Spatial updating and spatial properties in scene recognition
Bibliographic record
Abstract
When an observer's viewpoint of an object layout changes as a result of the movement of the layout itself, recognition performance is often poor. When the viewpoint change results from the observer's own movement, visual and non-visual information may serve to update the spatial representation, resulting in better recognition performance. The purpose of the current experiment was to evaluate the effects of non-visual updating on scene recognition while systematically manipulating the type of spatial information available (object position, object identity, or both). Subjects (Ss) learned the positions and/or identities of seven objects on a rotating table. They were subsequently presented with the layout from a novel viewpoint (due to either a table rotation or to Ss' own movement around the table) and made a same/different judgment. The results demonstrated that performance was faster and more accurate when Ss moved to a new viewpoint compared to situations in which they remained stationary while the table rotated. Further, Ss were more accurate when provided with position information combined with identity information compared to situations in which each was provided in isolation. In addition, males consistently outperformed females in all conditions except for the situations when Ss remained stationary and were provided with identity information alone, in which case females outperformed males. This pattern of results changed however when subjects were required to move, in which case, males again outperformed females. This finding supports previous evidence suggesting that females excel in tasks that have a higher verbal component (identity) compared to tasks that relate more directly to spatial features (position), in which case males excel. Further, the current results indicate that specific spatial properties have dissociable effects, suggesting that independent mechanisms are involved in the encoding and updating of spatial representations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".