematically enhance human understanding of networks.

Abstract

Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To answer these questions, we study the optimization of network learnability. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core-periphery structure we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.