The interaction between stimulus-driven and goal-driven orienting as revealed by eye movements.
Bibliographic record
Abstract
It is generally agreed that attention can be captured in a stimulus-driven or in a goal-driven fashion. In studies that investigated both types of capture, the effects on mean manual response time (reaction time [RT]) are generally additive, suggesting two independent underlying processes. However, potential interactions between the two types of capture may fail to be expressed in manual RT, as it likely reflects multiple processing steps. Here we measured saccadic eye movements along with manual responses. Participants searched a target display for a red letter. To assess contingent capture, this display was preceded by an irrelevant red cue. To assess stimulus-driven capture, the target display could be accompanied by the simultaneous onset of an irrelevant new object. At the level of eye movements, the results showed strong interactions between cue validity and onset presence on the spatiotemporal trajectories of the saccades. However, at the level of manual responses, these effects cancelled out, leading to additive effects on mean RT. We conclude that both types of capture influence a shared spatial orienting mechanism and we provide a descriptive computational model of their dynamics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".