Cued Shifts of Attention and Memory Encoding in Partial Report: A Dual-Task Approach
Bibliographic record
Abstract
This study explores how cued shifts of visual attention and rapid encoding of visual information relate to limited-capacity processing mechanisms. Three experiments were conducted placing a partial-report task within a dual-task paradigm. Experiments 1 and 2 involved a simple speeded visual discrimination (Task 1) and then an unspeeded partial-report task (Task 2). Generally, Task 2 accuracy declined as the temporal overlap between the two tasks increased. In addition, in Experiment 1, varying the number of items in the partial-report display had an effect on performance regardless of overlap. In contrast, in Experiment 2, varying the type of probe had an effect only at long task overlap. The generality of the interference effect was tested in Experiment 3 using an auditory discrimination as Task 1. Again, Task 2 accuracy declined as the temporal overlap between the two tasks increased. In all cases, the observed interference had the properties of a processing bottleneck. It is argued that encoding information into memory and response selection for the first task both require general-purpose processing. The results are discussed in terms of the functional relationship between attention and memory.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".