Sensory signals predict performance on a non-predictive cue-target task
Bibliographic record
Abstract
After the appearance of peripheral, non-predictive cues, participants respond faster to targets appearing at the cued location when the time that elapses in between the cue and target is short and they respond more slowly when this time is longer. Traditionally, this pattern of early facilitation and later inhibition has been interpreted as indexing two separate, but related, components of orienting spatial attention. Namely, after the peripheral cue catches the participants' attention (attentional capture), they are slower to reorient to this previously attended and unmeaningful location (inhibition of return). One problem with this interpretation is that inhibition of return can be observed when no evidence of attentional capture is obtained (e.g., Maylor, 1985). Here, we describe the neurophysiological correlates of these behavioral effects. The activity of single neurons in the superficial and intermediate layers of the superior colliculus was recorded while monkeys performed a non-predictive, cue-target task. In all experimental sessions, inhibition of return was obtained in behavior and was associated with attenuated target-aligned activity of visual and visuomotor neurons in the superficial and intermediate layers. In many sessions, attentional capture was also observed, which was associated with enhanced target-aligned activity because residual activity from the cue augmented the incoming activity from the target. In some sessions, however, attentional capture was not found. In these instances, the target-aligned signal tended to be reduced. Taken together, attentional capture, the absence of attentional capture, and inhibition of return arise from a continuum of sensory processing.
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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) | 0.002 | 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".