Design of a Virtual Reality Navigational (VRN) experiment for assessment of egocentric spatial cognition
Bibliographic record
Abstract
Virtual reality (VR) experiments are commonly used to assess human brain functions. We orient ourselves in an environment by computing precise self-to-object spatial relations (egocentric orientation) as well as object-to-object spatial relations (allocentric orientation). Egocentric orientation involves cues that depend on the position of the observer (i.e. left-right, front-behind), whereas allocentric orientation is maintained through the use of environmental features such as landmarks. As such, allocentric orientation involves short-term memory, whereas egocentric orientation does not. This paper presents a Virtual Reality Navigational (VRN) experiment specifically designed to assess egocentric spatial cognition. The design aimed to minimize the effect of spatial cues or landmarks for human navigation in a naturalistic VR environment. The VRN experiment designed for this study, called the Virtual House, is a symmetric three story cubic building, with 3 windows on each side on every floor, and one entrance on each side of the building. In each trial, a window is marked by a pseudo-random sequence as the objective. The marked window is shown to the participant from an outdoor view. The task is to reach the objective window using the shortest path through the building. The experiment entails 2 sets of 8 trials to cover all possibilities. The participants' performance error is measured by the difference between their traversed distance trajectory and the shortest natural distance (calculated using the VR engine), normalized by the shortest distance, in each trial. Fifty-two cognitively healthy adults participated in the study. The results show no learning effect during the 16 trails, implying that the experiment does not rely on short-term memory. Furthermore, the subjects' normalized performance error showed an almost linear increase with age, implying that egocentric spatial cognition ability declines with age.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".