Neural Basis for Priming of Pop-Out during Visual Search Revealed with fMRI
Bibliographic record
Abstract
Maljkovic and Nakayama first showed that visual search efficiency can be influenced by priming effects. Even "pop-out" targets (defined by unique color) are judged quicker if they appear at the same location and/or in the same color as on the preceding trial, in an unpredictable sequence. Here, we studied the potential neural correlates of such priming in human visual search using functional magnetic resonance imaging (fMRI). We found that repeating either the location or the color of a singleton target led to repetition suppression of blood oxygen level-dependent (BOLD) activity in brain regions traditionally linked with attentional control, including bilateral intraparietal sulci. This indicates that the attention system of the human brain can be "primed," in apparent analogy to repetition-suppression effects on activity in other neural systems. For repetition of target color but not location, we also found repetition suppression in inferior temporal areas that may be associated with color processing, whereas repetition of target location led to greater reduction of activation in contralateral inferior parietal and frontal areas, relative to color repetition. The frontal eye fields were also implicated, notably when both target properties (color and location) were repeated together, which also led to further BOLD decreases in anterior fusiform cortex not seen when either property was repeated alone. These findings reveal the neural correlates for priming of pop-out search, including commonalities, differences, and interactions between location and color repetition. fMRI repetition-suppression effects may arise in components of the attention network because these settle into a stable "attractor state" more readily when the same target property is repeated than when a different attentional state is required.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".