EPELI: a novel virtual reality task for the assessment of goal-directed behavior in real-life contexts
Bibliographic record
Abstract
A recently developed virtual reality task, EPELI (Executive Performance in Everyday LIving), quantifies goal-directed behavior in naturalistic conditions. Participants navigate a virtual apartment, performing household chores given by a virtual character. EPELI aims to tap attention, executive function, and prospective memory. To ensure its applicability to further research and clinical work and to study its relationship to relevant background factors, we examined several key properties of EPELI in 77 typically developing 9-13-year-old children. These included EPELI's internal consistency, age and gender differences, sensitivity to gaming experience, head-mounted display (HMD) type, and verbal recall ability, as well as its relationships with parent-rated everyday executive problems. Of the eight EPELI measures, the following six showed acceptable internal consistency: task and navigation efficacy, number of correctly performed tasks and overall actions, time monitoring, and controller movement. Some measures were associated with age, gender, or verbal encoding ability. Moreover, EPELI performance was associated with parent-rated everyday executive problems. There were no significant associations of gaming background, task familiarity, or HMD type with the EPELI measures. These results attest to the reliability and ecological validity of this new virtual reality tool for the assessment of attention, executive functions, and prospective memory in children.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".