Presentation Video (IEEE VR 2023)

Fast Forward (IEEE VR 2023)


Human performance is poor at detecting certain changes in a scene, a phenomenon known as change blindness. Although the exact reasons of this effect are not yet completely understood, there is a consensus that it is due to our constrained attention and memory capacity: We create our own mental, structured representation of what surrounds us, but such representation is limited and imprecise. Previous efforts investigating this effect have focused on 2D images; however, there are significant differences regarding attention and memory between 2D images and the viewing conditions of daily life. In this work, we present a systematic study of change blindness using immersive 3D environments, which offer more natural viewing conditions closer to our daily visual experience. We devise two experiments; first, we focus on analyzing how different change properties (namely type, distance, complexity, and field of view) may affect change blindness. We then further explore its relation with the capacity of our visual working memory and conduct a second experiment analyzing the influence of the number of changes. Besides gaining a deeper understanding of the change blindness effect, our results may be leveraged in several VR applications such as redirected walking, games, or even studies on saliency or attention prediction.



@article{martin2023changeblindness, author={Martin, Daniel and Sun, Xin and Gutierrez, Diego and Masia, Belen}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={A Study of Change Blindness in Immersive Environments}, year={2023}, volume={}, number={}, pages={1-10}, doi={10.1109/TVCG.2023.3247102}}

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