Using extended reality to study the experience of presence
Bibliographic record
Abstract
Extended Reality (XR), encompassing various forms of virtual reality (VR) and augmented reality (AR), has become a powerful experimental tool in consciousness research due to its capability to create holistic and immersive experiences of oneself and surrounding environments through simulation. One hallmark of a successful XR experience is when it elicits a strong sense of presence, which can be thought of as a subjective sense of reality of the self and the world. Although XR research has shed light on many factors that may influence presence (or its absence) in XR environments, there remains much to be discovered about the detailed and diverse phenomenology of presence, and the neurocognitive mechanisms that underlie it. In this chapter, we analyse the concept of presence and relate it to the way in which humans may generate and maintain a stable sense of reality during both natural perception and virtual experiences. We start by reviewing the concept of presence as developed in XR research, covering both factors that may influence presence and potential ways of measuring presence. We then discuss the phenomenological characteristics of presence in human consciousness, drawing on clinical examples where presence is disturbed. Next, we describe two experiments using XR that investigated the effects of sensorimotor contingency and affordances on a specific form of presence related to the sense of objects as really existing in the world, referred to as ‘objecthood’. We then go beyond perceptual presence to discuss the concept of 'conviction about reality', which corresponds to people's beliefs about the reality status of their perceptual experiences. We finish by exploring how the novel XR method of ‘Substitutional Reality’ can allow experimental investigation of these topics, opening new experimental directions for studying presence beyond the ‘as-if’ experience of fully simulated environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".