Can Change Probability Contextual Information Improve the Change Identification Process?
Bibliographic record
Abstract
The visual world is extremely complex, so unconscious mechanisms exist to autonomously direct attention to objects with behavioral importance. One such mechanism – contextual cueing – utilizes the visual context of a scene to focus attention. Therefore, because contextual information unconsciously influences human visual perception, its role in enabling individuals to process scenes is of great interest. This study examined whether contextual information regarding change probability can facilitate the process of change identification. MATLAB and Psychophysics Toolbox Version 3 were used to present abstract scenes in a one-shot change blindness paradigm. Two types of scenes were presented: one in which context was predictive of change likelihood, the other in which context was non-predictive of change likelihood. The accuracy with which subjects detected and localized changes in both scene types was compared, but no significant difference in accuracy was found. This observation suggests that contextual information regarding change probability alone is insufficient to improve the change identification accuracy. Subsequently, it may be that even when individuals are aware that a visual scene is likely to change, they still require additional contextual cues to improve change identification.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".