Welcome to the presentation of the Effect\h of Increased Body Motion in Virtual Reality\h\h
on the Placement-Retrieval Tasl by\h Arnaud Prouzeau Michael McGuffin and\h\h
myself Thibault Friedrich We worked on how to\h improve user interfaces for knowledge workers.\h\h
In virtual reality, you can replace physical\h desktop monitors with virtual floating windows.\h\h
It allows users to work in standing mode,\h sitting mode or even while moving around.\h\h
But because of so many windows\h around, the user may be lost.\h\h
In the literature, a lot of factors are\h improving Spatial Memory of windows layout\h\h
but there is no literature about user's\h Locomotion to improve Spatial Memory of\h\h
windows layout in Virtual Reality that's why\h we decided to focus on this specific point.
In the literature, various tasks exist to measure\h the impact of different factors on Spatial Memory\h\h
for example the Placement-Retrieval task\h called Data Mountain proposed to the user to\h\h
place several windows and then\h retrieve some of these windows.
This task has been designed by George\h Robertson in 1998 and for example,\h\h
Andy Cockburn used this same task Data Mountain\h\h
to show the impact of Spatial Memory in 2d\h in 3d physical and virtual environments.
In the literature, you also have the\h impact of locomotion on special memory\h\h
with Wall Displays with for example Can Liu\h that uses Wall display to show the impact of\h\h
Locomotion on a classification task or the article\h of Mikkel Jakobsen or even the article of Yvonne\h\h
Jansen who used Wall Display to show the effect\h of Locomotion using the same Data Mountain task.\h\h
And what about Spatial Memory in Virtual\h Reality? Eric Krokos showed the impact of\h\h
immersion on recall using the\h principle of virtual memory palace.\h\h
But there is no previous studies of the impact\h of locomotion on Spatial Memory in Virtual\h\h
Reality maybe because it was not easy to create\h experiments with Locomotion in VR in the past\h\h
but now using new VR headsets like\h Oculus Quest, it is far easier.
Our experiment consists of a\h Placement-Retrieval Task similar to\h\h
Data Mountain but adapted to Virtual Reality.\h The first phase is a placement of 60 windows\h\h
where the user has to drag and drop\h these windows using an oculus touch.
Then the user can fully reposition all windows\h during the phase. The 60 windows were chosen\h\h
from a pool of 341 screenshots of windows\h generated from a list of the top 500 websites.
The second phase is a blind recall of 20 windows\h\h
where one window is displayed to the user and\h the user has to retrieve the matching windows\h\h
in the 60 windows previously placed using a ray\h cast selection with the oculus touch controller.\h\h
The windows content is this time hidden and\h the user has only one attempt by window.
The third phase is a very visible recall of\h 20 windows using the same ray cast selection.\h\h
This time the window content is visible again\h and the user has one attempt for each window.
Our experiment has two main conditions about\h the movement during the Placement phase.\h\h
The first condition is Stationary where the user\h can't move outside a virtual disk of 30 cm radius.\h\h
As you can see on the video, as soon as the user's\h head is outside the disk, the drag and drop is\h\h
disabled. The user has to come back to the\h disk to have the drag-and-drop able again.
The second condition is Locomotion where the user\h has to place the window outside a virtual disk of\h\h
140cm radius. As you can see on the video\h the window has a red border that disappears\h\h
as soon as the window is outside the disk. Our\h experiment gathered 16 participants, 14 men,\h\h
2 women aged between 20 to 36 years old. Given the\h number of conditions, the number of participants,\h\h
and the number of windows, we had around 2000\h windows positioned and 1000 windows recalled.\h\h
We also measured the time and the error\h as a distance between the selected windows\h\h
and the correct tune windows using different\h distance measures like the Euclidian distance,\h\h
the normalized Euclidian distance, and the\h number of items closer to the correct one.
During our experiment, half the users started\h with the Locomotion and half of them started\h\h
with Stationary. You can see there\h\h
the layout of this part of all the\h participants during the experiment.
During our analysis, we used confident intervals\h instead of neural hypothesis significance\h\h
testing and p-values based on Pierre Dragicevic's\h article. About the placement, we can see that the\h\h
confidence interval of stationary minus locomotion\h is mostly below zero. It means that Stationary\h\h
is faster than Locomotion but this part is totally\h normal because the user has to use his legs.\h\h
The real question is does Locomotion improve\h Spatial Memory despite this initial time cost?\h\h
About the visible recall: we can see\h that there is little or no evidence\h\h
of a difference in time between the\h condition. Then we analyzed the error\h\h
based on the confidence interval we can say there\h is no evidence of difference between Stationary\h\h
and Locomotion in the Euclidean Error Distance.\h However there is a difference using the Normalized\h\h
Error Distance but we realized these measures\h are misleading. In the Locomotion condition\h\h
windows place adjointly we'll automatically\h have a smaller Normalized Error Distance.
So we had to go for Post-hoc Exploratory\h Data Analysis. We used a new way to measure\h\h
the error distance which is the number\h of items closer to the correct one.\h\h
Following this new distance, confidence interval\h Stationary minus Locomotion is mostly above zero\h\h
so we can say that locomotion\h results in smaller errors.
In conclusion, we can say that Locomotion\h resulted in a modest benefit to Spatial Memory.\h\h
However, it increased the Placement time of 3.4\h seconds per window. In some cases, indirect input\h\h
like red cast pointing for example, is more\h convenient to position windows and the lack of\h\h
locomotion shouldn't substantially harm Retrieval\h performances. Thank you for your attention.