Object-based Attention Measured with SSVEPs
Bibliographic record
Abstract
Feature-based attention has been demonstrated to have non-spatial aspect: Specifically, if attention is directed to a target, it extends to other targets with similar features (e.g., color or direction of motion) anywhere in the visual field. We tested whether this non-spatial property also holds for object-based attention where attention to one target superimposed on another (e.g., to a house superimposed on a face) may extend to other similar but task irrelevant targets elsewhere in the visual field. We used Steady State Visually Evoked Potentials (SSVEPs) with high-density EEG. While maintaining central fixation, participants were cued to pay attention to one of two superimposed images, either a house or a face, presented above fixation. In half of the trials, the orientation of the cued image briefly changed, and the participant had to report this by pressing a key. Average performance on this detection task was 84%. In addition to the overlapped house and face above fixation, another house and face were presented separately, one on the left and one on the right side of fixation, flickering at different frequencies (7.5Hz and 12Hz). These extra images were irrelevant to the task but if attention was nonetheless allocated to them, it should be evident as an increase in SSVEPs at harmonics of their flicker frequency. For two participants, topographies of the relevant harmonics showed a lateralized attention-dependent response, suggesting that the attention to the house or the face in the overlapped pair extended to the irrelevant, matching stimulus on the left or right. For the other two, no obvious attention-dependent response was observed. Next, we will test exemplars of the attended object categories to better prevent the recruitment of feature-based attention and we will add a more in-depth region-based analysis of an extended dataset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".