Refixation patterns of mind-wandering during real-world scene perception.
Bibliographic record
Abstract
Recent studies have shown that mind-wandering (MW) is associated with changes in eye movement parameters, but have not explored how MW affects the sequential pattern of eye movements involved in making sense of complex visual information. Eye movements naturally unfold over time and this process may reveal novel information about cognitive processing during MW. The current study used Recurrence Quantification Analysis (Anderson, Bischof, Laidlaw, Risko, & Kingstone, 2013) to describe the pattern of refixations (fixations directed to previously inspected regions) during MW. Participants completed a real-world scene encoding task and responded to thought probes assessing intentional and unintentional MW. Both types of MW were associated with worse memory of the scenes. More important, RQA showed that scanpaths during unintentional MW were more repetitive than during on-task episodes, as indicated by a higher recurrence rate and more stereotypical fixation sequences. This increased repetitiveness suggests an adaptive response to processing failures through reexamining previous locations. Moreover, this increased repetitiveness contributed to fixations focusing on a smaller spatial scale of the stimuli. Finally, we were also able to validate several traditional measures: both intentional and unintentional MW were associated with fewer and longer fixations; eye-blinking increased numerically during both types of MW but the difference was only significant for unintentional MW. Overall, the results advanced our understanding of how visual processing is affected during MW by highlighting the sequential aspect of eye movements. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".