Scene meaningfulness guides eye movements even during mind-wandering
Bibliographic record
Abstract
During scene viewing, semantic information in the scene has been shown to play a dominant role in guiding fixations compared to visual salience (e.g., Henderson & Hayes, 2017). However, scene viewing is sometimes disrupted by cognitive processes unrelated to the scene. For example, viewers sometimes engage in mind-wandering, or having thoughts unrelated to the current task. How do meaning and visual salience account for fixation allocation when the viewer is mind-wandering, and does it differ from when the viewer is on-task? We asked participants to study a series of real-world scenes in preparation for a later memory test. Thought probes occasionally occurred after a subset of scenes to assess whether participants were on-task or mind-wandering. We used salience maps (Graph-Based Visual salience; Harel et al., 2007) and meaning maps (Henderson & Hayes, 2017) to represent the distribution of visual salience and semantic richness in the scene, respectively. Because visual salience and meaning were represented similarly, we could directly compare how well they predicted fixation allocation. Our results indicate that fixations prioritized meaningful over visually salient regions in the scene during mind-wandering just as during attentive viewing. These results held across the entire viewing time. A re-analysis of an independent study (Krasich et al., 2020) showed similar results. Therefore, viewers appear to prioritize meaningful regions over visually salient regions in real-world scenes even during mind-wandering.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".