HickStudyVR: A Hick’s Law-Based Information Processing Speed Test in VR for Large Choice Sets
Bibliographic record
Abstract
Hick’s law, a foundational principle in human–computer interaction (HCI), predicts that reaction time increases logarithmically with the number of equally likely choices. While its theoretical value is well established, practical applications in modern HCI remain limited, particularly for accurately estimating individual information processing speed (IPS), defined as the reciprocal of the regression slope. This study introduces HickStudyVR, a virtual reality–based system designed to measure IPS with high precision by minimizing perceptual and motor confounds through gaze-based aiming and controller-based selection. A pilot study with four participants showed strong adherence to Hick’s law (all R2> 0.94), with IPS ranging from 5.00 to 5.56 bit/s. To further validate the system’s design, future work will compare it against traditional 2D layouts to determine whether the VR-based interaction method indeed enhances IPS measurement accuracy. Additional investigations will explore the impact of stimulus type, spatial layout, and gamification on IPS estimation, supporting the development of more robust and generalizable tools for cognitive assessment in immersive environments.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".