The Impact of Attentional Fluctuations on Performance in an Image Flanker Task
Bibliographic record
Abstract
Sustained attention is essential for tasks requiring prolonged focus. Yet, our attention fluctuates, even for simple tasks, as our minds shift in and out of focus. Such lapses in attention are called mind wandering (MW) and are often associated with performance declines. The flanker task is a well-established paradigm to study the ability to suppress irrelevant information and respond accurately to the target stimuli. When combined with MW assessments, it has been found that during MW periods, performance on incongruent trials decreases. Previous studies have often used symbolic stimuli, such as arrows, numbers, or letters, which may limit transfer to real-world situations. Here, we tested whether “in-zone” (high task engagement) and “out-of-zone” (low task engagement) periods affect performance in a task involving images of indoor/outdoor scenes and living/non-living items. Participants (N = 47) completed an image Flanker task designed to assess sustained attention through response times (RTs) and accuracy. Results showed significant differences between “in-zone” and “out-of-zone” conditions. Participants in the "in-zone" condition exhibited faster RTs and higher accuracy compared to the "out-of-zone" condition. In the “in-zone” condition, no significant differences in RTs were found between congruent and incongruent trials, whereas the “out-of-zone” condition showed significant RT differences, with incongruent trials slower than congruent ones. For accuracy, no significant differences were found in either zone. These results align with theoretical models, suggesting that attentional fluctuations directly impact task performance. Our findings underscore the dynamic nature of sustained attention and its significant influence on task performance. The clear distinctions between “in-zone” and “out-of-zone” conditions highlight the critical role of attentional fluctuations in shaping behavioural outcomes. Future studies will identify the ideal duration of attention-demanding tasks and effective break strategies that could inform interventions to enhance focus and productivity across various settings.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".