Attending to a feature results in neighboring within-feature suppression
Bibliographic record
Abstract
According to the Selective-Tuning model (Tsotsos, 1990), convergence of neural input and selection of a attended feature will result in surround suppression within that feature domain: the nearby feature values in the same feature dimension will be inhibited, but not the feature values farther away in the dimension. We present three experiments that support this hypothesis. The first experiment used a feature-cue paradigm. The subjects' attention was first attracted to an orientation cue, and then subjects made a perceptual judgment about a stimulus with same or different orientation as the cue. We found that orientation attention actively suppressed the nearby orientations (5 ∼ 10 degree from the cue), but did not influence far away orientations (20 ∼ 90 degree from the cue), replicating the results of Tombu & Tsotsos (2008). In the second experiment we used the same paradigm but added a distractor to the cue, and the stimulus sequence became cue -[[gt]] distractor -[[gt]] probe. This time the subjects must actively ignore the distractor to focus their attention on the cue. By increasing the difficulty of the subjects pay attention to the cue, we found an even stronger Mexican-hat profile of attentional suppression. In the third experiment, we extended our findings from orientation to color. Again, we acquired a Mexican-hat profile of the feature attention in color dimension. These results further extend the evidence supporting the Selective Tuning explanation of spatial and feature attention and suggest a more general mechanism of signal-to-noise enhancement applicable to any feature dimension.
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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) | 0.001 | 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) | 0.002 | 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".