Dissociating Orienting Biases From Integration Effects With Eye Movements
Bibliographic record
Abstract
Despite decades of research, the conditions under which shifts of attention to prior target locations are facilitated or inhibited remain unknown. This ambiguity is a product of the popular feature discrimination task, in which attentional bias is commonly inferred from the efficiency by which a stimulus feature is discriminated after its location has been repeated or changed. Problematically, these tasks lead to integration effects; effects of target-location repetition appear to depend entirely on whether the target feature or response also repeats, allowing for several possible inferences about orienting bias. To parcel out integration effects and orienting biases, we designed the present experiments to require localized eye movements and manual discrimination responses to serially presented targets with randomly repeating locations. Eye movements revealed consistent biases away from prior target locations. Manual discrimination responses revealed integration effects. These data collectively revealed inhibited reorienting and integration effects, which resolve the ambiguity and reconcile episodic integration and attentional orienting accounts.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".