The Effects of Spatial Endogenous Pre-cueing across Eccentricities
Bibliographic record
Abstract
Frequently, we use expectations about likely locations of a target to guide the allocation of our attention. Despite the importance of this attentional process in everyday tasks, examination of pre-cueing effects on attention, particularly endogenous pre-cueing effects, has been relatively little explored outside an eccentricity of 20°. Given the visual field has functional subdivisions that attentional processes can differ significantly among the foveal, perifoveal, and more peripheral areas, how endogenous pre-cues that carry spatial information of targets influence our allocation of attention across a large visual field (especially in the more peripheral areas) remains unclear. We present two experiments examining how the expectation of the location of the target shapes the distribution of attention across eccentricities in the visual field. We measured participants' ability to pick out a target among distractors in the visual field after the presentation of a highly valid cue indicating the size of the area in which the target was likely to occur, or the likely direction of the target (left or right side of the display). Our first experiment showed that participants had a higher target detection rate with faster responses, particularly at eccentricities of 20° and 30°. There was also a marginal advantage of pre-cueing effects when trials of the same size cue were blocked compared to when trials were mixed. Experiment 2 demonstrated a higher target detection rate when the target occurred at the cued direction. This pre-cueing effect was greater at larger eccentricities and with a longer cue-target interval. Our findings on the endogenous pre-cueing effects across a large visual area were summarized using a simple model to assist in conceptualizing the modifications of the distribution of attention over the visual field. We discuss our finding in light of cognitive penetration of perception, and highlight the importance of examining attentional process across a large area of the visual field.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".