Spatial Cognition: The Role of Landmark, Route, and Survey Knowledge in Human and Robot Navigation1

Werner, Steffen; Krieg-Brückner, Bernd; Mallot, Hanspeter A.; Schweizer, Karin; Freksa, Christian

doi:10.1007/978-3-642-60831-5_8

Cited by 66 publications

(44 citation statements)

References 9 publications

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“…Students were required to sketch maps of a given real world environment, and the maps were then grouped according to the landmark, route, survey model for the acquisition of spatial knowledge (Werner et al, 1997). When compared to the marks achieved in the course, they found a trend for the high achievers to draw survey maps, while those who sketched route maps performed less well but better than those who produced landmark maps.…”

Section: Spatial Ability and Mental Modelsmentioning

confidence: 99%

Spatial Ability and Learning to Program

Jones¹,

Burnett²

2008

Human Technology

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Section: Spatial Ability and Mental Modelsmentioning

confidence: 99%

Spatial Ability and Learning to Program

Jones¹,

Burnett²

2008

Human Technology

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“…Although relying on a host of different sensory and motor mechanisms, the abstract tasks of planning a route and executing the right actions along the way are nearly the same (e.g. Werner et al, 1997). It is thus not surprising that in this field, disciplines such as biology, psychology, linguistics, artificial intelligence, and geography jointly contribute to our understanding of the basic processes underlying spatial navigation.…”

Section: Introductionmentioning

confidence: 99%

Modelling Navigational Knowledge by Route Graphs

Werner

Krieg-Brückner

Herrmann

2000

Lecture Notes in Computer Science

Self Cite

107

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Abstract. Navigation has always been an interdisciplinary topic of research, because mobile agents of different types are inevitably faced with similar navigational problems. Therefore, human navigation can readily be compared to navigation in other biological organisms or in artificial mobile agents like autonomous robots. One such navigational strategy, route-based navigation, in which an agent moves from one location to another by following a particular route, is the focus of this paper. Drawing on the research from cognitive psychology and linguistics, biology, and robotics, we present a simple, abstract formalism to express the key concepts of route-based navigation in a common scientific language. Starting with the distinction of places and route segments, we develop the notion of a route graph, which can serve as the basis for complex navigational knowledge. Implications and constraints of the model are discussed along the way, together with examples of different instantiations of parts of the model in different mobile agents. By providing this common conceptual framework, we hope to advance the interdisciplinary discussion of spatial navigation.

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“…In realworld navigation, people usually take three strategies to find a destination. They remember properties of landmarks such as shape and structure (i.e., landmark knowledge) [49,50], or the sequential order of landmarks encountered and directional relationship between these landmarks (i.e., route knowledge) [51], or an overview of the environment like a map showing spatial relationships between routes and landmarks (i.e., survey knowledge) [52] to find a destination. This knowledge is also involved in web navigation.…”

Section: Quantifying Mental Modelmentioning

confidence: 99%

How Influential Are Mental Models on Interaction Performance? Exploring the Gap between Users’ and Designers’ Mental Models through a New Quantitative Method

Xie

Zhou

Wang

2017

Advances in Human-Computer Interaction

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The objective of this study is to investigate the effect of the gap between two different mental models on interaction performance through a quantitative way. To achieve that, an index called mental model similarity and a new method called path diagram to elicit mental models were introduced. There are two kinds of similarity: directionless similarity calculated from card sorting and directional similarity calculated from path diagram. An experiment was designed to test their influence. A total of 32 college students participated and their performance was recorded. Through mathematical analysis of the results, three findings were derived. Frist, the more complex the information structures, the lower the directional similarity. Second, directional similarity (rather than directionless similarity) had significant influence on user performance, indicating that it is more effective in eliciting mental models using path diagram than card sorting. Third, the relationship between information structures and user performance was partially mediated by directional similarity. Our findings provide practitioners with a new perspective of bridging the gap between users' and designers' mental models.

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Spatial Cognition: The Role of Landmark, Route, and Survey Knowledge in Human and Robot Navigation1

Cited by 66 publications

References 9 publications

Spatial Ability and Learning to Program

Spatial Ability and Learning to Program

Modelling Navigational Knowledge by Route Graphs

How Influential Are Mental Models on Interaction Performance? Exploring the Gap between Users’ and Designers’ Mental Models through a New Quantitative Method

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