Bibliographic record
Abstract
Purpose. The visual system integrates local orientation information across space to define spatial contours (Field, et al V.R. 33, 1993). More recently, it has been shown that a similar integration occurs for the direction of local motion signals in different parts of the field if they are aligned along a spatial contour (Ledgeway & Hess ARVO, 2001). Here we ask what are the spatial properties of this specialized type of motion integration. Methods Using a standard 2AFC task, observers were asked to choose which interval contained the elongated spatial contour (path) defined solely by motion. One interval chosen at random on each trial contained 158 micropatterns of random position and direction (background micropatterns) and in the other interval (path plus background) the motion directions of some (8) of the background micropatterns were arranged to lie along the invisible backbone of an elongated contour. The micropatterns contained 2-d, spatial noise that either had a spatial frequency bandwidth of 1 octave (Expt 1) or had an orientation bandwidth of 30 (Expt 2) and the centre frequencies/orientations of individual micropatterns could be varied. Results Motion-defined contours exhibit broad tuning for spatial frequency but narrow tuning for orientation. Conclusions Although motion-defined contours differ from orientation-defined counterparts in terms of their spatial frequency tuning they both exhibit narrow orientation tuning.
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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 | 0.007 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".