Scene Configuration and Object Reliability Affect the Use of Allocentric Information for Memory-Guided Reaching
Bibliographic record
Abstract
Previous research has shown that egocentric and allocentric information is used for coding target locations for memory-guided reaching movements. Especially, task-relevance determines the use of objects as allocentric cues. Here, we investigated the influence of scene configuration and object reliability as a function of task-relevance on allocentric coding for memory-guided reaching. For that purpose, we presented participants images of a naturalistic breakfast scene with five objects on a table and six objects in the background. Six of these objects served as potential reach-targets (= task-relevant objects). Participants explored the scene and after a short delay, a test scene appeared with one of the task-relevant objects missing, indicating the location of the reach target. After the test scene vanished, participants performed a memory-guided reaching movement toward the target location. Besides removing one object from the test scene, we also shifted the remaining task-relevant and/or task-irrelevant objects left- or rightwards either coherently in the same direction or incoherently in opposite directions. By varying object coherence, we manipulated the reliability of task-relevant and task-irrelevant objects in the scene. In order to examine the influence of scene configuration (distributed vs. grouped arrangement of task-relevant objects) on allocentric coding, we compared the present data with our previously published data set (Klinghammer et al., 2015). We found that reaching errors systematically deviated in the direction of object shifts, but only when the objects were task-relevant and their reliability was high. However, this effect was substantially reduced when task-relevant objects were distributed across the scene leading to a larger target-cue distance compared to a grouped configuration. No deviations of reach endpoints were observed in conditions with shifts of only task-irrelevant objects or with low object reliability irrespective of task-relevancy. Moreover, when solely task-relevant objects were shifted incoherently, the variability of reaching endpoints increased compared to coherent shifts of task-relevant objects. Our results suggest that the use of allocentric information for coding targets for memory-guided reaching depends on the scene configuration, in particular the average distance of the reach target to task-relevant objects, and the reliability of task-relevant allocentric information.
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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 | 0.007 |
| 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 | 0.001 |
| 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".