Dynamic visual information facilitates object recognition from novel viewpoints
Bibliographic record
Abstract
Normally, people have difficulties recognizing objects from novel as compared to learned views, resulting in increased reaction times and errors. Recent studies showed, however, that this "view-dependency" can be reduced or even completely eliminated when novel views result from observer's movements instead of object movements. This observer movement benefit was previously attributed to extra-retinal (physical motion) cues. In two experiments, we demonstrate that dynamic visual information (that would normally accompany observer's movements) can provide a similar benefit and thus a potential alternative explanation. Participants performed sequential matching tasks for Shepard-Metzler-like objects presented via head-mounted display. As predicted by the literature, object recognition performance improved when view changes (45° or 90°) resulted from active observer movements around the object instead of object movements. Unexpectedly, however, merely providing dynamic visual information depicting the viewpoint change showed an equal benefit, despite the lack of any extra-retinal/physical self-motion cues. Moreover, visually simulated rotations of the table and hidden target object (table movement condition) yielded similar performance benefits as simulated viewpoint changes (scene movement condition). These findings challenge the prevailing notion that extra-retinal (physical motion) cues are required for facilitating object recognition from novel viewpoints, and highlight the importance of dynamic visual cues, which have previously received little attention.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".