OpenMaze: An open-source toolbox for creating virtual navigation experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Incorporating 3D virtual environments into psychological experiments offers an innovative solution for balancing experimental control and ecological validity. Their flexible application to virtual navigation experiments, however, has been limited because accessible development tools best support only a subset of desirable task design features. We created OpenMaze, an open-source toolbox for the Unity game engine, to overcome this barrier. OpenMaze offers researchers the ability to conduct a wide range of first-person spatial navigation experiment paradigms in fully customized 3D environments. Crucially, because all experiments are defined using human-readable configuration files, our toolbox allows even those with no prior coding experience to build bespoke tasks. OpenMaze is also compatible with a variety of input devices and operating systems, broadening its possible applications. To demonstrate its advantages and limitations, we review and contrast other available software options before providing an overview of our design objectives and walking the reader through the process of building an experiment in OpenMaze.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it