Exploring the spatiotemporal properties of fractal rotation perception
Bibliographic record
Abstract
A series of three experiments was conducted with the aim of determining the processing nature of the fractal rotation stimulus introduced by C. P. Benton, J. M. O'Brien, and W. Curran (2007). This stimulus has been proposed to be invisible to first-order sensitive mechanisms considering it is drift-balanced. Rather, motion perception would require the analysis of spatial structure (orientation) changing over time. In Experiment 1, spatiotemporal properties of fractal rotation perception have been explored, in comparison with first-order rotation perception. In Experiment 2, a motion paradigm similar to the one developed by K. Nakayama and C. W. Tyler (1981) and later used by A. E. Seiffert and P. Cavanagh (1998) has been used to characterize the motion processing mechanism responsible for fractal rotation perception. In Experiment 3, we have used a paradigm similar to N. E. Scott-Samuel and A. T. Smith (2000) to evaluate whether fractal rotation perception is analyzed by common or distinct mechanisms to those for first-order rotation perception. Results indicate that fractal rotation perception involves feature-tracking processes with mechanisms responding to global orientation-based changes of the image. Given the absence of cancellation of first-order and fractal rotation motion signals, we can therefore conclude that the first-order and fractal motion sensitive pathways are dissociable at early stages of the visual processing stream.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".