Is attention really biased toward the last target location in visual search? The role of focal attention and stimulus-response translation rules.
Bibliographic record
Abstract
There is considerable confusion in the visual attention literature as to whether shifts of attention are biased against or in favor of previously attended regions. Studies requiring target localization have shown a performance cost when the target location randomly repeats instead of changes, whereas studies requiring arbitrary keypress responses to target identities have shown a benefit. These studies differ in the amount of attention required to the target and in the stimulus-response translation rules. To evaluate the contribution of each of these factors in accounting for the mixed results, we had participants indicate whether color singletons appeared in the left versus right visual field, or in the upper versus lower visual field, by making spatially compatible keypress responses (a between-experiment manipulation of the stimulus-response translation rules). Within each experiment, we manipulated whether a subtle discrimination of shape was necessary before localizing the target (a manipulation of focal attention). The findings revealed that the costs and benefits for repeating the target location are determined by stimulus-response translation rules, with no effect of or on attention independent of these rules. The results are accounted for by the theory of event coding, and further challenge the notion that location repetition effects reliably reflect attentional bias. (PsycINFO Database Record (c) 2019 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".