Bibliographic record
Abstract
In recent years, videogames have gathered interest in cognitive neuroscience for their potential to study cognition in dynamical and naturalistic contexts.Yet, the complexity of game environments often challenges traditional modeling approaches, and current annotation methods-typically manual or based on modified games-remain labor-intensive and limited in scope.Here, we introduce a flexible and scalable framework using the gym-retro Python library to emulate a classic action-platformer, Shinobi III: Return of the Ninja Master (Sega, 1993), and automatically annotate gameplay events directly from the game's memory states.This setup enables the identification of both player actions (e.g., jumping, hitting) and feedback events (e.g., killing an enemy, being hit), without modifying the game.Four individuals played the videogame for a combined total of 32 hours (>7 hours each) while undergoing functional magnetic resonance imaging (fMRI).Resulting activation maps revealed distributed engagement of visual, motor, executive, and limbic systems, consistent with the cognitive demands of gameplay.Within-subject reproducibility of brain responses across sessions was robust across event types (r ≈ .25-.55), with some consistency observed even for rarer events like HealthLoss.Between-subject correlations were notably lower, reflecting participant-specific neural signatures.Multivoxel pattern analysis showed that brain responses to different in-game events were highly discriminable, with classification accuracy typically around or above 90%, though occasionally dropping to ~40% for less frequent events.These findings demonstrate that automated emulator-based annotations enable robust, interpretable, and scalable mapping of naturalistic cognitive processes using commercial videogames.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.604 | 0.468 |
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".