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Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
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A dataset on human navigation strategies in foreign networked systems.
Attila Kőrösi1, Attila Csoma1, Gábor Rétvári1
1MTA-BME Information Systems Research Group, Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar tudósok krt. 2, Hungary.
Scientific Data
|March 14, 2018
Summary
This study introduces a novel dataset of human navigation strategies in word-based networks. The data reveals how players develop mental models and navigation skills in unfamiliar complex systems.
Area of Science:
- Cognitive Science
- Network Science
- Human-Computer Interaction
Background:
- Humans interact with numerous real-world networked systems, including social networks, language, and physical environments.
- Understanding human navigation and information acquisition in unfamiliar networks is crucial but lacks large, open datasets.
Purpose of the Study:
- To introduce a novel dataset of human navigation in a word-based network.
- To facilitate research into human mental models and navigation strategies within complex systems.
Main Methods:
- A smartphone application was developed to collect data on players navigating between English words.
- Navigation involved step-by-step changes, altering only one letter at a time between source and destination words.
- The collected paths serve as a proxy for players' navigation skills and mental model development.
Main Results:
- The dataset captures the emergent navigation strategies employed by human players.
- Analysis of paths provides insights into how individuals learn and adapt to foreign networked environments.
- The data reflects the players' mastery of navigation skills within the defined word network.
Conclusions:
- The dataset offers a valuable resource for studying human navigation in abstract networks.
- It enables broader investigations into cognitive strategies for exploring and utilizing complex systems.
- This work bridges the gap in empirical data for understanding human behavior in networked environments.
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