Where Did they Go? Recovering Dynamic Objects after Interruptions
Bibliographic record
Abstract
The aim of the current study was to identify strategies used in recovering dynamic objects after long interruptions. The primary task in this study required participants to track a set of six target dots amongst eight distracter dots and locate them after a 30 second interruption. In one condition the dots reappeared in their displaced locations (moving condition) while in the other condition they reappeared in their original pre-interruption locations (not-moving condition). Consistent with previous research we found that the not-moving condition had significantly better accuracy than the moving condition. Above chance accuracy was found in both conditions. Reaction time data provided further insight into the strategies used to recover displaced objects. In the end, it was concluded that pre-interruption location is the most salient and easily remembered characteristic. Reaction time data did provide preliminary support for the use of on-line tracking during interruptions, although such abilities seem to be limited in capacity to approximately three targets. The results of this research have wide spread implications to domains requiring constant tracking of objects such as air traffic control.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".