Agentic aspects of attentional disengagement.
Bibliographic record
Abstract
Moments of inattention are commonly experienced by most people in everyday life. While some of these inattentive episodes occur inadvertently, others are the result of an individual's choices. Here we characterize the aspects of inattention over which individuals have agency, with a specific consideration of the inattentive state of mind wandering (i.e., off-task thought). Our research on this topic has revealed several general principles that underpin people's agency over their inattention to a task at hand. First, people have nuanced awareness of their inattentive episodes, reporting various degrees of both spontaneous and deliberate moments of inattention, even during tasks in which they agree to be attentive. Second, spontaneous and deliberate bouts of attentional disengagement have distinct correlates and underlying mechanisms. Third, people have considerable control over the degree of their inattention and are able to regulate it skillfully on command and strategically in response to situational demands. Fourth, people have reliable memories of prior moment-to-moment changes in their inattention. And finally, individuals are able to accurately forecast how their inattention might change in an upcoming task. These principles suggest an agentic view of inattention, according to which people are imperfect but intelligent managers of their attentional engagement and disengagement. We discuss the wide-ranging implications of this view on prior and future studies of attentional disengagement, the interpretability of performance in cognitive tasks, and strategies that can be implemented to reduce inattention. (PsycInfo Database Record (c) 2025 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".