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Record W4410724099 · doi:10.2196/65836

Virtual Reality Gamification of Visual Search, Response Inhibition, and Visual Short-Term Memory Tasks for Cognitive Assessment: Experimental Study

2025· article· en· W4410724099 on OpenAlexvenueno aff
Marios Hadjiaros, Andria Shimi, Kleanthis Kleanthous, Constantinos S. Pattichis, Marios N. Avraamides

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintTerm (time)CognitionCognitive psychologyPsychologyComputer scienceHuman–computer interactionNeuroscienceWorld Wide Web

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.531
Teacher spread0.417 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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