Does attention prioritize task relevant features in ensemble processing?
Bibliographic record
Abstract
Ensemble processing allows the visual system to condense visual information into useful summary statistics (e.g., average size), thereby overcoming capacity limitations to visual processing. Here we explored a novel dissociation between task relevant (i.e., a size cue paired with an average size ensemble task) and irrelevant (i.e., a color cue paired with an average size task) attentional cues in ensemble processing by creating a new paradigm that merged the action effect (a manipulation of attention) with ensemble-processing tasks. Participants made a simple action (action condition) if a task relevant word cue (“Large” or “Small”) corresponded to the size of a subsequent object (large or small rectangle) and made no action (viewing condition) if the cue did not correspond to the subsequent object size. Immediately after, they were shown an ensemble display of 8 ovals of varying sizes and were asked to report either the average size of all ovals (ensemble task) or the size of a single oval from the set (single task). On congruent trials the word cue corresponded to the average size of the ensemble display, while on incongruent trials the word cue did not correspond to the average size. An action effect occurred for the ensemble task, whereby cue congruency differences were found in the action but not the viewing condition. In contrast, no action effect was present in the single task. Experiments 2 and 3 examined if task irrelevant cues (colour instead of size) would also generate action effects in ensemble processing. No action effects were found in these experiments. Overall, task relevant cues that elicit an action can influence ensemble processing, but task irrelevant cues cannot, suggesting that attention is involved in ensemble processing but only when it is directed towards a task relevant feature.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".