Editorial: Human spatial perception, cognition, and behaviour in extended reality
Bibliographic record
Abstract
Editorial on the Research Topic Human spatial perception, cognition, and behaviour in extended realityThe concept of eXtended Reality (XR) has recently become a staple of the mainstream discussion of technological innovation.XR is an umbrella term for digital technologies simulating sensory experiences in real or imagined environments, such as Augmented Reality (AR) and Virtual Reality (VR).These technologies replace or augment the physical world with a mediated version of what designers want the world to look like.To that end, XR refers to a wide range of hardware and software platforms, from partial sensory inputs to fully immersive bodysuits, to make users believe they are in another environment or interacting with something or someone that does not exist in reality.Its inherent spatiality, the freedom of design, and full control over experiences make XR ideally suited for investigating spatial perception and cognition.Technologies that use space as input and output, such as motion controllers and position sensors, fall under spatial computing.At its core, spatial computing defines how we explore and interact with our surroundings (Pangilinan et al., 2019).In an XR environment, the user's position and relationship to objects within that environment are synchronized to maintain a unified experience between different modalities (virtual-real or virtual-virtual).The rich visualspatial cues that are accessible from an egocentric, embodied perspective offer a source of stimulation for users to think spatially and guide their actions.By replicating real-life scenarios and sensory input, XR has the potential to support, facilitate, or develop spatial thinking (i.e., thinking in, about, and with space) in a comparable manner to everyday activities but with the freedom of infinite realities.XR environments are highly flexible and programmable, opening up research opportunities for the development of novel methods to understand human cognition and behaviour.It should be noted that the quality of the experience does not rest on whether it follows the physical laws of the real world.A digital representation that is entirely different from the physical one may still be perceived to be plausible as long as its spatial features and object movements and interactions comply with internally consistent rules that are reasonable for the presented scene (referred to as coherence by Skarbez et al., 2021).
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.035 | 0.022 |
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".