The influence of scene context on parafoveal processing of objects
Bibliographic record
Abstract
Many studies in reading have shown the enhancing effect of context on the processing of a word before it is directly fixated (parafoveal processing of words).Here, we examined whether scene context influences the parafoveal processing of objects and enhances the extraction of object information.Using a modified boundary paradigm called the Dot-Boundary paradigm, participants fixated on a suddenly onsetting cue before the preview object would onset 4° away.The preview object could be identical to the target, visually similar, visually dissimilar or a control (black rectangle).The preview changed to the target object once a saccade toward the object was made.Critically, the objects were presented on either a consistent or an inconsistent scene background.Results revealed that there was a greater processing benefit for consistent than inconsistent scene backgrounds and that identical and visually similar previews produced greater processing benefits than other previews.In the second experiment, we added an additional context condition in which the target location was inconsistent, but the scene semantics remained consistent.We found that changing the location of the target object disrupted the processing benefit derived from the consistent context.Most importantly, across both experiments, the effect of preview was not enhanced by scene context.Thus, preview information and scene context appear to independently boost the parafoveal processing of objects without any interaction from object-scene congruency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".