Edge-based versus region-based texture perception: does the task matter?
Bibliographic record
Abstract
Aim: Studies of texture segregation have suggested that some types of textures are processed by ‘edge-based’ and others by ‘region-based’ mechanisms (e.g. Wolfson & Landy, Vis. Res., 1998). On the other hand, studies using nominally ‘edge-based’ textures have found evidence for region-based processing when the task was to detect rather than to segregate the textures (Kingdom & Keeble, Vis. Res., 1996). Here we investigate directly whether the nature of the task determines if region-based or edge-based mechanisms are involved in texture perception. Method: Stimuli consisted of randomly positioned Gabor micropattern texture arrays with three types of modulation: orientation modulation (OM), contrast modulation (CM) and luminance modulation (LM). Each modulation type was defined by three types of waveforms: sine-wave (SN), square-wave (SQ) and cusped-wave (CS). The CS waveform was constructed by removing an equal-amplitude sine-wave from a square-wave. The SN textures had only smooth variations, whereas the SQ and CS waveforms had sharp texture edges, but with different texture energies. Subjects performed two tasks. In the detection task subjects selected which of two stimuli contained the modulation. In the discrimination task subjects indicated which of two textures with slightly different texture-bar orientations contained leftward-oriented bars. Results: At low texture spatial frequencies threshold amplitudes in the detection task followed the rule SQ <SN <CS, as would be expected if all the texture energy available was used for detection, and suggesting that the task was region-based. However, for the discrimination task the order was SQ similar to CS and both less than SN, suggesting that the texture edges were the more salient features. At medium and high texture spatial frequencies the two tasks produced comparable results. Conclusion: A change in the task from detection to discrimination can under some circumstances cause texture processing to switch from being region-based to edge-based. Meeting abstract presented at VSS 2013
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".