Object-Based Attention
Bibliographic record
Abstract
There appear to be three independent systems for allocating attention: space-based, feature based, and object-based. Here, we review the literature of object-based attention to determine its underlying mechanisms. First, findings from unconscious priming and cuing suggest that the pre-attentive targets of object-based attention can be fully developed object representations. Next, the control of object-based attention appears to come from ventral visual areas specialized in object analysis that project downward to early visual areas. Whether feedback from object areas can accurately target the object’s specific locations and features is controversial, but recent work in autoencoding has made this plausible. Finally, we suggest that the three classic modes of attention may not be as independent as is commonly considered, and instead could rely on object-based attention for all three modes of selection. Specifically, studies show that attention can spread over the separated members of a group – without affecting the space between them — matching the defining property of feature-based attention. At the same time, object-based attention directed to a single small item has the properties of space-based attention. Nevertheless, the evidence for a parallel, space-based selection controlled through saccade centers is also convincing. We outline the architecture for this combined system and discuss how it works in parallel with other attention pathways.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".