MétaCan
Menu
Back to cohort
Record W3126775743 · doi:10.3389/fnhum.2021.628818

The Role of Virtual Reality in Screening, Diagnosing, and Rehabilitating Spatial Memory Deficits

2021· article· en· W3126775743 on OpenAlexaff
Miles Jonson, Sinziana Avramescu, Derek Chen, Fahad Alam

Bibliographic record

VenueFrontiers in Human Neuroscience · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHumber River Regional HospitalHealth Sciences CentreUniversity of WaterlooUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsVirtual realityMemory impairmentRecallNeuropsychologyCognitive psychologySpatial memoryComputer scienceCognitionSpatial cognitionPsychologyHuman–computer interactionWorking memoryNeuroscience

Abstract

fetched live from OpenAlex

Impairment of spatial memory, including an inability to recall previous locations and navigate the world, is often one of the first signs of functional disability on the road to cognitive impairment. While there are many screening and diagnostic tools which attempt to measure spatial memory ability, they are often not representative of real-life situations and can therefore lack applicability. One potential solution to this problem involves the use of virtual reality (VR), which immerses individuals in a virtually-simulated environment, allowing for scenarios more representative of real-life without any of the associated risks. Here, we review the evidence surrounding the use of VR for the screening and diagnosis of spatial memory impairments, including potential limitations and how it compares to standard neuropsychological tests. We will also discuss the evidence regarding the potential use of VR in the rehabilitation of spatial memory deficits, which has not been well studied, but which could be game-changing if proven successful.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.308
Teacher spread0.290 · 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 designObservational
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".

Quick stats

Citations37
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Human NeuroscienceSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207