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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".