Learned spatial suppression is not always proactive.
Bibliographic record
Abstract
Learning to ignore distractors is critical for navigating the visual world. Research has suggested that a location frequently containing a salient distractor can be suppressed. How does such suppression work? Previous studies provided evidence for proactive suppression, but methodological limitations preclude firm conclusions. We sought to overcome these limitations with a new search-probe paradigm. On search trials, participants searched for a shape oddball target while a salient color singleton distractor frequently appeared in a high-probability location. On randomly interleaved probe trials, participants discriminated the orientation of a tilted bar presented briefly at one of the search locations, allowing us to index the spatial distribution of attention at the moment the search would have begun. Results on search trials replicated previous findings: reduced attentional capture when a salient distractor appeared in the high-probability location. However, critically, probe discrimination was no different at the high-probability and low-probability locations. We increased the incentive to ignore the high-probability location in Experiment 2 and found, strikingly, that probe discrimination accuracy was greater at the high-probability location. These results suggest that the high-probability location was initially selected before being suppressed, consistent with a reactive mechanism. Overall, the accuracy probe procedure demonstrates that learned spatial suppression is not always proactive, even when response time metrics seem consistent with such an inference. (PsycInfo Database Record (c) 2023 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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".