Allocation of spatial attention in human visual cortex as a function of endogenous cue validity
Bibliographic record
Abstract
Several areas of visual cortex contain retinotopic maps of the visual field, and neuroimaging studies have shown that covert attentional guidance will result in increases of activity within the regions representing attended locations. However, little research has been done to directly compare neural activity for different types of attentional cues. Here, we used fMRI to investigate how retinotopically-specific cortical activity would be modulated depending on whether we provided deterministic or probabilistic spatial information. On each trial, a four-item memory array was presented and participants' memory for one of the items would later be probed. Critically, trials began with a foveally-presented endogenous cue that was either 100% valid (deterministic runs), 70% valid (probabilistic runs), or neutral. By dividing visual cortex into quadrant-specific regions of interest (qROIs), we could examine how attention was spatially distributed across the visual field within each trial, depending on cue type and delay. During the anticipatory period prior to the memory array, we found increased activation at the cued location compared to noncued locations, with surprisingly comparable levels of facilitation for both deterministic and probabilistic cues. However, we found significantly greater facilitation on deterministic relative to probabilistic trials following the onset of the memory array, with only deterministic cue-related facilitation persisting through the presentation of the probe. These findings reveal how cue validity can drive differential allocations of neural resources over time across cued and noncued locations, and that the allocation of attention should not be assumed to invariably scale alongside the validity of a cue.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".