Spatial integration of orientation-defined texture
Bibliographic record
Abstract
Previous studies have reported linear summation for Glass patterns from measures of detection thresholds as a function of signal area, and have proposed specialized concentric orientation texture detectors (Wilkinson et al., 1997; cf. Dakin & Bex, 2002). Motivated by these findings and recent results in curvature discrimination showing strong summation of curvature information for circular segments up to 180˚ (semi-circle) (Schmidtmann et al., 2013), we investigated spatial integration for a variety of different orientation-defined textures (circular, radial, spiral, translational) composed of 150 Gabor patches. In a 2AFC, subjects had to detect the texture in a single randomly positioned pie-wedge sector of varying angular extent ranging from 36˚ - 360˚. The signal to noise ratio in that sector was varied, whereas the remaining array contained randomly oriented elements (noise only). Results show that, contrary to previous studies, detection thresholds for all texture types decrease with angular extent following a power-law function with an exponent around -0.5. To investigate the role of spatial uncertainty we fixed the angular position of the sector containing signal elements. This improved performance disproportionately for small sectors, resulting in even weaker summation across angular extent and can therefore not explain any lack of summation. Next we analyzed the correlation between correct responses and clustering of signal elements. Results show that observers are more likely to make correct responses if signal elements are clustered (high density). To summarize, we found that, a) the detection of orientation-defined texture is independent of texture type; b) summation across area was weaker than reported previously and c) summation strength is further reduced by adding spatial certainty. We suggest that detecting local clusters of signal elements might limit the detection of global form in these textures. Meeting abstract presented at VSS 2014
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".