Investigating Attentional Repulsion as a Mechanism for Anisotropic Position Shifts around Moving Objects
Bibliographic record
Abstract
Moving objects shift the perceived position of nearby flashes (Whitney & Cavanagh, 2000). We presented bars rotating around the center of gaze and flashed dots at different locations and times relative to the bars. We found that flashes presented ahead of a moving bar were shifted in the direction of the bar's motion, peaking for probes 15 degrees of rotation ahead of the bar, while flashes presented behind the bar remained largely unaffected (similar to Watanabe et al., 2003; Durant & Johnston, 2004). It has previously been proposed (Shams, Kohler, & Cavanagh, ECVP 2023) that this anisotropic position shift is due to attentional repulsion (Suzuki & Cavanagh, 1997) where, in this case, the focus of attention leads the moving object. Here, we further tested the role of attention by varying the number of bars and flashes and by introducing a spatial cue at the center pointing to the physical location of the target, presented either before (pre-cue) or after (post-cue) the bar’s motion. In the post-cue condition, we observed a large position shift (~1.7 dva) that did not differ between the one-bar and four-bar conditions. In contrast, in the pre-cue condition, the position shift decreased (~7%) in the one-bar condition and increased (~12%) in the four-bar condition compared to the post-cue conditions. The lack of effect of the number of probes in the post-cue conditions suggest that exogenous attention must be involved because exogenous attention can be unaffected by attentional load (Wright, 1994; Solomon, 2004). The pre-cue conditions suggest that endogenous attention may also play a role by either modulating the position shift by reallocating resources between the moving object and the probe location (as proposed by Adamian & Cavanagh, 2024) or by increasing the speed at which the probe location can be retrieved (Müsseler & Aschersleben, 1998).
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".