Does contingent capture occur in driving scenes?
Bibliographic record
Abstract
Contingent capture theory suggests that only stimuli consistent with the observer’s internal goals will capture attention. For example, when looking for a red target, red stimuli should automatically capture observers’ attention. It is unclear whether contingent capture occurs in complex real-world scenes. Arexis et al. (2017) investigated contingent capture using a search task where participants viewed photographed driving scenes in which a single red letter appeared at random locations. Their task was to decide whether the red target letter was a T or an L. A GPS navigation system image was also shown in the bottom right corner of the display. The GPS either had a blank screen, a red-coloured route (goal-relevant distractor colour) or a green-coloured route (goal-irrelevant distractor colour). The GPS appeared 1 second in advance of the driving scene and letter, acting as a pre-search display. If contingent capture occurs, participants should be slower to respond when the GPS showed a red route (goal-relevant distractor colour). However, goal-relevance had no effect, perhaps because the GPS pre-search display appeared so far in advance of the search display (1 s). In the present study, we manipulated the presentation duration of the pre-search display (0 ms, 100 ms, 1 s) and instructed participants to ignore the GPS. We predicted that goal-relevance would have an effect, but only when there was insufficient time to disengage attention from the GPS distractor before search display onset (the 0 and 100 ms conditions). Results call into question the contingent capture hypothesis within the context of real-world scenes.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".