Bibliographic record
Abstract
Visual motion can be a cue to travel distance when the motion signals are integrated. Previous work has given conflicting results on the precision of travel distance estimation from visual motion: Frenz and Lappe reported underestimation, Redlick, Jenkin and Harris overestimation of travel distance. In a collaborative study we resolved the conflict by tracing it to differences in the tasks given to the subjects. Self-motion was visually simulated in a immersive virtual environment. Subjects completed two tasks in separate blocks. They either had to report the distance traveled from the start of the movement as in earlier studies of Frenz and Lappe, or they had to report when they reached a predetermined target position as in earlier studies by Redlick et al. Consistent with both earlier studies, underestimation of travel distance occurred when the task required judgment of distance from the starting position, and overestimation of travel distance occurred when the task required judgment of the remaining distance to the previewed target position. Based on these results we developed a leaky integrator model that explains both effects with a single mechanism. In this model, a state variable, either the distance from start or the distance to target, is updated during the movement by integration over the space covered by the movement. Travel distance mis-estimation occurs because the integration leaks and because the transformation of visual motion to travel distance involves a gain factor. Mis-estimates in both tasks can be explained with the same leak rate and gain in both conditions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".