Using Augmented Reality to Assess the Role of Intuitive Physics in the Water-Level Task
Bibliographic record
Abstract
The “Water Level Task” (WLT) is a classic cognitive task that assesses an individual’s ability to draw the water level in a tilted container. Most of the existing research has used 2D imagery and shown that adults struggle with the task. Our research investigates if the use of augmented reality (AR) improves an individual’s performance by engaging embodied interaction and natural interaction with the world, thus taking advantage of their “intuitive physics.” We created a traditional online WLT to recruit low- and high-scoring participants for the AR experiment. Using a HoloLens2 AR headset, we created two containers half-filled with water. One of the simulations featured a water surface that did not remain horizontal when the container was tilted, while in the other simulation, the water surface remained level. Participants were able to interact with the containers and were asked to indicate which simulation looked more natural. Our results revealed that individuals prone to errors in the 2D version of the task were more likely to make errors in the AR version, indicating that misconceptions about water orientation persist even in a more natural setting. However, people’s perceptions of the natural orientation of water differed in 2D and AR settings, suggesting that different perceptual and cognitive factors were involved in participants’ intuitive understanding of the natural orientation of water in the two settings. Additionally, we found that participants were insensitive to minor tilts of the water surface. Our study highlights the potential benefits of using AR to create more realistic and interactive virtual environments, which provides a basis for further study of intuitive physics and how humans interact with physical 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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".