Control of Spatial and Feature-Based Attention in Frontoparietal Cortex
Bibliographic record
Abstract
Visual attention selects task-relevant information from scenes to help achieve behavioral goals. Attention can be deployed within multiple domains to select specific spatial locations, features, or objects. Recent evidence has shown that voluntary shifts of attention in multiple domains are consistently associated with transient increases in cortical activity in medial superior parietal lobule, suggesting that this may be the source of a domain-independent control signal that initiates the reconfiguration of attention. To investigate this hypothesis, we used fMRI to measure changes in cortical activation while human subjects shifted attention between spatial locations or between colors at a location. Univariate multiple regression analysis revealed a common, domain-independent transient signal [in posterior parietal cortex (PPC) and prefrontal cortex] time-locked to shifts of attention in both domains. However, multivariate pattern classification conducted on the cortical surface revealed that the spatiotemporal pattern of activity within PPC differed reliably for spatial and feature-based attention shifts. These results suggest that the posterior parietal cortex is a common hub for the control of attention shifts but contains subpopulations of neurons with domain-specific tuning for cognitive control.
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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 | 0.001 |
| 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) | 0.001 | 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".