Contingent capture effects in temporal order judgments.
Bibliographic record
Abstract
The contingent attentional capture hypothesis proposes that visual stimuli that do not possess characteristics relevant for the current task will not capture attention, irrespective of their bottom-up saliency. Typically, contingent capture is tested in a spatial cuing paradigm, comparing manual reaction times (RTs) across different conditions. However, attention may act through several mechanisms and RTs may not be ideal to disentangle those different components. In 3 experiments, we examined whether color singleton cues provoke cuing effects in temporal order judgments (TOJs) and whether they would be contingent on attentional control sets. Experiment 1 showed that color singleton cues indeed produce cuing effects in TOJs, even in a cluttered and dynamic target display containing multiple heterogeneous distractors. In Experiment 2, consistent with contingent capture, we observed reliable cuing effects only when the singleton cue matched participants' current attentional control set. Experiment 3 suggests that a sensory interaction account of the differences found in Experiment 2 is unlikely. Our results help to discern the attentional components that may play a role in contingent capture. Further, we discuss a number of other effects (e.g., reversed cuing effects) that are found in RTs, but so far have not been reported in TOJs. Those differences suggest that RTs are influenced by a multitude of mechanisms; however, not all of these mechanisms may affect TOJs. We conclude by highlighting how the study of attentional capture in TOJs provides valuable insights for the attention literature, but also for studies concerned with the perceived timing between stimuli.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".