The role of discrepant retinal motion during walking in the realignment of egocentric space
Bibliographic record
Abstract
Visually guided action relies on accurate perception of egocentric direction. Unfortunately, perceived direction easily becomes misaligned. How is this problem overcome? One theory (R. Held & S. J. Freedman, 1963) is that during self-movement the observer uses the relationship between anticipated and experienced sensory feedback as a source of information to maintain alignment. However, data supporting this theory is equivocal, and recent evidence appears contradictory. We reexamined the issue. We injected an error into perceived visual direction and then assessed realignment after a period of walking toward a target. We manipulated the sensory information available (presence of retinal motion, Experiment 1; presence of peripheral motion, Experiment 2) and found that as the amount of retinal motion was reduced (Experiments 1 and 2), realignment of perceived visual direction decreased. When we then (Experiment 3) removed the discrepancy between anticipated and experienced retinal motion, no realignment was observed. Our results provide evidence that a discrepancy between anticipated and experienced sensory feedback is an important source of information for the alignment of egocentric space, with retinal motion having a particular role in driving a realignment of perceived visual direction.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".