bmlTUX: Design and control of experiments in virtual reality and beyond
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
Advances in virtual reality (VR) technology have made it a valuable new tool for vision and perception researchers. Coding VR experiments from scratch can be difficult and time-consuming so researchers rely on software such as Unity game engine to create and edit virtual scenes. However, Unity lacks built-in tools for controlling experiments. Existing third-party add-ins require complicated scripts to define experiments. This can be difficult and requires advanced coding knowledge, especially for multifactorial experimental designs. In this paper, we describe a new free and open-source tool called the BiomotionLab Toolkit for Unity Experiments (bmlTUX) that provides a simple interface for controlling experiments in Unity. In contrast to existing tools, bmlTUX provides a graphical interface to automatically handle combinatorics, counterbalancing, randomization, mixed designs, and blocking of trial order. The toolbox works “out-of-the-box” since simple experiments can be created with almost no coding. Furthermore, multiple design configurations can be swapped with a drag-and-drop interface allowing researchers to test new configurations iteratively while maintaining the ability to easily revert to previous configurations. Despite its simplicity, bmlTUX remains highly flexible and customizable, catering to coding novices and experts alike.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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