Alerting effects occur in simple—But not in compound—Visual search tasks.
Bibliographic record
Abstract
(e.g., a brief brightening of the screen just before a target display) is known to facilitate visual search in simple tasks that involve the single step of detecting a pop-out item within a stimulus array. What is not known is whether alerting facilitates performance also in compound search tasks which involve two steps: First, locate the pop-out item, then identify a detail of that item. In a series of five experiments, we show that alerting facilitates performance of each component of a compound task when tested separately, (Experiments 2a and 2b) but not when the components are combined in a compound task (Experiment 1). Yet, alerting does facilitate performance in a compound task when the pop-out item is displayed in the same location on successive trials (Experiment 3). We hypothesized that such spatial repetition allows attention to linger at that location, thus allowing the first component (locate the pop-out item) to be bypassed. In practice, this turns the compound task into a simple task. That hypothesis was confirmed in Experiment 4 using a reorienting cue to shift the focus of attention to another location. An overall account of the absence of alerting effects in compound search tasks is proposed in terms of the temporal relationship between a period of enhancement rendered as an ex-Gaussian function and the hypothesized sequence of processing stages in visual search. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".