Allocation of spatial attention in human visual cortex as a function of endogenous cue validity
Bibliographic record
Abstract
Certain areas of the brain are known to contain retinotopic maps of the visual field, and covert attentional guidance has been shown to result in spatially-specific increases of neural activity within certain cortical regions representing the attended locations. However, little research has been done to directly compare how attentional cues that carry differing levels of task-relevant spatial information will impact cue-modulated neural activity, particularly in terms of preparatory (i.e., pre-stimulus) attention. Here, we used fMRI to investigate how activity in area V4 would respond depending on if participants were cued with deterministic or probabilistic spatial information. Every trial began with a central arrow cue and subsequent pre-array delay, followed by a four-item memory array and subsequent post-array delay prior to the presentation of the memory probe for one of the array items. Critically, at the start of each run of trials, participants were informed that the arrow cues would indicate the to-be-probed location with either 100% validity (deterministic spatial cue) or 70% validity (probabilistic spatial cue). Our results revealed significantly higher cued versus noncued V4 quadrant activity for both probabilistic and deterministic cues prior to the onset of the memory array, but following the onset of the memory array, only significantly higher cued versus noncued V4 quadrant activity for deterministic cue trials. These findings reveal how cue validity alone can drive a differential allocation of neural resources across cued and noncued locations, and how this allocation can vary over time within a trial. Information providing certainty regarding the target’s upcoming location appeared to bias attention both in anticipation of, and following, the presentation of task-relevant stimuli. In contrast, while information regarding where a target is most probable (but not guaranteed) to appear initially biased attention, this bias was more likely to spread or wane after the onset of task-relevant stimuli.
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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 | 0.004 |
| 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 | 0.001 | 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".