Equivalent internal noise for contour integration
Bibliographic record
Abstract
Oriented wavelets give the percept of a contour if each wavelet has the appropriate position and orientation to belong to that contour. The rules governing this have been tested using a contour integration task: observers detect a contour of aligned wavelets buried in a background noise of randomly-oriented wavelets. Here we present a new threshold contour task that does not rely on a background noise field to limit performance. Observers discriminate which of four contours (at an eccentricity of 2.8 degrees in four quadrants) contains the appropriate conjunction of (6 c/deg log-Gabor) wavelet positions and orientations to feature "good continuation". The other three contours have orientations appropriate to the opposite direction of curvature. Thresholds are measured as a function of curvature (straighter contours are more difficult). By corrupting the orientation and position of the individual wavelets with external noise we measured noise masking functions. Both orientation and position noise elevated thresholds. Equivalent internal noise values were compared to those from tasks where observers made judgements about single elements. We found that in a contour the orientation noise was elevated (260%) but the position noise was reduced (40%). This suggests that contour processing privileges the refinement of position information at the expense of orientation. The sensitivity loss between 4 and 8 degrees of eccentricity was modest (10% threshold increase), and was abolished when the scale of the contours was doubled in the periphery. In another experiment, we introduced a "baseline" curvature to the stimuli to investigate discrimination of curvier contours. Thresholds were elevated relative to discrimination of straighter contours (190%). Contrary to results from the previous contour task, we found observers could integrate curvy contours in the periphery. The measurements from this novel paradigm could be used to investigate mid-level visual impairments, and to constrain models of the traditional contour task. Meeting abstract presented at VSS 2017
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".