Exploring the spatiotemporal properties of fractal rotation
Bibliographic record
Abstract
Motion perception of first- and second-order stimuli has been proposed to be mediated by separate mechanisms. Whereas luminance-based stimuli are analyze by energy-based motion detectors, uncertainty remains as to the mechanisms involved in the processing of second-order stimuli. The aim of this experiment was to determine the nature of mechanisms accounting for detection of fractal rotation (Benton, O'Brien & Curran, 2007) in comparison with those responsible for first-order rotation. To reproduce such a stimulus, a rotating oriented filtered noise pattern was used, in which orientation varied from frame to frame. Noise was resampled for each frame. This stimulus should be invisible to first-order motion sensitive mechanisms considering the absence of energy movement. Rather, rotation is the only local cue available to motion detectors. Hence, motion perception would be based on the analysis of spatial structure, more specifically the orientation change over time. In comparison, we have used another stimulus composed of a single rotating oriented filtered noise frame where motion is detected by first-order sensitive mechanisms. First, we measured the temporal response of fractal rotation. Contrast thresholds were measured using a direction discrimination task at various temporal frequencies. First-order rotation was found to be band-pass, whereas fractal rotation was low-pass, as previously reported for contrast-, polarity- and spatial length-modulated motion. Hence, fractal rotation has second-order mechanism properties. Second, the nature of mechanisms responsible for detection of fractal rotation has been explored using a known paradigm where different energy levels are used by changing velocities (Seiffert & Cavanagh, 1998). Sensitivity to first-order stimuli is expected to change proportionately with energy levels while sensitivity to second-order properties is not. Results suggest a velocity-based mechanism account for perception of first-order motion but not for fractal rotation. This implies second-order mechanisms are sensitive to change of spatial orientation structure over time.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".