Deconstructing the frame effect
Bibliographic record
Abstract
The perception of an object's location is profoundly influenced by the surrounding dynamics. This is dramatically demonstrated by the frame effect, where a moving frame induces substantial shifts in the perceived location of objects that flash within it. In this study, we examined the elements contributing to the large magnitude of this effect. Across three experiments, we manipulated the number of probes, the dynamics of the frame, and the spatiotemporal relationships between probes and the frame. We found that the presence of multiple probes amplified the position shift, whereas the accumulation of the frame effect over repeated motion cycles was minimal. Notably, an oscillating frame generated more pronounced effects compared to a unidirectional moving frame. Furthermore, the spatiotemporal distance between the frame and the probe played pivotal roles, with larger shifts observed near the leading edge of the frame. Interestingly, although larger frames produced stronger position shifts, the maximum shift occurred almost at the same distance relative to the frame's center across all tested sizes. Our findings suggest that the number of probes, frame size, relative probe-frame distance, and frame dynamics collectively contribute to the magnitude of the position shift.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".