Do ensemble representations guide visual attention in a visual search task?
Bibliographic record
Abstract
Ensemble processing plays an important role in our daily lives by condensing abundant visual information in our environment into statistical representations. Our study examined how these statistical representations are prioritized in the attentional system by asking whether ensemble representations, such as the average orientation of a set of items, can guide attention in a subsequent task. To explore this, we integrated an orientation-based ensemble-processing task with a visual search task. On each trial, participants were shown an initial display of eight bars of varying orientations. The subsequent task—either a search or an average task—was signalled by the colour of the fixation cross. When the cross turned orange (25% of trials), participants engaged in the search task. They had to locate and click on the shortest bar among six others displayed around the fixation point. Importantly, in half of these search displays, the target bar matched the average orientation of the initial eight-bar display. When the fixation cross turned blue (75% of trials), participants performed the average task. This task involved a display of two bars to the left and right of the fixation point, and participants had to determine which of these two bars corresponded to the average orientation of the initial eight-bar display. In both tasks, participants were instructed to respond as quickly and accurately as possible. The results revealed shorter response times (RTs) in the search task when the target bar matched the average orientation of the initial eight-bar display compared to when the orientation of the target bar did not match the average orientation of the initial display. On a local level, this finding indicates that ensemble representations guide attention in subsequent tasks. On a global level, this means that the representation of an item never explicitly perceived can guide attention and subsequent behaviour.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".