The neural basis of Tetris gameplay: implicating the role of visuospatial processing
Bibliographic record
Abstract
Abstract Tetris is not only a widely used entertaining computer game, but has been used as a component in emerging psychological interventions targeting dysfunctional mental imagery, e.g., intrusive memories and imagery-based cravings. However, little is known about the neurobiological mechanisms underlying these interventions. Tetris gameplay has been hypothesized to disrupt dysfunctional mental imagery (e.g., imagery-based intrusive memories of adverse events) and cravings (e.g., substance use) by taxing visuospatial working memory. In line with this, the present study aimed to characterize brain areas involved in the visuospatial aspects of Tetris gameplay, by controlling for motor activity (button presses) and using gameplay instructions emphasizing mental rotation. Participants (N = 28) received mental rotation instructions and thereafter either played Tetris, or only pressed buttons as if playing Tetris (motor activity), while undergoing functional magnetic resonance imaging. Tetris gameplay (when using mental rotation instructions and controlling for motor activity) robustly activated brain areas located in the ventral and dorsal stream, with maximum peak activation in the inferior and mid temporal gyrus. To the best of our knowledge, this is the first study to characterize brain areas specifically associated with the visuospatial aspects of Tetris gameplay, by controlling for motor activity and when using mental rotation instructions. Results demonstrate that engaging in Tetris gameplay recruits an extensive brain circuitry previously tied to visuospatial processing. Thus, findings are consistent with the use of Tetris as an imagery-competing task as one of several components of emerging interventions targeting dysfunctional mental imagery.
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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 | 0.000 | 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) | 0.002 | 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 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".