IJHCI 2021 Article

Last week, a co-authored journal paper was published at International Journal of Human-Computer Interaction sponsored by Taylor & Francis. The paper is entitled “Study of Full-body Virtual Embodiment Using noninvasive Brain Stimulation and Imaging” and investigates the influence of anodal transcranial direct current stimulation of the brain area linked to processing of the bodily self (right temporoparietal junction) to the subjective strength of virtual embodiment and its main constituents, using within subject experimental design with sham-controlled stimulation.

The sense of embodiment in virtual reality is a strong case of body ownership illusion, effectively allowing humans to experience the ownership of a modified, or a completely different body. However, little is known about the neural mechanisms behind full-body virtual embodiment. Virtual embodiment was studied using questionnaires, accompanied by brain signals gathered using EEG.  Our results suggest that stimulation did not affect the sense of ownership toward the virtual avatar. Borderline strengthening of the perceived sense of agency toward the avatar’s actions was found in the sessions with stimulation.

More information:

https://www.tandfonline.com/doi/abs/10.1080/10447318.2020.1870827

Nissan 3D Car Experience

The recent release of the Looking Glass Portrait has gotten the public excited about personal displays that simulate 3D visuals, but in the public display marketing space, this is an area that has already received a lot of attention. Nissan UK debuted recently an innovative new cube installation in London's Southbank area.

 

Although the installation does not appear to use any of the holographic or augmented reality innovations we regularly cover here, the display nevertheless does appear to give off the effect of a 3D construct moving in the real world. The occasion for the unusual display is the introduction of Nissan's new Qashqai SUV.

More information:

https://next.reality.news/news/nissan-launches-3d-car-experience-streets-london-0384757/

Fractals Can Train AI

Most image-recognition systems are trained using large databases that contain millions of photos of everyday objects, from snakes to shakes to shoes. With repeated exposure, AIs learn to tell one type of object from another. Now researchers in Japan have shown that AIs can start learning to recognize everyday objects by being trained on computer-generated fractals instead.

Generating training data automatically is an exciting trend in machine learning. And using an endless supply of synthetic images rather than photos scraped from the internet avoids problems with existing hand-crafted data sets. Researchers also tried training their AI using other abstract images, including ones produced using Perlin noise and Bezier curves.

More information:

https://www.technologyreview.com/2021/02/04/1017486/fractals-ai-learn-see-more-ethically-bias-imagenet-training/