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Record W6925294119 · doi:10.17605/osf.io/42uhj

Virtual Reality Attention Task: Effectiveness in Predicting ADHD and Establishing Validity in an Adult Sample

2023· other· en· W6925294119 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2023
Typeother
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual realityEcological validityNeuropsychologyImmersion (mathematics)FeelingPopulationNeuropsychological assessmentReality testing

Abstract

fetched live from OpenAlex

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder with symptomatic ADHD suggested to be prevalent within 6.76% an adult population (Song et al., 2021). Research has identified an association between ADHD in adults and maintaining attention, with a specific focus on impaired sustained attention (Avisar, 2022). Sustained attention refers to prolonged focus on a task, and is measured through computerised neuropsychological tasks, such as the Continuous Performance Test (CPT; Fortenbaugh et al., 2017; Conners et al., 2003). One of the paradigms represented by the CPT is assessed through inhibition tasks, in which non-target stimuli are detected and target stimuli are ignored (Servera & Cardo, 2006). The CPT is incorporated clinically in the assessment of adult ADHD by measuring reaction time (RT) of correct responses, variability of RT, omission and commission errors (Conners et al., 2003). Despite the CPT being highly established, it possesses low ecological validity. Thus, performance on this test may not accurately represent attention functioning in every day life. To overcome this limitation, Virtual Reality (VR) has been implemented within clinical research over recent decades with immersive properties increasing ecological validity and optimising therapeutic outcomes (Bhugra et al., 2017). Immersion refers to the technical aspects of the virtual environment that increase feelings of 'presence' (Wilkinson et al., 2021). Research has identified that immersion falls along two dimensions, VR presence and VR sickness, with higher levels of VR presence and lower levels of VR sickness increasing performance (Maneuvrier et al., 2020). Immersive VR environments have been researched within the assessment of psychiatric disorders, with findings suggesting that VR attention tasks can predict ADHD in adults, as well as other highly prevalent disorders such as depression and anxiety (Ohnishi et al., 2019; Voinescu et al., 2021). Due to high comorbidity rates between these disorders, it is important than the VR attention task is able to predict ADHD after controlling for both depression and anxiety. Moreover, research on VR and ADHD has a primary focus on children and adolescent samples, with findings identifying the clinical utility of VR within assessment (Parsons et al., 2019). The small body of research currently available with a focus on ADHD in adults and VR assessment has reported similar findings providing evidence for the need for further research with an adult sample (Areces et al., 2019). In light of this information, validation of a novel VR task is required to ensure the clinical utility of the environment in the assessment of sustained, selective, divided and alternating attention in adults. This will be achieved through comparing the VR attention task results to the CPT to establish convergent validity, and the Montreal Cognitive Assessment/Victoria Stroop Task to establish divergent validity. Despite the CPT assessing sustained attention only, all four attention types within the VR task require validation through correlation with the CPT outcomes. Additionally, this means all four attention types will be compared to the CPT to determine effectiveness in predicting ADHD.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.020
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.083
GPT teacher head0.412
Teacher spread0.330 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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