The Architecture of Object-Based Attention
Bibliographic record
Abstract
Evidence for the existence of object-based attention raises several important questions: what are objects, how does attention access them, and what anatomical regions are involved? What are the “objects” that attention can access? Several studies have shown that items in visual search tasks are only loose collections of features prior to the arrival of attention. Nevertheless, findings from a wide variety of paradigms, including unconscious priming and cuing, have overturned this view. Instead, the targets of object-based attention appear to be fully developed object representations that have reached the level of identity prior to the arrival of attention. Where do the downward projections of object-based attention originate? Current research indicates that the control of object-based attention must come from ventral visual areas specialized in object analysis that project downward to early visual areas. If so, how can feedback from object areas accurately target the object’s early locations and features when the object areas have only crude location information? Critically, recent work on autoencoders has made this plausible as they are capable of recovering the locations and features of the target objects from the high level, low dimensional codes in the object areas. I will outline the architecture of object-based attention, the novel predictions it brings, and discuss how it works in parallel with other attention pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".