Active and passive object recognition in a virtual environment
Bibliographic record
Abstract
In an earlier report (Harman, Humphrey, and Goodale, 1999), we demonstrated that observers who actively rotated three-dimensional novel objects on a computer screen by means of a track ball later showed faster visual recognition of these objects than did observers who had passively viewed exactly the same sequence of images of these virtual objects. In the present experiment, we report evidence that under very different viewing and manipulation conditions, active exploration of objects facilitated object recognition compared to passive viewing. Observers studied objects while immersed in a virtual reality CAVE (fakespace). For viewing of half of the study objects, observers were able to rotate the objects in a way that mapped onto normal object movement very naturally. Specifically, to rotate the virtual objects observers rotated a real block that they held in their hands. Rotations of the real block about any axis in 3-D space were mapped onto identical rotations of the virtual object. The other half of the study objects were viewed passively without any control of the rotations. During a test session, participants recognized three-dimensional static images faster if they had actively rotated those objects during a study session than if they had simply viewed them passively. This is the first study to demonstrate that active manipulation of novel objects in virtual reality environments facilitates subsequent recognition.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".