Linking Cognitive and Computational Saliences in Route Information
Finding a destination in a new spatial environment can be a daunting task. To aid navigation, many people take advantage of route directions, either provided by other people or by electronic navigation services. However, their effectiveness may be hampered if they are overly complex. While most people are generally good at focusing on important information, this is a challenge for navigation services. Thus, being able to automatically determine important points along a route that need to be included in route directions would provide a further step towards cognitively ergonomic navigation services. In the present study, methods for calculating the salience—or importance—of decision points are correlated with the frequency of decision points appearing in route directions. Results show that metrics based on the probability of a decision point being traversed and information-theoretic quantities of decision points correlate significantly with incidence in route directions, indicating that it is possible to identify crucial decision points in advance. This has implications for the design of navigation services that are able to adapt their assistance in real time.
Keywordsnavigation route directions individual differences salience
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- 3.Caduff, D., Timpf, S.: The landmark spider: Representing landmark knowledge for wayfinding tasks. In: Barkowsky, T., Freksa, C., Hegarty, M., Lowe, R. (eds.) Reasoning with Mental and External Diagrams: Computational Modeling and Spatial Assistance - Papers from the 2005 AAAI Spring Symposium, Menlo Park, CA, pp. 30–35 (2005)Google Scholar
- 7.Dale, R., Geldof, S., Prost, J.P.: Using natural language generation in automatic route description. Journal of Research and Practice in Information Technology 37(1), 89–105 (2005)Google Scholar
- 9.Denis, M.: The description of routes: A cognitive approach to the production of spatial discourse. Cahiers Psychologie Cognitive 16(4), 409–458 (1997)Google Scholar
- 12.Hillier, B.: Space is the Machine: A Configurational Theory of Architecture. Cambridge University Press, Cambridge (1996)Google Scholar
- 14.Hirtle, S.C., Richter, K.F., Srivinas, S., Firth, R.: This is the tricky part: When directions become difficult. Journal of Spatial Information Science (1), 53–73 (2010)Google Scholar
- 26.Maaß, W.: How Spatial Information Connects Visual Perception and Natural Language Generation in Dynamic Environments: Towards a Computational Model. In: Kuhn, W., Frank, A.U. (eds.) COSIT 1995. LNCS, vol. 988, pp. 223–240. Springer, Heidelberg (1995)Google Scholar
- 28.Michon, P.-E., Denis, M.: When and Why Are Visual Landmarks Used in Giving Directions? In: Montello, D.R. (ed.) COSIT 2001. LNCS, vol. 2205, pp. 292–305. Springer, Heidelberg (2001)Google Scholar
- 29.Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford InfoLab (November 1999)Google Scholar
- 33.Richter, K.F.: Context-Specific Route Directions - Generation of Cognitively Motivated Wayfinding Instructions, DisKI, vol. 314. IOS Press, Amsterdam (2008) also published as SFB/TR 8 Monographs vol. 3Google Scholar
- 34.Richter, K.F., Dara-Abrams, D., Raubal, M.: Navigating and learning with location based services: A user-centric design. In: Gartner, G., Li, Y. (eds.) Proceedings of the 7th International Symposium on LBS and Telecartography, pp. 261–276 (2010)Google Scholar
- 38.Takemiya, M., Ishikawa, T.: Determining decision-point salience for real-time wayfinding support. Journal of Spatial Information Science (in press, 2012)Google Scholar