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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 AsGet 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

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