From alternation to repetition: Spatial attention biases contribute to sequential effects in a choice reaction-time task
Bibliographic record
Abstract
Observers often take longer to respond to a visual target when it appears at a recently stimulated location than when it appears at a new location in the visual field. This behavioral impairment - known as inhibition of return (IOR) - is mirrored by a reduction of an event-related potential (ERP) component called the N2pc that has been associated with attentional selection. Together, these findings indicate that the mechanism underlying IOR operates to bias covert attention against re-visiting the most recently attended location. The goal of the present study was to determine how this inhibitory attention bias evolves across successive trials of a two-item search task. Initially, targets appearing at previously attended locations were associated with behavioral IOR and a concomitant reduction of the N2pc. After several successive trials, this initial inhibitory bias was superseded by expectancy-based biases associated with "predictable" inter-trial patterns of location repeats or location changes, in some cases leading to faster responses and a larger N2pc when the target location repeated (facilitation of return). These results provide evidence that biases in the covert deployment of attention are updated dynamically according to the recent selection history and contribute to well-known sequential effects in serial choice reaction-time tasks.
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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 | 0.009 |
| 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 | 0.000 |
| 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".