On a Moving Target Selection Model in Virtual Reality Based on Decision Trees
Bibliographic record
Abstract
Virtual reality (VR) systems have been used in various industries.Highly effective humancomputer interaction (HCI) and natural HCI experience have become the key indicators for evaluating a VR system, where target selection is the key for interaction efficiency and experience.In this paper, we propose a moving target selection prediction model, based on the probabilistic Fitts's Law and in combination with decision trees, for moving target selection in VR systems.Firstly, we verified the feasibility of predicting the user intention based on the size and distance of moving targets in VR scenarios through two selection task experiments with a sphere as the target.Then, also through two experiments, we proposed an improved moving target prediction model by factoring in head posture with target size and distance and taking into account the influence of head orientation.Based on the decision tree algorithm, we calculated its prediction accuracy and compared it with the distance scoring function.The results show that the improved prediction model has significantly better accuracy and can accurately predict the user's moving target selection intention in a VR system.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".