Integration of egocentric and allocentric information during memory-guided reaching to images of a natural environment
Bibliographic record
Abstract
When interacting with our environment we generally make use of egocentric and allocentric object information by coding object positions relative to the observer or relative to the environment, respectively. Bayesian theories suggest that the brain integrates both sources of information optimally for perception and action. However, experimental evidence for egocentric and allocentric integration is sparse and has only been studied using abstract stimuli lacking ecological relevance. Here, we investigated the use of egocentric and allocentric information during memory-guided reaching to images of naturalistic scenes. Participants encoded a breakfast scene containing six objects on a table (local objects) and three objects in the environment (global objects). After a 2 s delay, a visual test scene reappeared for 1 s in which 1 local object was missing (= target) and of the remaining, 1, 3 or 5 local objects or one of the global objects were shifted to the left or to the right. The offset of the test scene prompted participants to reach to the target as precisely as possible. Only local objects served as potential reach targets and thus were task-relevant. When shifting objects we predicted accurate reaching if participants only used egocentric coding of object position and systematic shifts of reach endpoints if allocentric information were used for movement planning. We found that reaching movements were largely affected by allocentric shifts showing an increase in endpoint errors in the direction of object shifts with the number of local objects shifted. No effect occurred when one local or one global object was shifted. Our findings suggest that allocentric cues are indeed used by the brain for memory-guided reaching towards targets in naturalistic visual scenes. Moreover, the integration of egocentric and allocentric object information seems to depend on the extent of changes in the scene.
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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".