Feature-based surround suppression in the motion domain
Bibliographic record
Abstract
When we attend to a certain visual feature, such as a specific orientation (Tombu & Tsotsos, 2008) or specific colour (Störmer & Alvarez, 2014), processing of features nearby in that space are suppressed (i.e., feature-based surround suppression). In the present study, we investigated feature-based surround suppression in a new feature domain, motion direction, using motion repulsion as a measurement. Chen and colleagues (2005) suggested that attention to one motion direction reduces motion repulsion by inhibiting the other direction. Based on this finding, we conducted a similar direction judgment task having naïve participants. They reported perceived directions of two superimposed motions after viewing the motions for 2 sec. The directional differences between two motions systematically varied (10~70 deg) and the surfaces were separated by different colours (green or red). In the unattended condition, participants performed direction judgment tasks only, attending equally to both motions. In the attended condition, a colour cue was presented, indicating which motion participants should attend. Participants were asked to detect a brief directional shift of the cued motion and then, report the perceived motion directions. We compared the magnitude of motion repulsion between the two attention conditions. In contrast to the findings of Chen and colleagues, participants showed greater motion repulsion in the attended condition than in the unattended condition, especially when two motions moved along nearby directions. The results suggest that feature-based surround suppression exists in the motion domain and that it may occur on an early stage of motion processing where the global direction of motion is computed. 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 | 0.000 | 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) | 0.001 | 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".