In what ways does visual search benefit from a spatial cue?
Bibliographic record
Abstract
An attentional blink (AB) deficit is an impairment in the perception of the second of two rapidly sequential targets. It has been shown that during the AB, an exogenous spatial cue can be perceived and thus trigger orienting of attention towards the target in a search task (Ghorashi, Di Lollo, & Klein, 2006). Therefore, the cue caused an improvement in search performance. However, since the number of elements in the search array (i.e., set size) was not manipulated, it was not clear which aspect of the search was improved: was it the intercept or the slope of the search function (performance × set size)? In three experiments we sought to answer this question by maintaining the same methodology and varying the set size. Hence, in keeping with Ghorashi et al. (2006), the first target was always a white letter among black letter distractors. The second target was a search array in which observers identified the tilt of a letter T among rotated letter Ls. The search display was followed by a mask and was preceded by an informative spatial cue. In experiment 1, a dynamic staircase method was used to obtain the critical exposure duration of the second target that yielded 80% correct responses. The cue affected the intercept but not the search slope. In experiment 2, the search array was not masked but remained on the screen until the observer's speeded response. Reaction times revealed significant effects of the cue on both slope and intercept. In experiment 3 we found that the same results as in experiment 2 are obtained even when the search is performed as a single task. Collectively, the results are explained in terms of the exogenous cue causing a reduction in the effective set size of the search array.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".