Storing visual object features and locations across saccades
Bibliographic record
Abstract
In a series of studies, we tested how many object features and locations could be retained across saccades in 6 human subjects. Visual targets were either circular gabor patches or luminance disks. The Saccade Task consisted of briefly presenting a random number of targets (as many as 15). Each target's spatial position and orientation or luminance was varied randomly. Then, subjects saccaded to a different location and were briefly presented with a probe. The probe's orientation or luminance was systematically varied relative to the pre-saccadic target at the same location (the 80% detection amount determined from preliminary one-target trials). Subjects reported how the probe's visual feature differed from the original target. We compared the performance in this task to a Fixation Task which was identical except subjects maintained eye-fixation throughout the trial. The magnetic search coil technique was used for precise monitoring of eye movements. Results showed that up to 6 targets the subjects' accuracy in the Saccade Task was the same as in the Fixation Task. For trials with more than 6 targets, performance in both tasks declined but the Saccade Task declined at a faster rate than the Fixation Task. This decline was not observed when the test target was attentionally cued - showing that these were not low-level effects. Moreover, subjects' performance was poorer with larger saccade amplitudes than smaller saccades. These findings suggest that limits of transsaccadic memory depend on the number of objects it can retain and the size of the saccade.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".