How Do You Know If You Were Mind Wandering? Dissociating Explicit Memories of Off Task Thought From Subjective Feelings of Inattention
Bibliographic record
Abstract
Mind wandering is a common experience in which your attention drifts away from the task at hand and toward task-unrelated thoughts. To measure mind wandering we typically use experience sampling and retrospective self-reports, which require participants to make metacognitive judgments about their immediately preceding attentional states. In the current study, we aimed to better understand how people come to make such judgments by introducing a novel distinction between explicit memories of off task thought and subjective feelings of inattention. Across two preregistered experiments, we found that participants often indicated they were "off task" and yet had no memory of the content of their thoughts-though, they were less common than remembered experiences. Critically, remembered experiences of mind wandering and subjective feelings of inattention differed in their behavioral correlates. In Experiment 1, we found that only the frequency of remembered mind wandering varied with task demands. In contrast, only subjective feelings of inattention were associated with poor performance (Experiments 1 and 2) and individual differences in executive functioning (Experiment 2). These results suggest that the phenomenology of mind wandering may differ depending on how the experiences are brought about (e.g., executive functioning errors versus excess attentional resources), and provide preliminary evidence of the importance of measuring subjective feelings of inattention when assessing 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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".