LIVE-streaming 3D images: A neuroscience approach to full-body illusions
Bibliographic record
Abstract
Inspired by recent technological advances in the gaming industry, we used capture cards to create and LIVE-stream high quality 3D-images. With this novel technique, we developed a real-life stereoscopic 3D full-body illusion paradigm (3D projection). Unlike previous versions of the full-body illusion that rely upon unwieldy head-mounted displays, this paradigm enables the unobstructed investigation of such illusions with neuroscience methods (e.g., transcranial direct current stimulation, transcranial magnetic stimulation, electroencephalography, and near-infrared spectroscopy) and examination of their neural underpinnings. This paper has three aims: (i) to provide a step-by-step guide on how to implement 3D LIVE-streaming, (ii) to explain how this can be used to create a full-body illusion paradigm; and (iii) to present evidence that documents the effectiveness of our methods (de Boer et al., 2020), including suggestions for potential applications. Particularly significant is the fact that 3D LIVE-streaming is not GPU-intensive and can easily be applied to any device or screen that can display 3D images (e.g., TV, tablet, mobile phone). Therefore, these methods also have potential future clinical and commercial benefits. 3D LIVE-streaming could be used to enhance future clinical observations or educational tools, or potentially guide medical interventions with real-time high-quality 3D images. Alternatively, our methods can be used in future rehabilitation programs to aid recovery from nervous system injury (e.g., spinal cord injury, brain damage, limb loss) or in therapies aimed at alleviating psychosis symptoms. Finally, 3D LIVE-streaming could set a new standard for immersive online gaming as well as augmenting online and mobile experiences (e.g., video chat, social sharing/events).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".