Viewpoint Independence in Implicit Scene Learning Revealed in a Contextual Cueing Paradigm
Bibliographic record
Abstract
For a 3D scene, whether implicit spatial learning in a contextual cueing paradigm can be transferred to a different viewpoint has not been well studied (but see Chua and Chun, 2003). In this study we examined this question using a computer rendered illustration of 3D scenes. Participants viewed a scene consisted of an array of either different "stools" or different "chairs" randomly positioned on the ground and in their normal upright orientation. The stools were made of various circular structures so that the side view of the stool appeared to be the same from different viewpoints. The chairs were created by adding a "back" portion on top of the stools. The back of the chairs provided orientation information of the objects and the scene (with all the chairs having a coherent orientation). Observers searched for and identified a target positioned on the seat of a stool or a chair. Significant contextual cuing effect was found in the training session, with faster RTs in the repeated condition than in the novel condition. In the testing session, when the viewpoints of the scene (1) remained the same, or (2) switched 45 degree for the chair scene, the contextual cueing effect was comparable to that at the end of training phase. However, for the stool scene, after 45 degree view shift, the contextual cuing effect diminished. Our results suggest that when the scene contained clear indication of the viewpoint change (from individual chairs), the spatial relation learned during training can be mentally transformed to a new viewpoint. When such indication of view change is missing, the learning can not be transferred to the new viewpoint. Moreover the ordinal information between different objects alone (as in the stool scene) would not be able to explain the viewpoint independence found in the chair scene. Meeting abstract presented at VSS 2014
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".