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A Computational Method for the Classification of Mental Representations of Objects in 3D Space (Short Paper)

Authors Samuel S. Sohn , Panagiotis Mavros , Mubbasir Kapadia , Christoph Hölscher

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Author Details

Samuel S. Sohn
  • Rutgers University, Piscataway, NJ, USA
Panagiotis Mavros
  • Singapore-ETH Centre, Future Cities Laboratory, CREATE campus, 1 CREATE Way, #06-01 CREATE Tower, 138602, Singapore
Mubbasir Kapadia
  • Rutgers University, Piscataway, NJ, USA
Christoph Hölscher
  • Chair of Cognitive Science, ETH Zürich, Switzerland
  • Future Cities Laboratory, Singapore-ETH Centre, Singapore

Funding

The research was conducted at the Future Cities Lab, Singapore-ETH Centre and supported by the National Research Foundation, Singapore under the CREATE programme, as well as NSF Awards: IIS-1703883, IIS-1955404, IIS-1955365, RETTL-2119265, and EAGER-2122119.

Cite As Get BibTex

Samuel S. Sohn, Panagiotis Mavros, Mubbasir Kapadia, and Christoph Hölscher. A Computational Method for the Classification of Mental Representations of Objects in 3D Space (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 20:1-20:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.COSIT.2022.20

Abstract

The structure mapping task is a simple method to test people’s mental representations of spatial relationships, and has recently been particularly useful in the study of volumetric spatial cognition such as the spatial memory for locations in multilevel buildings. However, there does not exist a standardised method to analyse such data and structure mapping tasks are typically analysed by human raters, based on criteria defined by the researchers. In this article, we introduce a computational method to assess spatial relationships of objects in the vertical and horizontal domains, which are realized through the structure mapping task. Here, we reanalyse participants' digitised structure maps from an earlier study (N=41) using the proposed computational methodology. Our results show that the new method successfully distinguishes between different types of structure map representations, and is sensitive to learning order effects. This method can be useful to advance the study of volumetric spatial cognition.

Subject Classification

ACM Subject Classification
  • General and reference → Metrics
  • General and reference → Experimentation
  • General and reference → Evaluation
Keywords
  • mental representations of space
  • spatial cognition
  • structure mapping task
  • 3D space
  • volumetric space

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References

  1. Simon J. Büchner et al. Path choice heuristics for navigation related to mental representations of a building. In Proceedings of the Euro CogSci, 2007. Google Scholar
  2. R. Campello et al. Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining. Springer, 2013. Google Scholar
  3. Helen Couclelis et al. Exploring the anchor-point hypothesis of spatial cognition. Journal of Environmental Psychology, 7, 1987. Google Scholar
  4. Yan Feng et al. Wayfinding behaviour in a multi-level building: A comparative study of HMD VR and Desktop VR. Advanced Engineering Informatics, 51, 2022. Google Scholar
  5. Mary Hegarty et al. Development of a self-report measure of environmental spatial ability. Intelligence, 30:425-447, 2002. Google Scholar
  6. Christoph Hölscher et al. Up the down staircase: Wayfinding strategies in multi-level buildings. Journal of Environmental Psychology, 26(4):284-299, 2006. Google Scholar
  7. Rob Kitchin and Marc Blades. The Cognition of Geographic Space. I.B. Tauris, 2002. Google Scholar
  8. Saskia F. Kuliga et al. Exploring Individual Differences and Building Complexity in Wayfinding: The Case of the Seattle Central Library. Environment and Behavior, 2019. Google Scholar
  9. Yi Lu and Yu Ye. Can people memorize multilevel building as volumetric map? A study of multilevel atrium building. Environment and Planning B: Urban Analytics and City Science, 46(2):225-242, 2019. URL: https://doi.org/10.1177/2399808317705659.
  10. Panagiotis Mavros et al. Human Navigation in a Multilevel Travelling Salesperson Problem. PsyArXiv Pre-prints, pages 1-38, 2022. Google Scholar
  11. M. Schwartz. Human centric accessibility graph for environment analysis. Automation in Construction, 127:103557, 2021. Google Scholar
  12. G.B. Thomas. Calculus and analytic geometry. Addison-Wesley Publishing, 1968. Google Scholar
  13. J.M. Wiener and Hanspeter A. Mallot. "Fine-to-Coarse" Route Planning and Navigation in Regionalized Environments. Spatial Cognition and Computation, 3(4):331-358, 2003. Google Scholar
  14. Sudanthi Wijewickrema and Andrew Papliński. Principal component analysis for the approximation of an image as an ellipse. In WSCG’2005, Plzen, Czech Republi, 2005. Google Scholar
  15. Svante Wold et al. Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3):37-52, 1987. Google Scholar
  16. Andreas Zwergal et al. Anisotropy of Human Horizontal and Vertical Navigation in Real Space: Behavioral and PET Correlates. Cerebral Cortex, 26(11):4392-4404, 2016. Google Scholar
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