The Effect of Interface on Social Action in Online Virtual Worlds

Anthony Steed

Department of Computer Science

University College London

Avatar Puppet Systems

• From the very early systems common behaviours emerged– Customisation of representation– Spatial group-forming behaviours– Social reactions– “Presence” or not in your avatar (i.e. being at the

keyboard) needs to be signalled with gestures, otherwise difficult to interact

Outline: Transparency & Boundaries

• Puppeteering systems take effort to express activity and motion

• Can be difficult for users to understand intentions and actions

• Immersive systems alleviate some of these barriers by making the interface transparent

• Do this by engaging the the user(s) in the virtual world, but we can envisage mixed-reality systems that break the boundary in a different way

Immersive Interfaces

Effectiveness of Immersive Interaction

Subjective Report

Tasks

Cognitive & Emotional

Behaviour

PhysiologicalAutonomic

Social

Effectiveness of Immersive Interaction

Subjective Report

Tasks

Cognitive & Emotional

Behaviour

PhysiologicalAutonomic

Social

Effectiveness of Immersive Interaction

Subjective Report

Tasks

Cognitive & Emotional

Behaviour

PhysiologicalAutonomic

Social

Effectiveness of Immersive Interaction

Subjective Report

Tasks

Cognitive & Emotional

Behaviour

PhysiologicalAutonomic

Social

“I felt as if I was being watched”

Effectiveness of Immersive Interaction

Subjective Report

Tasks

Cognitive & Emotional

Behaviour

PhysiologicalAutonomic

Social

Effectiveness of Immersive Interaction

Subjective Report

Tasks

Cognitive & Emotional

Behaviour

PhysiologicalAutonomic

Social

Why is Collaboration So Effective?

• Tracked gestures are immediately communicative• It is very easy to interpret gaze and pointing of the

other• Immersed users spend very little time

“manipulating” the interface

• Indeed in other experiments, users with immersive interfaces emerged as leaders over desktop interfaces users

Forgetting Which Hand is Which

Capturing the User

Eyecatching: Eyetracking Generation 2

Experiment Outline

• Task– Grab-Instructions

– Position-Instructions

• Measures– Time to do both types of instruction

– Errors in both types of instruction

– Conversational analysis

• Conditions– No eye movement

– Modelled eye gaze

– Tracked eye gaze

Results

Conversational Analysis

• Success– “OK, can you pick this cube”

– “This one?”

– “Yes”

Look at speaker

Look from head to cube

Point at cubeLook at speaker

Mutual gaze

Look at speaker

Look at speaker

Look at speaker

Conversational Analysis

• Failure– “OK, can you pick this cube”

– “This one?”

– “No, this one”, “This one?”

Look at speaker

Look from head to wrong cube

Point at wrong cubeLook at speaker

Mutual gaze

Look at cube

Technical Challenges

• Avatar representation• Lip synchronisation• End-to-end latency• Frame rate• Motion capture

• Capture real world so you can talk about it

Eye Gaze

Key Aspects

• Comparison with video as benchmark• Subjects answer as series of questions to a

confederate• Stage 1: Do users exhibit characteristic gaze,

blink and pupil dilation when they talk to a video or avatar-mediated representation of a questioner?

• Stage 2: Can independent observers detect lying when it is presented as an avatar?

Stage 1: Key Findings

• Participants have similar behaviour when speaking to an avatar or a video

Stage 2: Key Findings

Breaking Boundaries

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CaptureDestination

CaptureVisitor

DisplayVisitor

DisplayDestination

\\\\\

Asymmetric Collaboration

Immersion is great but …

• Immersive interfaces are expensive• You are bound to the metaphor where there is a

virtual place you go to

• Bring the avatar to you– Make it aware of the user and the space around you– Interpret the real world and interact with it

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Demo Highlights

Panoramic Camera(PointGrey Ladybug 3)

Conclusions

Conclusions

• Collaboration can be very fluid with immersive interfaces– Several challenges remain concerning capture– Desirable to bring more of those capabilities to non-

immersive (passive capture) systems

• Many rules that can be applied to agents• We expect that asymmetric collaboration

situations will be more common in the future and this deserves further attention

Acknowledgements

• Eyecatching– William Steptoe, Robin Wolff, Alessio Murgia, Estefania

Guimaraes, John Rae, Wole Oyekoya

• Presenccia– Wole Oyekoya

• BEAMING– Will Steptoe, Wole Oyekoya, Tim Weyrich, Fabrizio

Pece, Jan Kautz– Partners at UB, Jean-Marie Normand, Mel Slater– www.beaming-eu.org