Bibliographic record
Abstract
When observers initiate responses to visual targets, they do so sooner when a preceding stimulus indicates that the target will appear shortly. This consequence of a warning signal may change neural activity in one of four ways. On the sensory side, the warning signal may speed up the rate at which the target is registered by the brain or enhance the magnitude of its signal. On the motor end, the warning signal may lower the threshold required to initiate a response or speed up the rate at which activity accumulates to reach threshold. Here, we describe which explanation is better supported. To accomplish this end, monkeys performed different versions of a cue-target task while we monitored the activity of visuomotor and motor neurons in the superior colliculus. Although the cue target task was designed to measure the properties of reflexive spatial attention, there are two events in this task that produce nonspecific warning effects: a central reorienting event (brightening of central fixation marker) that is used to direct attention away from the cue, and the presentation of the cue itself. Monopolizing on these tendencies, we show that warning effects are associated with several changes in neural activity: the target-related response is enhanced, the threshold for initiating a saccade is lowered, and the rate at which activity accumulates toward threshold rises faster. Ultimately, the accumulation of activity toward threshold predicted behavior most closely. In the discussion, we describe the implications and limitations of these data for theories of warning effects and potential avenues for future research.
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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".