Manipulating and recognizing virtual objects: Where the action is.
Bibliographic record
Abstract
In an earlier report (Harman, Humphrey, & Goodale, 1999), we demonstrated that observers who actively rotated three-dimensional novel objects on a computer screen 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 Experiment 1 of the present study we showed that compared to passive viewing, active exploration of three-dimensional object structure led to faster performance on a "mental rotation" task involving the studied objects. In addition, we examined how much time observers concentrated on particular views during active exploration. As we found in the previous report, they spent most of their time looking at the "side" and "front" views ("plan" views) of the objects, rather than the three-quarter or intermediate views. This strong preference for the plan views of an object led us to examine the possibility in Experiment 2 that restricting the studied views in active exploration to either the plan views or the intermediate views would result in differential learning. We found that recognition of objects was faster after active exploration limited to plan views than after active exploration of intermediate views. Taken together, these experiments demonstrate (1) that active exploration facilitates learning of the three-dimensional structure of objects, and (2) that the superior performance following active exploration may be a direct result of the opportunity to spend more time on plan views of the object.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".