Large-scale functional networks underlying visual attention
Bibliographic record
Abstract
Attention networks are loosely defined as the regions of the brain which interact to control behaviour during attentional tasks, but the specific definition of attention networks varies between research programs based on task demands and modalities. The Attention Network Task was designed to exemplify three aspects of attention, alerting, orienting, and executive control, using a visual cueing paradigm. Its proponents propose a system of networks which underlies these aspects. It is debated whether there exists a unified system of networks which underlies attention independently of other cognitive and sensory processing systems. We review the evidence for an attention system within the domain of visual attention. Neuroimaging research using fMRI, EEG, MEG, and others across a variety of tasks attributed to attention, visual cueing, visual search, and divided attention, is compared. This concludes with a discussion on the limitations of an independent “attention system” for describing how the brain flexibly controls many abilities attributed to visual attention. • An independent “attention system” limits understanding of cognitive neural mechanisms. • The dorsal attention network is the main network responsible for visual attention. • Stimulus detection across sensory domains involves the ventral attention network. • Executive control networks support, but are not specific to, attention tasks. • Normal aging influences attentional behavior through executive control networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".