Digital Humanities Day #3

Mental representations of geographic space are based on knowledge of spatial elements and the spatial relation between these elements [1, 2]. This also requires assessing distances between pairs of spatial elements.

In Virtual Reality (VR) applications, physics-based locomotion is constrained by the size of the available room space and limitations of the used room scale tracking system [3]. Therefore, many VR applications use additional locomotion methods as artificial locomotion (continuous forward movement) or teleporting (‘jumping’ from one location to another). These locomotion methods move the user through virtual space based on controller input. However, it has not yet been investigated how different established controller-based locomotion methods affect distance estimations in VR.

In an experiment, we compared distance estimations between artificial locomotion and teleport before and after training the participants (introduction of a 10 m movement). The results showed that distance estimations in both locomotion conditions improved after the training. Thus, feedback seems to improve VR distance estimations. Additionally, distance estimations were found to be more accurate when teleport locomotion was used. We argue that participants from both locomotion conditions may have relied on different counting strategies: counting seconds in the artificial locomotion condition and subdividing the total distance in smaller chunks in the teleport condition. As counting seconds requires continuous attention, it may be interpreted as cognitively more demanding than counting the amount of teleport jumps. This supposedly higher cognitive load may have negatively affected distance estimations.

Future studies should investigate how distance estimations with controller-based locomotion differ from physics-based locomotion in VR or in real-world applications. As physics-based locomotion adds the perception of proprioceptive feed-back, distance estimations are expected to be more accurate and shorter than distance estimations with controller-based locomotion types.

Corresponding email: dennis.edler@rub.de

References

[1] Tversky, B.: Structures of Mental Spaces: How People Think About Space. Environment and Behavior 35(1), 66–80 (2003).
[2] McNamara, T.P., Valiquette, C.M.: Remembering Where Things Are. In Alle, G.L. (ed.) Human spatial memory: Remembering where., pp. 23–44, Lawrence Erlbaum Associates Publishers (2004).
[3] Langbehn, E., Lubos, P., Bruder, G., Steinicke, F.: Bending the Curve: Sensitivity to Bend-ing of Curved Paths and Application in Room-Scale VR, IEEE transactions on visualization and computer graphics 23(4), 1389–1398 (2017).