Piloting systems reset path integration systems during position estimation.
Bibliographic record
Abstract
During locomotion, individuals can determine their positions with either idiothetic cues from movement (path integration systems) or visual landmarks (piloting systems). This project investigated how these 2 systems interact in determining humans' positions. In 2 experiments, participants studied the locations of 5 target objects and 1 single landmark. They walked a path after the targets and the landmark had been removed and then replaced the targets at the end of the path. Participants' position estimations were calculated based on the replaced targets' locations (Mou & Zhang, 2014). In Experiment 1, participants walked a 2-leg path. The landmark reappeared in a different location during or after walking the second leg. The results showed that participants' position estimations followed idiothetic cues in the former case, but the displaced landmark in the latter case. In Experiment 2, participants saw the displaced landmark when they reached the end of the second leg and then walked a third leg without the view of the landmark. Participants were asked or not to point to 1 of the targets before they walked the third leg. The results showed that the initial position of the third leg was still influenced by the displaced landmark in the former case, but was determined by idiothetic cues in the latter case. These results suggest that the path integration system works dynamically and the piloting system resets the path integration system when people judge their positions in the presence of conflicting piloting cues. (PsycINFO Database Record
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".