Statistical regularities alter the spatial scale of attention
Bibliographic record
Abstract
When looking out at a scene, we can flexibly direct our attention to individual objects (e.g., a specific tree), or to the whole scene (e.g., a forest). Here we examine how the learning of statistical regularities prioritizes individual objects in the array (local attention) or the entire array (global attention). In Experiment 1, we examined whether local regularities draw attention to a local scale. Observers viewed arrays of nine colored objects arranged in a 3x3 matrix. Each matrix was either in the shape of a square or a diamond. Each individual object was either a square or a diamond. Unbeknownst to the observers, the matrix either contained three triplets of colored objects (i.e., local regularities) in the structured condition, or contained colored objects in a random arrangement in the random condition. The task was to indicate, as fast as possible, either the shape of the individual object, or the global shape. We found that observers were reliably faster at identifying individual objects but slower in identifying the global shape, when the array was structured vs. random. This suggests that local regularities facilitate local attention and impede global processing. In Experiment 2, we examined whether global regularities cue global attention. Everything was the same as in Expt1 except that there were no triplets. Instead, the four corners of the matrix contained a color quadruple (i.e., global regularities) in the structured condition. We found that observers were reliably faster at identifying the global shape but slower in identifying individual objects, in the structured vs. random condition. This suggests that global regularities facilitate global attention and impede local processing. These findings demonstrate that spatial regularities can determine whether attention is directed to individual elements or to the entire scene, providing evidence for the influence of statistical learning on the spatial scale of attention. Meeting abstract presented at VSS 2014
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".