Towards a computational theory of cognitive maps

Yeap, Wai K.

doi:10.1016/0004-3702(88)90064-1

Cited by 88 publications

(46 citation statements)

References 17 publications

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“…Other theories on spatial orientation in robots [50] and rodents [44] also rely on the visible area as the basic element. 7 Several continuous mental travel in the area of an encoded vista space can be imagined.…”

Section: Vista Space Reference Frames As the Basic Unit In The Represmentioning

confidence: 99%

“…In addition, it is unclear which of the two metric representations determine survey navigation. 7 In Yeap's theory [50] all vista spaces are directly adjacent to each other and are connected via exits. Survey relations computed from that representation are, therefore, correct when the form of individual vista spaces are correct.…”

Section: Vista Space Reference Frames As the Basic Unit In The Represmentioning

confidence: 99%

“…Hierarchic theories of spatial memory have been very prominent (e.g., [4], [11], [40], [50]). In such views, smaller scale spaces are stored at progressively lower levels of the hierarchy.…”

Section: The Relation Between Vista Space Reference Frames: Network Vmentioning

confidence: 99%

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The Network of Reference Frames Theory: A Synthesis of Graphs and Cognitive Maps

Meilinger

Lecture Notes in Computer Science

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Abstract. The network of reference frames theory explains the orientation behavior of human and non-human animals in directly experienced environmental spaces, such as buildings or towns. This includes selflocalization, route and survey navigation. It is a synthesis of graph representations and cognitive maps, and solves the problems associated with explaining orientation behavior based either on graphs, maps or both of them in parallel. Additionally, the theory points out the unique role of vista spaces and asymmetries in spatial memory. New predictions are derived from the theory, one of which has been tested recently.

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Section: Vista Space Reference Frames As the Basic Unit In The Represmentioning

confidence: 99%

Section: Vista Space Reference Frames As the Basic Unit In The Represmentioning

confidence: 99%

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The Network of Reference Frames Theory: A Synthesis of Graphs and Cognitive Maps

Meilinger

Lecture Notes in Computer Science

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“…In the MRE 2 A raw cognitive map contains just information about the local environment without the addition of semantic interpretation (Yeap 1988;Yeap & Jefferies 1999).…”

Section: Motivationmentioning

confidence: 99%

“…Becoming familiar with the configuration of a place like a town is a process that involves walking around, looking at buildings, landmarks, streets and other details of the environment that are subsequently encoded into memories that make the place recognizable and easily navigated. The process of forming these spatial memories is called cognitive mapping (Chown & Kaplan & Kortenkamp 1995;Kuipers 1978;2000;Yeap 1988;Yeap & Jefferies 1999). The ability to build a cognitive map is useful for any agent that has a need for tracking its location, navigating, and determining where places are located with respect to one another (Chown & Kaplan & Kortenkamp 1995;Kortenkamp & Bonasso & Murphy 1998;Kuipers 1978;2000;Levitt & Lawton 1990).…”

Section: Introductionmentioning

confidence: 99%

Applying Perceptually Driven Cognitive Mapping to Virtual Urban Environments

Hill¹,

W.²,

Changhee³

et al. 2002

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This paper describes a method for building a cognitive map of a virtual urban environment. Our routines enable virtual humans to map their environment using a realistic model of perception. We based our implementation on a computational framework proposed by Yeap and Jefferies (Yeap & Jefferies 1999) for representing a local environment as a structure called an Absolute Space Representation (ASR). Their algorithms compute and update ASRs from a 2-1/2D 1 sketch of the local environment, and then connect the ASRs together to form a raw cognitive map. Our work extends the framework developed by Yeap and Jefferies in three important ways. First, we implemented the framework in a virtual training environment, the Mission Rehearsal Exercise (Swartout et al. 2001). Second, we describe a method for acquiring a 2-1/2D sketch in a virtual world, a step omitted from their framework, but which is essential for computing an ASR. Third, we extend the ASR algorithm to map regions that are partially visible through exits of the local space. Together, the implementation of the ASR algorithm along with our extensions will be useful in a wide variety of applications involving virtual humans and agents who need to perceive and reason about spatial concepts in urban environments.

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