Neural basis of feature cueing in the perception of object contours
Bibliographic record
Abstract
Cueing attention to a particular location is space leads to enhanced processing of objects in that circumscribed area. However, attention can also be cued to non-spatial properties of objects (e.g. colour) resulting in preferential processing of that feature throughout the visual field. We have previously shown that this principle of feature-based attention contributes to higher-level processing, such as during the perception of objects. When attending to contour defined loops, perception of similar object contours is better relative to the perception of other incongruent features. In the current experiment we investigated the influence of feature cues. Participants viewed a rapid serial visual presentation of random arrays of gabors that sometimes formed a loop that was either contour- or motion-defined and that should be detected as quickly as possible. To cue feature-based attention, in separate blocks of trials there was an 80% chance that the target was a contour or motion, respectively. We found valid cues to contour-defined loops produced faster reaction times compared to invalid cues. This demonstrates the impact of a feature cue in perceiving object contours. To investigate the neural mechanism responsible for this feature cue effect, we recorded ERP's while performing this task. We observed cue-related positivity at about 300ms after stimulus onset. Our data argue for a contribution of later, possibly top-down mechanisms to feature-based attention, perhaps reflecting an attentional set for feature dimensions.
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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 | 0.003 |
| 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 | 0.001 |
| 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".