The (un)usefulness of interactive exploration in building 3D- mental representations.
Bibliographic record
Abstract
The generation of mental representations from visual images is crucial in 3-D object recognition. In two experiments, thirty-six participants were divided into a low, middle, and high visuospatial ability (VSA) group, which was determined by Vandenberg and Kuse's MRT-A test (1978 Perception and Motor Skills 47 599 - 601). In the experiments, the influence of four types of exploration (none, passive 2-D, passive 3-D, and interactive 3-D) on building 3-D mental representations was investigated. First, 24 simple and 24 complex objects (consisting of respectively 3 and 5 geons (Biederman, 1987 Psychological Review 94 115 - 147) were explored and, subsequently, tested through a mental rotation test. Results revealed that participants with a low VSA benefit from interactive exploration of objects opposed to passive exploration. This refines James et al's findings (2001 Canadian Journal of Experimental Psychology 55 111 - 120), who reported a general increased performance with interactive as compared to passive exploration. Our results underline that individual differences are of key importance when investigating human's visuospatial system or visualisation techniques.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".