Slow Fluctuations in Attentional Control of Sensory Cortex
Bibliographic record
Abstract
Top-down control of visual sensory cortex has long been tied to the orienting of visual spatial attention on a rapid, moment-to-moment basis. Here, we examined whether sensory responses in visual cortex are also modulated by natural and comparatively slower fluctuations in whether or not one is paying attention to the task at hand. Participants performed a simple visual discrimination task at fixation as the ERPs to task-irrelevant probes in the upper visual periphery were recorded. At random intervals, participants were stopped and asked to report on their attentional state at the time of stoppage-either "on-task" or "off-task." ERPs to the probes immediately preceding these subjective reports were then examined as a function of whether attention was in an on-task versus off-task state. We found that sensory-evoked responses to the probes were significantly attenuated during off-task relative to on-task states, as measured by the visual P1 ERP component. In two additional experiments, we replicated this effect while (1) finding that off-task sensory attenuation extends to the auditory domain, as measured by the auditory N1 ERP component, and (2) eliminating state-dependent shifts in general arousal as a possible explanation for the effects. Collectively, our findings suggest that sensory gain control in cortex is yoked to the natural ebb and flow in how much attention we pay to the current task over time.
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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 | 0.002 |
| 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".