Utilizing Virtual Reality to Improve the Ecological Validity of Clinical Neuropsychology: An fMRI Case Study Elucidating the Neural Basis of Planning by Comparing the Tower of London with a Three-Dimensional Navigation Task
Bibliographic record
Abstract
Virtual reality (VR) was used to create an ecologically valid spatial-navigation task in hand with functional magnetic resonance imaging (fMRI) to articulate the neural basis of planning behavior. A virtual version of a traditional planning measure, the Tower of London, was also developed to ascertain convergent and divergent validity in terms of planning behavior and functional neuroanatomy. This VR-fMRI case study experiment was performed at 3.0 Tesla on a young healthy male subject. The obtained image data suggest both convergent and divergent specificity between the two conditions in terms of location and overall intensity of activation. Overall, the present case study provides supportive evidence that the activity of various brain regions associated with planning tasks is largely modulated by the ecological validity of the measure being used. This finding may extend to all domains of inquiry in neuropsychological research and assessment when deductive conclusions are formulated on the results of neuropsychological test measures that could be considered contrived in nature.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".