Disentangling the contributions of spatiotopic, retinotopic, and configural frames of reference to the filtering of probable distractor locations.
Bibliographic record
Abstract
Human observers can allocate their attention to locations likely to contain a target and can also learn to avoid locations likely to contain a salient distractor during visual search. However, it is unclear which spatial frame of reference such learning is applied to. As such, our aim was to systematically disentangle the contributions of spatiotopic, retinotopic, and configural frames of reference to provide a comprehensive account of how the probabilistic distractor filtering effect comes about. We first demonstrate that the filtering effect is better determined by the probability of a salient distractor appearing at a relative location (i.e., in relation to one's eye position or an item's position in relation to other items within a display) rather than a fixed (spatiotopic) location, by varying the position of visual search arrays (along with fixation) across spatial contexts. We then separate retinotopic and configural reference frames by varying the configural but not retinotopic properties of biased (i.e., displays containing a probable distractor location) and unbiased visual search arrays and vice versa. In doing so, we find the filtering effect to be restricted to biased contexts when retinotopic positions are maintained, but configural properties are varied. In contrast, when the configural properties of visual search arrays are maintained, we show the transfer of the filtering effect across retinotopic positions. Thus, we demonstrate that probabilistic distractor filtering primarily emerges via a configural representation that codes the relative positions of items within search displays independent of spatiotopic and retinotopic coordinates. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".