Motivation, intentionality, and mind wandering: Implications for assessments of task-unrelated thought.
Bibliographic record
Abstract
Researchers of mind wandering frequently assume that (a) participants are motivated to do well on the tasks they are given, and (b) task-unrelated thoughts (TUTs) that occur during task performance reflect unintentional, unwanted thoughts that occur despite participants' best intentions to maintain task-focus. Given the relatively boring and tedious nature of most mind-wandering tasks, however, there is the possibility that some participants have little motivation to do well on such tasks, and that this lack of motivation might in turn result in increases specifically in intentional TUTs. In the present study, we explored these possibilities, finding that individuals reporting lower motivation to perform well on a sustained-attention task reported more intentional relative to unintentional TUTs compared with individuals reporting higher motivation. Interestingly, our results indicate that the extent to which participants engage in intentional versus unintentional TUTs does not differentially relate to performance: both types of off-task thought were found to be equally associated with performance decrements. Participants with low levels of task-motivation also engaged in more overall TUTs, however, and this increase in TUTs was associated with greater performance decrements. We discuss these findings in the context of the literature on mind wandering, highlighting the importance of assessing the intentionality of TUTs and motivation to perform well on tasks 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 | 0.006 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".