Neural Correlates of the Automatic and Goal-Driven Biases in Orienting Spatial Attention
Bibliographic record
Abstract
How do stimuli in the environment interact with the goals of observers? We addressed this question by showing that the relevance of an abruptly appearing visual object (cue) changes how observers orient attention toward a subsequent object (target) and how this target is represented in the activity of neurons in the superior colliculus. Initially after the appearance of the cue, attention is driven to its locus. This capture of attention is followed by a second bias in orienting attention, where observers preferentially orient to new locations in the visual scene-an effect called inhibition of return. In the superior colliculus, these two automatic biases in orienting attention were associated with changes in neural activity linked to the appearance of the target-relatively stronger activity linked to the capture of attention and weaker activity linked to inhibition of return. This behavioral pattern changes when the cue predicts the upcoming location of the target-the benefit associated with the capture of attention is enhanced and inhibition of return is reduced. These goal-driven changes in behavior were associated with an increase in pretarget- and target-related activity. Taken together, the goals of observers modify stimulus-driven changes in neural activity with both signals represented in the salience maps of the superior colliculi.
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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 | 0.003 |
| 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) | 0.000 | 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".