State-dependent dynamic grouping and the perception of motion
Bibliographic record
Abstract
A major legacy of the Gestalt Psychology movement was the determination that perceptual organization is based on laws of grouping. In many of their demonstrations, effects of grouping variables on the compositional structure of a stimulus are perceptually realized as qualitative changes in the spatial pattern perceived for the stimulus (e.g., multi-element grids of dots are grouped into horizontal rows or vertical columns). This, however, is not generally the case when multiple surfaces are connected to form an object. Changing the luminance of one surface of an object can change the object's compositional structure without resulting in the perception of a qualitatively different spatial pattern. We now show, however, that changes in the compositional structure of objects can be perceptually realized through motion created by dynamic grouping, even without qualitative changes in the perceived spatial pattern. (Such changes co-occurred with motion in an earlier study of how grouping/parsing affects on motion perception; Tse, Cavanagh & Nakayama, 1998.) Method. Stimuli were composed of two or three connected surfaces, one of which changed in luminance. Motion was perceived within the changing surface, as in the line motion illusion. Results. We have found that changes in grouping variables (luminance and texture similarity; good continuation) that increase a surface's affinity with an adjacent surface result in motion perception away from the boundary separating the surfaces. Motion is toward the boundary when affinity decreases. Moreover, the likelihood of a change in affinity resulting in motion perception depends on the nonlinear summation of the affinities ascribable to individual grouping variables (specifically, an accelerating nonlinearity), and the surface's affinity-state prior to the change in grouping variables. Additional experiments have shown that compositional structure affects how motion due to dynamic grouping and motion due to changes in edge and surface contrast function in tandem.
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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 | 0.002 |
| 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 | 0.001 | 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".