Co-determination of attentional allocation by endogenous and exogenous factors
Bibliographic record
Abstract
Spatial attention is driven by both endogenous and exogenous factors. Here we report results from a cueing experiment that explores the interplay between these factors in determining the allocation of spatial attention in a visual search task. Observers searched for a 2-deg circular target patch of one-dimensional oriented 1/f noise in a large circular field of isotropic 1/f noise of equal contrast. 10 possible random target sites within the 1/f background were indicated by 2-deg circles. Cued sites were indicated by red circles, non-cued sites by light grey circles. Between 0 and 10 sites were cued on every trial. The reliability of the cue was varied, but cueing was usually informative and at worst neutral. Targets appeared in half the trials. Observers were told that the target might appear in either red or grey circles, but the red circles were generally more reliable. Observers were asked to indicate presence/absence of the target with one of two keys, and to respond as quickly as possible, while minimizing errors. We recorded the reaction time for target detection as a function of the number of cued locations and considered two models of search. The first is a standard serial search model that accounts only for the endogenous component of attention. The second model combines both endogenous and exogenous components, where the exogenous cue is based upon novelty: salience of cued sites is inversely proportional to the number of cued sites, and likewise for non-cued sites. For 10 out of 10 subjects, we found that the second model combining both endogenous and exogenous factors provided a closer account of the data. These results suggest that the exogenous cue of novelty is a strong determinant of attentional allocation even in the face of salient exogenous competition and countervailing endogenous factors.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".