Immersive Virtual Reality–Based Methods for Assessing Executive Functioning: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Neuropsychological assessments traditionally include tests of executive functioning (EF) because of its critical role in daily activities and link to mental disorders. Established traditional EF assessments, although robust, lack ecological validity and are limited to single cognitive processes. These methods, which are suitable for clinical populations, are less informative regarding EF in healthy individuals. With these limitations in mind, immersive virtual reality (VR)-based assessments of EF have garnered interest because of their potential to increase test sensitivity, ecological validity, and neuropsychological assessment accessibility. OBJECTIVE: This systematic review aims to explore the literature on immersive VR assessments of EF focusing on (1) EF components being assessed, (2) how these assessments are validated, and (3) strategies for monitoring potential adverse (cybersickness) and beneficial (immersion) effects. METHODS: EBSCOhost, Scopus, and Web of Science were searched in July 2022 using keywords that reflected the main themes of VR, neuropsychological tests, and EF. Articles had to be peer-reviewed manuscripts written in English and published after 2013 that detailed empirical, clinical, or proof-of-concept studies in which a virtual environment using a head-mounted display was used to assess EF in an adult population. A tabular synthesis method was used in which validation details from each study, including comparative assessments and scores, were systematically organized in a table. The results were summed and qualitatively analyzed to provide a comprehensive overview of the findings. RESULTS: The search retrieved 555 unique articles, of which 19 (3.4%) met the inclusion criteria. The reviewed studies encompassed EF and associated higher-order cognitive functions such as inhibitory control, cognitive flexibility, working memory, planning, and attention. VR assessments commonly underwent validation against gold-standard traditional tasks. However, discrepancies were observed, with some studies lacking reported a priori planned correlations, omitting detailed descriptions of the EF constructs evaluated using the VR paradigms, and frequently reporting incomplete results. Notably, only 4 of the 19 (21%) studies evaluated cybersickness, and 5 of the 19 (26%) studies included user experience assessments. CONCLUSIONS: Although it acknowledges the potential of VR paradigms for assessing EF, the evidence has limitations. The methodological and psychometric properties of the included studies were inconsistently addressed, raising concerns about their validity and reliability. Infrequent monitoring of adverse effects such as cybersickness and considerable variability in sample sizes may limit interpretation and hinder psychometric evaluation. Several recommendations are proposed to improve the theory and practice of immersive VR assessments of EF. Future studies should explore the integration of biosensors with VR systems and the capabilities of VR in the context of spatial navigation assessments. Despite considerable promise, the systematic and validated implementation of VR assessments is essential for ensuring their practical utility in real-world applications.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".