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Same Task, Different Place: Developing Novel Simulation Environments with Equivalent Task Difficulties

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 958)

Abstract

We introduce a novel framework for creating and evaluating multiple virtual reality environments (VEs) that are naturalistic and similar in navigational complexity. We developed this framework in support of a spatial-learning study using a within-subjects design. We generated three interior environments and used graph-theoretic methods to ensure similar complexity. We then developed a scavenger-hunt task that ensured participants would visit all parts of the environments. Here, we describe VE development and a user study evaluating the relative task difficulty in the environments. Our results showed that our techniques were generally successful: the average time to complete the task was similar across environments. Some participants took longer to complete the task in one of the environments, indicating room for refinement of our framework. The methods described here should be of use for future studies using VEs, especially in within-subjects design.

Keywords

Virtual environments Graph-theoretic measures Within-subjects designs Task development Floorplan design Bayesian modelling 

Notes

Acknowledgements

This work was funded by the US Army Research Laboratory’s Human sciences campaign. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. The authors thank Debbie Patton, Mark Ericson, and the entire Training Effectiveness/Immersion group for their comments and suggestions on this work. Bianca Dalangin helped conduct the user study.

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Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020

Authors and Affiliations

  1. 1.US Army Research LaboratoryLos AngelesUSA
  2. 2.DCS CorporationLos AngelesUSA
  3. 3.Psychological and Brain SciencesUniversity of California at Santa BarbaraSanta BarbaraUSA
  4. 4.Computer Science, University of MinnesotaMinneapolisUSA
  5. 5.Natick Soldier Research, Development & Engineering Center – Simulation & Training Technology CenterOrlandoUSA

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