Impact of Visual Game-Like Features on Cognitive Performance in a Virtual Reality Working Memory Task: Within-Subjects Experiment
Bibliographic record
Abstract
BACKGROUND: Although the pursuit of improved cognitive function through working memory training has been the subject of decades of research, the recent growth in commercial adaptations of classic working memory tasks in the form of gamified apps warrants additional scrutiny. In particular, the emergence of virtual reality as a platform for cognitive training presents opportunities for the use of novel visual features. OBJECTIVE: This study aimed to add to the body of knowledge regarding the use of game-like visual design elements by specifically examining the application of two particular visual features common to virtual reality environments: immersive, colorful backgrounds and the use of 3D depth. In addition, electroencephalography (EEG) data were collected to identify potential neural correlates of any observed changes in performance. METHODS: A simple visual working memory task was presented to participants in several game-like adaptations, including the use of colorful, immersive backgrounds and 3D depth. The impact of each adaptation was separately assessed using both EEG and performance assessment outcomes and compared with an unmodified version of the task. RESULTS: Results suggest that although accuracy and reaction time may be slightly affected by the introduction of such game elements, the effects were small and not statistically significant. Changes in EEG power, particularly in the beta and theta rhythms, were significant but failed to correlate with any corresponding changes in performance. Therefore, they may only reflect cognitive changes at the perceptual level. CONCLUSIONS: Overall, the data suggest that the addition of these specific visual features to simple cognitive tasks does not appear to significantly affect performance or task-dependent cognitive load.
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How this classification was reachedexpand
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".