Memory of a Scene Following Viewpoint Change Caused by Viewer Locomotion
Bibliographic record
Abstract
Viewpoint changes caused by an observer's locomotion lead to better scene recognition performance than that following equivalent movement of the scene. In studies testing change detection of a tabletop scene, the benefit of observer locomotion has been attributed to spatial updating through body-based information (Simons & Wang, 1998), or knowledge of a change of the reference direction gained through locomotion (Mou et al, 2009). In the current study, six experiments were performed using a similar paradigm but measuring both accuracy and reaction time of the response. Experiment 1-4 examined the effect of an external visual indicator introduced during the testing phase signalling the learning direction (as in Mou et al, 2009). Experiments 1 and 2 compared performance in the locomotion condition with table rotation condition, Experiments 3 and 4 compared performance in a short walking condition with conditions where body-based information was not reliable (disorientation or walking a long, curved path). Experiments 5 and 6 examined the effect of intrinsic reference direction information by aligning the orientation of the dominant axis of all the objects in the scene. Experiments 1-4 show that even with the visual indicator, performance in conditions that lacked normal locomotion was still significantly worse than that in the observer locomotion condition and that the visual indicator did not improve performance at all. However, in Experiments 5 and 6, performance for a scene composed of objects with consistent orientations was: (1) better than that for a scene composed to objects with random orientation and (2) comparable to that in observer locomotion condition. Overall we show that the body-based information in observer locomotion provides the most prominent information, while knowledge of a reference direction is useful but might only be effective in limited scenarios, such as scenes with an obvious and dominant orientation. Meeting abstract presented at VSS 2014
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".