The effects of learning on visual search and change detection
Bibliographic record
Abstract
Despite the fact that humans can readily recognize visual objects from one moment to the next, recent work has shown that we have detailed information about only a handful of objects at any one time. One interesting phenomena that highlights this limitation is ‘change blindness’. This is the striking phenomenon whereby individuals have difficulty detecting changes to visual stimuli. In a series of experiments, a visual search task was imbedded within a flicker or change detection paradigm. The target was defined by a change across two visual displays separated by a blank temporal gap. Each display contained identical items at each location, except for at the target location, which contained different items. The sequence of visual displays and gaps were cycled until observers detected the changing target item. This paradigm is particularly useful because the type of change, at the target location, can be manipulated. In the current series of experiments the type of change was varied in terms of features (i.e., the number of features changing at the target location) and familiarity of the change (e.g., a changing familiar object vs. unfamiliar object). In addition, we investigated the extent to which change detection performance varied as a function of processing time (i.e., display duration) and practice (i.e., training sessions). The results provide strong support for the idea that visual changes can be detected using both featural-level information (number of features) and object-level information (familiar vs. unfamiliar object). The results are also discussed in terms of the shift from feature-based to object-based processing, and the degree to which change detection performance improved as a function of learning.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".