Does Tool Use in Virtual Reality Change the Visual Perception of Extrapersonal and Peripersonal Space?
Bibliographic record
Abstract
When people experience VR for the first time they reach out in an attempt to see their own hands in order to manipulate objects in the virtual environment.In the real world, the space where people are able to physically manipulate objects is referred to as peripersonal space whereas extrapersonal space is any physical area that is beyond the observer's arm's reach.Gamberini, Carlesso, Seraglia, and Craighero (2013) examined how people perform a line bisection task in VR and suggested that a tool's action consequence (i.e., the result of using a tool on an object in a virtual environment) affects how people perceive extrapersonal and peripersonal space.Gamberini et al. reported that when a tool was perceived as a "cutter" because it cut a to-be-bisected virtual line into two segments, the tool effectively extended the boundaries of peripersonal space as it allowed the observer to directly interact with lines that were in extrapersonal space.If, however, the tool was simply a "pointer" (i.e., did not break a virtual line into two segments on a line bisection task), then the separability of peripersonal and extrapersonal space remained intact.Two experiments are reported that attempted to replicate Gamberini et al.'s (2013) tool (pointer vs. cutter) by distance (peripersonal vs. extrapersonal) interaction.In contrast to Gamberini et al.'s findings, tool and distance had additive effects on response time, accuracy, and directional bias in both experiments.There was, however, a robust interaction between line length (short vs. long) and distance in both experiments.It is concluded that line length and distance have more of an effect than tool type on how observers interact with objects in virtual space.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".