Visual Search and Dual Tasks Reveal Two Distinct Attentional Resources
Bibliographic record
Abstract
Most theories of visual processing assume that a target will "pop out" from an array of distractors ("parallel" visual search, e.g., color or orientation discrimination) if targets and distractors can be discriminated without attention. When the discrimination requires attention (e.g., rotated L vs. T or red-green vs. green-red bisected disks), "serial" examination is needed in visual search. Attentional requirements are also frequently assessed by measuring interference from a concurrently performed attentionally demanding task. It is commonly believed that attention acts equivalently in dual-task and visual search paradigms, based on the implicit assumption that visual attentional requirements can be defined along a single dimension. Here we show that there is no such equivalence: We report on targets that do not trigger pop-out, even though they can be discriminated from distractors with attention occupied elsewhere (natural scenes, color-orientation conjunctions); conversely, we show that certain targets that pop out among distractors need undivided attention to be effectively discriminated from distractors when presented in isolation (rotated L vs. +, depth-rotated cubes). In other words, visual search and dual-task performance reveal attentional resources along two independent dimensions. We suggest an interpretation of these results in terms of neuronal selectivities and receptive field size effects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".