Rapid Resumption is modulated by high-level strategies.
Bibliographic record
Abstract
If a display is interrupted while participants search for a target (a T among Ls) they are able to resume search rapidly when the display reappears (Enns, Rensink, Vandenbeld & Lleras, 2003). This has been called Rapid Resumption (RR) because whereas correct responses to an initial display do not begin before 500 ms have elapsed, many responses following its reappearance are initiated within 100–400 ms. In this study we tested for the influence of high-level strategies on RR, by varying the likelihood that the display would reappear. On repeated-look trials, each presentation was 100 ms, separated by intervals of 900 ms. On single-look trials, only one 100 ms presentation occurred. Experiment 1 included a random mix of 80% repeated looks and 20% single looks; Experiment 2 contained an equal mix of repeated and single looks (50%). If RR depends on participant's strategy then it should be reduced in Experiment 2, where repeated looks were much less likely to occur. Also, when uncertain about the target identity, participants may withhold responses while waiting for confirmation if they know reappearance is likely. Consistent with a strategic component, the results for repeated-look trials showed much stronger evidence for RR in Experiment 1 than in Experiment 2. The results of the single look trials also supported this interpretation: participants were more likely to find the target in a single look when these trials were more frequent. However, neither experiment provided evidence that participants were withholding responses following a single-look: responses made once participants realized the display would not reappear contained an equal number of correct and incorrect responses. These findings show that (1) RR can be influenced by strategy, but (2) these do not include the strategic withholding of responses. Instead, RR is interpreted as an index of the interactions that normally occur between sensory input and perceptual hypotheses in everyday vision. Grant from Nissan Motor Company Ltd. to Ronald Rensink and James Enns and NSF postdoctoral fellowship to Alejandro Lleras.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".