The effects of feedback on targeting with multiple moving targets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A number of task settings involve selection of objects from dynamic visual environments with multiple moving targets. Target selection is difficult in these settings because objects move, because there are a number of distracter objects for any targeting action, and because objects can occlude the target. Target feedback has been suggested as a way to assist targeting in visual environments. We carried out an experiment to test the effects of visual target feedback. We found that targeting does become more difficult as the number and speed of objects increases, and that feedback can improve error rates. When feedback was provided on all objects in the space, performance improved significantly over no feedback. Target-only feedback, however, was not significantly better than no feedback. This is a valuable result because all-object feedback is in most cases the only implementation option - since it is usually not possible to pre-determine the user's target among the set of objects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it