Perceived position stabilization depends on the moving frame’s displacement: an online study
Bibliographic record
Abstract
Knowing where things are is important. Here we examine the effect of motion context, using a frame that moves back and forth, on the perceived position of probes (Cavanagh, Anstis, & Wexler, VSS 2019). When two probe dots are flashed inside the frame at the same physical location, each at one extreme of the frame’s movement, a very large illusory offset is seen between the probes, roughly equal to the frame’s travel. Here we examine the effects of the distance, duration, and speed of the frame’s travel on the perceived spatial offset. A total of 274 York University undergraduates completed an online task (PsychoJS, hosted on Pavlovia). After screening participants for appropriate devices and self-reported understanding of the task (60), and response outliers (73), 141 remained. Reliable monitor calibration was available for about 40% of participants. The size of the stimuli in degrees of visual angle did not affect illusion strength so we combined all data. The perceived spacing approximately matched the distance the frame moved, both when varying the speed (r²=0.97, p=.001) and the duration (r²=0.92, p=.006) of the frame’s motion. Conclusion: stimuli flashed before and after a frame’s motion are seen in their coordinates relative to the frame as if the frame were stationary.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".