Comparing User Behavior in Real vs. Virtual Supermarket Shelves: An Eye-Tracking Study Using Tobii 3 Pro and Meta Quest Pro
October 19, 2025 Β· Declared Dead Β· π InteracciΓ³n
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Francesco Vona, Julia Schorlemmer, Paulina Kaulard, Sebastian Fischer, Jessica Stemann, Jan-Niklas Voigt-Antons
arXiv ID
2510.16764
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
InteracciΓ³n
Last Checked
4 months ago
Abstract
This study compares user behavior between real and virtual supermarket shelves using eye tracking technology to assess behavior in both environments. A sample of 29 participants was randomly assigned to two conditions: a real world supermarket shelf with Tobii eye tracking and a virtual shelf using the Meta Quest Pro eye tracker. In both scenarios, participants were asked to select three packs of cereals belonging to specific categories, healthy or tasty. The aim was to explore whether virtual environments could realistically replicate real world experiences, particularly regarding consumer behavior. By analyzing eye tracking data, the study examined how attention and product selection strategies varied between real and virtual conditions. Results showed that participants' attention differed across product types and shopping environments. Consumers focused more on lower shelves in real settings, especially when looking for healthy products. In VR, attention shifted to eye level shelves, particularly for tasty items, aligning with optimal product placement strategies in supermarkets. Overall, sweet products received less visual attention across both settings.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted