Humans sense by touch the location of objects that roll in handheld containers
Bibliographic record
Abstract
Humans use active touch to gain behaviourally relevant information from their environment, including information about contained objects. Although most common, the perceptual basis of interacting with containers remains largely unexplored. The first aim of this study was to determine how accurately people can sense, by touch only, the location of a contained rolling object. Experiment 1 used tubes containing physical balls and demonstrated a considerable degree of accuracy in estimating the rolled distance. The second aim was to identify the relative effectiveness of the various available physical cues. Experiment 2 employed virtual reality technology to present, in isolation and in various combinations, the constituent haptic cues produced by a rolling ball, which are, the mechanical noise during rolling, the jolts from an impact with an internal wall, and the intensity and timing of the jolts resulting from elastic bounces. The rolling noise was of primary importance to the perceptual estimation task suggesting that the implementation of the laws of motion is based on an analysis of the ball's movement velocity. Although estimates became more accurate when the rolling and impact cues were combined, they were not necessarily more precise. The presence of elastic bounces did not affect performance.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".