Intrinsic Reference System in Implicit Spatial Learning: Evidence from Contextual Cueing Paradigm
Bibliographic record
Abstract
It has been proposed (Mou etal, 2004) that for memory of a scene, the structure of the object layout or environment could be used to form a reference direction which aids spatial learning. Here we evaluated the effect of various indicators of reference direction in facilitating implicit spatial learning. We used a contextual cueing paradigm where repeated configurations of random elements induce faster search performance than novel configurations. We examined search behavior in a computer rendered illustrations of a 3D scene. Human 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 chairs provided orientation information of the objects and the scene (with coherent orientations for all the chairs). Observers searched for and identified a target positioned on the seat of a stool or a chair. The learning effect was indicated by (1) the magnitude of contextual cueing effect in a given block and (2) number of learning blocks needed to reach significant difference between repeated and novel scene. We found greater learning effect (i) when the orientation of all chairs was coherent compared to random (ii) for the chair scene with coherent orientation compared to the stool scene. However greater learning effect was not found when we introduced environmental cue for reference direction (parallel lines on the floor) to the stool scene. The results indicted that implicit spatial learning can be facilitated by the availability of intrinsic axis provided by individual objects in the scene but not from external environmental indicators. 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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".