Open design and validation of a reproducible videogame controller for MRI and MEG
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
Playing video games in a neuroimaging environment is both scientifically promising and technically challenging. Primary among these challenges is the need to use scanner-compatible devices to register player inputs, which limits the type of games that can be comfortably played in a scanner and often reduces the ecological validity of video game tasks. In this paper, we introduce an MRI- and MEG-compatible video game controller that is made exclusively of 3D-printed and commercially available parts, and we release the design files and documentations in the goal of making its production accessible to any research team with minimal engineering resources. In line with the open science philosophy, we made this work accessible under an Open Source Hardware license that aims to promote accessibility and reproducibility. Additionally, we validated the responsiveness and scanner-compatibility of our controller by comparing it to a reference, non-MRI compatible controller, and by assessing the quality of the data recorded with and without the use of the said controller. The analysis of response latencies showed reliable button press accuracies. A higher latency was detected on button releases, both for long and short button presses although this effect was small enough as not to affect gameplay in most situations. Analysis of subject motion during fMRI recordings of various tasks showed that the use of our controller didn’t increase the amount of motion produced. We hope that this tool will stimulate further neuroimaging studies of video games tasks by improving both their accessibility and their validity.
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 | 0.001 | 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 | 0.001 |
| 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