Manual tracking of the double-drift illusion
Bibliographic record
Abstract
When a target has poor position information, vision may take the target’s motion into account in generating its perceived location, resulting in conflicts between apparent and actual position. The double-drift illusion (Lisi & Cavanagh, 2015) is one such case in which the internal motion of the target drives an accumulating perceived offset that, after as much as 2 to 4 seconds, reaches some saturation limit where the accumulation stops or resets to the physical location. To investigate this illusion and its limits, we asked participants to move a pen over a drawing tablet to continuously track where they perceived the stimulus. By interposing an angled mirror, the participants were able to see the target moving on the same horizontal surface where they moved the stylus but could not see their hand or the stylus. We found that the manual tracking data showed the double-drift illusion and that its magnitude was sensitive to the internal and external speeds of the moving gabor, being largest when the external speed was slow and the internal speed high. This indicates that the manual tracking data can in principle be used to follow the perceived target location moment by moment to investigate how and when the illusory position shift saturates.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".