Examining the Role of Attention and Sensory Stimulation in the Attentional Repulsion Effect
Bibliographic record
Abstract
It has been suggested that visual attention warps space, such that stimuli appearing near its locus are perceived as farther away than they actually are. This is known as the attentional repulsion effect (ARE). Recent data challenge the role of attention as the sole factor responsible for the ARE, suggesting instead that the ARE is, at least in part, a product of low level sensory interactions between a peripheral orienting cue and the Vernier target stimulus used to measure the effect. Here, we directly test whether attentional orienting, without a cue in peripheral vision to guide attention, is sufficient for generating an ARE. In Experiment 1, attention was guided to the visual periphery by a central symbolic cue that reliably indicated the locations of to-be-identified targets in peripheral vision. On a subset of trials, we probed for an ARE with Vernier targets. Reaction time (RT) data revealed that the cue guided attention but there was no trace of an ARE. In Experiment 2, we ensured that the Vernier targets were sensitive to the ARE by using the standard spatially uninformative peripheral cue to guide attention instead of the central symbolic cue. RT data again revealed that the cue guided attention, while the Vernier targets revealed an ARE. Collectively, these data suggest that attentional orienting without peripheral sensory stimulation is not sufficient for generating an ARE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 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".