Motion direction and temporal frequency tuning of texture-surround capture of contour-shape
Bibliographic record
Abstract
Aim: Contour-shape processing is selective to motion direction [Gheorghiu, Kingdom & Varshney, 2010, Journal of Vision, 10(3):18, 1-19), and surround textures prevent the shape of a contour from being processed as a contour but instead as part of a texture, a phenomenon we term here ‘texture-surround capture of contour-shape’ [Gheorghiu & Kingdom, 2011, Journal of Vision 11(11), 1038; Kingdom & Prins, 2009, Neuroreport, 20(1), 5-8]. This raises the question as to whether the effect of texture surrounds on contour shape processing is selective to motion direction and temporal frequency. Methods: Subjects adapted to pairs of sinusoidal-shaped textures or to single contours that differed in shape-frequency, and the resulting shifts in the apparent shape-frequency of single-contour test pairs was measured. The texture adaptors consisted of a central contour, and a non-overlapping surround made of a series of contours arranged in parallel. Contours drifted within a fixed stimulus window in one or other direction of their axis of shape modulation. We varied (i) motion direction and (ii) the temporal frequencies of both central contour and texture surround. Results: We found that (i) the shape after-effect was strongly reduced by surround textures moving in the same but not opposite directions to the central contour; (ii) the reduction in shape after-effect caused by the surround texture increased in magnitude with the temporal frequency of the central contour, and (iii) the reduction in shape after-effect was selective for same center-surround temporal frequency. Conclusion: Texture-surround capture of contour shape is tuned to both motion direction and temporal frequency. Meeting abstract presented at VSS 2012
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".