Perceived travel distance depends on the speed and direction of self-motion
Bibliographic record
Abstract
Although estimating travel distance is essential to our ability to move through the world, our distance estimates can be inaccurate. These odometric errors occur because people tend to perceive that they have moved further than they had. Many of the studies investigating the perception of travel distance have primarily used forward translational movements, and postulate that perceived travel distance results from integration over distance and is independent of travel speed. Speed effects would imply integration over time as well as space. To examine travel distance perception with different directions and speeds, we used virtual reality (VR) to elicit visually induced self-motion. Participants (n = 15) were physically stationary while being visually "moved" through a virtual corridor, either judging distances by stopping at a previously seen target (Move-To-Target Task) or adjusting a target to the previous movement made (Adjust-Target Task). We measured participants' perceived travel distance over a range of speeds (1-5 m/s) and distances in four directions (up, down, forward, backward). We show that the simulated speed and direction of motion differentially affect the gain (perceived travel distance / actual travel distance). For the Adjust-Target task, forwards motion was associated with smaller gains than either backward, up, or down motion. For the Move-To-Target task, backward motion was associated with smaller gains than either forward, up or down motion. For both tasks, motion at the slower speed was associated with higher gains than the faster speeds. These results show that transforming visual motion into travel distance differs depending on the speed and direction of optic flow being perceived. We also found that a common model used to study the perception of travel distance was a better fit for the forward direction compared to the others. This implies that the model should be modified for these different non-forward motion directions.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".