Understanding human navigation using network analysis
We have considered a simple word game called the word-morph. After making our participants play a stipulated number of word-morph games, we have analyzed the experimental data. We have given a detailed analysis of the learning involved in solving this word game. We propose that people are inclined t...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
17 January 2012
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| In: |
Topics in cognitive science
Year: 2012, Jahrgang: 4, Heft: 1, Pages: 121-134 |
| ISSN: | 1756-8765 |
| DOI: | 10.1111/j.1756-8765.2011.01178.x |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1111/j.1756-8765.2011.01178.x Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1756-8765.2011.01178.x |
| Verfasserangaben: | S.R. Sudarshan Iyengar, C.E. Veni Madhavan, Katharina A. Zweig, Abhiram Natarajan |
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| 520 | |a We have considered a simple word game called the word-morph. After making our participants play a stipulated number of word-morph games, we have analyzed the experimental data. We have given a detailed analysis of the learning involved in solving this word game. We propose that people are inclined to learn landmarks when they are asked to navigate from a source to a destination. We note that these landmarks are nodes that have high closeness-centrality ranking. | ||
| 650 | 4 | |a Centrality measures | |
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