Global form perception in motion-defined radial-frequency contours
Bibliographic record
Abstract
Purpose: The nature of neural mechanisms that transform local motion signals into a representation of global form remains elusive. Here, we use motion-defined radial-frequency contours (RFs) as a general and systematic framework for investigating form-from-motion. Method: Stimuli consisted of 36 collinear Gabor elements arranged in a virtual circle. Each Gabor had a fixed envelope and a drifting carrier whose speed was determined by a sinusoidal function of polar angle. Randomly permuting speeds across Gabor elements produced incoherent modulations that served as ‘null’ stimuli in two-alternative forced-choice detection tasks. Thresholds were defined as sinusoidal amplitudes corresponding to 75%-correct performance. Results: Detection and discrimination data suggest that motion-RFs are optimally processed in the range of 1 to 4 radial cycles. Spatial-summation experiments (where coherent contours were replaced by incoherent contours over a variable pie-wedge section) showed that thresholds improved with coherent-contour length at a higher rate than predicted by probability summation. Results also revealed that random radial offsets in Gabor position impair spatial-RF detection but largely spare motion-RF detection if thresholds are equated via speed-to-position transfer functions measured for illusory motion-induced shifts in Gabor position [DeValois & DeValois, 1991, Vis. Res., 31, 1619–1626]. Conclusions: Mechanisms sensitive to motion-RFs are selective for contour smoothness and integrate motion structure globally. Results rule out local illusory positional shifts as a potential confound and demonstrate that motion pathways mediate shape perception for motion-RFs. Motion-RFs can be combined into arbitrary shapes via Fourier synthesis and therefore constitute a promising tool for studying the neural representations of complex motion-defined shapes.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".