Virtual Reality Gamification of Visual Search, Response Inhibition, and Visual Short-Term Memory Tasks for Cognitive Assessment: Experimental Study
Bibliographic record
Abstract
Background: Cognitive tasks are foundational tools in psychology and neuroscience for studying attention, perception, and memory. However, they typically employ simple or artificial stimuli and require numerous repetitive trials, which can adversely affect participant engagement and ecological validity. Objective: This study investigated whether gamified versions of 3 established cognitive tasks, namely, the Visual Search task (attention), the Whack-the-Mole task (response inhibition), and the Corsi block-tapping test (visual short-term memory), replicate the typical patterns of results reported for their traditional counterparts. It also examined whether the method of administration-in immersive virtual reality (VR) versus desktop computer, and in the laboratory versus at home-influences performance. Methods: Seventy-five participants (male=24, female=51; age range 18-35 years; mean 23.15, SD 4.38 years) were randomly assigned to 1 of 3 administration conditions (n=25 each). In the VR-Lab condition, participants completed the tasks in immersive VR within the laboratory; in the Desktop-Lab condition, they completed the tasks on a 2D desktop screen in the laboratory; and in the Desktop-Remote condition, participants completed the tasks on their personal computers at home. All participants completed the same gamified tasks while seated, entering responses with either a mouse or a VR controller, depending on the condition. Results: The results obtained from these gamified tasks across all 3 administration conditions replicated the typical performance patterns observed with their traditional counterparts, despite using more ecologically valid stimuli and fewer trials. However, administration modality did influence certain performance measures, particularly reaction times (RTs) and task efficiency. Specifically, in the Visual Search task, RTs were significantly faster in the VR-Lab condition (mean 1.24 seconds) than in the Desktop-Lab (mean 1.49 seconds; P<.001) and Desktop-Remote (mean 1.44 seconds; P=.008) conditions. In the Whack-the-Mole task, no significant group differences emerged in d' scores (VR-Lab: mean 3.79, Desktop-Remote: mean 3.75, Desktop-Lab: mean 3.62; P=.49), but RTs were slower in the Desktop-Remote condition (mean 0.64 seconds) than in the VR-Lab (mean 0.41 seconds; P<.001) and Desktop-Lab (mean 0.48 seconds; P<.001) conditions. For the Corsi block-tapping test, no significant group differences in span scores were found (VR-Lab: mean 5.48, Desktop-Lab: mean 5.68, and Desktop-Remote: mean 5.24; P=.24). Finally, a significant positive correlation was observed between RTs for Hits in the Whack-the-Mole task and feature search trials in the Visual Search task (r=0.24; P=.04). Conclusions: Gamified cognitive tasks administered in VR replicated established behavioral patterns observed with their traditional versions while improving ecological validity and reducing task duration. Administration modality had limited effects on overall outcomes, although RTs were slower in remote settings. These findings support the feasibility of using gamified VR tasks for scalable and ecologically valid cognitive assessment. Overall, the study underscores the potential of VR to increase participant engagement and enrich cognitive research through more immersive and motivating testing environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".