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Journal Article

Isovist analysis captures properties of space relevant for locomotion and experience

MPS-Authors
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Franz,  G
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Rossmanith,  N
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84161

Reichelt,  A
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83839

Bülthoff,  HH
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://journals.sagepub.com/doi/10.1068/p5587
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Citation

Wiener, J., Franz, G., Rossmanith, N., Reichelt, A., Mallot, H., & Bülthoff, H. (2007). Isovist analysis captures properties of space relevant for locomotion and experience. Perception, 36(7), 1066-1083. doi:10.1068/p5587.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CD4F-B
Abstract
In a series of exploratory experiments we investigated interrelations between structure and shape of architectural indoor spaces on the one hand, and affective experience and navigation behaviour on the other hand. For this, isovist-based descriptions of 16 virtual indoor scenes were correlated with behavioural data from the experimental tasks. For all tasks—two active navigation tasks and an introspective appraisal of experiential qualities—strong correlations between subjects’ behaviour and a small set of quantitative measurands derived from the isovists were found. The outcomes suggest that isovist analysis captures behaviourally relevant properties of space and is therefore a promising general means for predicting central experiential qualities of architecture and navigation behaviour.