Project overview
Our recent VR dyad study measured shared attention as a continuous spatial estimate: the Receiver placed a cursor where the avatar’s operator attended. Natural interaction is often categorical instead: is my partner looking at me, at the object we are discussing, or somewhere else entirely? Categorical judgments impose far lower resolution demands, and theories of social attention argue that such functionally sufficient categories, not point estimates, are what people actually compute [1]. This project maps which avatar cues support categorical attention judgments and how little rendering fidelity suffices for them.
Project motivation
Many practical systems only need categorical attention (turn-taking support, “is anyone looking at my screen?”, meeting analytics [2]). If categorical judgments are robust to heavily reduced avatars, lightweight rigs can support them; and the special category “looking at me” connects this work to detection-threshold research on mutual gaze [3]. The parent study predicts body cues will dominate here too, but the me/not-me boundary is exactly where eye direction is psychophysically strongest, so the hierarchy may flip for that category.
Project goals
- Categorical paradigm. Extend the existing Unity environment with a categorical response mode (me / named object / elsewhere) alongside the continuous cursor, and run dyads through both.
- Sufficiency thresholds. Determine, per category, which cue sets (full body, body without eyes, head only) keep categorical accuracy high, and compare the cue hierarchy for the “looking at me” category against object-directed judgments.
You will
- Perform a literature review on categorical gaze perception and mutual gaze
- Add a categorical response mode to the existing Unity VR environment
- Run a dyad study with the existing motion-capture and eye-tracking pipeline
- Analyze accuracy per category and cue set (existing tooling available)
- Summarize your findings in a thesis and present them to an audience
- (Optional) co-write a research paper
You need
- Strong communication skills in English
- Good knowledge of Unity
- Basic statistics
References
- Stephenson, L. J., Edwards, S. G., & Bayliss, A. P. (2021). From gaze perception to social cognition: The shared-attention system. Perspectives on Psychological Science.
- Vertegaal, R. (1999). The GAZE groupware system: Mediating joint attention in multiparty communication and collaboration. CHI 1999. https://doi.org/10.1145/302979.303065
- Schott, D., et al. (2025). Estimating Detection Thresholds of Being Looked at in Virtual Reality for Avatar Redirection. CHI 2025. https://doi.org/10.1145/3706598.3714041
- The parent study on avatar-mediated shared attention in virtual reality; details are available from the advisor on request.
Keywords: VR, Mutual Gaze, Categorical Perception, Shared Attention, Avatars