How task-unrelated and freely moving thought relate to affect: Evidence for dissociable patterns in everyday life.
Bibliographic record
Abstract
Much of our knowledge about the correlates of mind-wandering comes from assessing task-unrelated thought. Less is known about the correlates of freely moving thought, a dimension that assesses the degree to which thoughts arise and unfold with low levels of guidance or constraints. Task-unrelated thought is consistently associated with more negative affective valence compared with being on task; however, it is unclear whether freely moving thought shares the same relationship with affect. We conducted two ecological momentary assessment studies in the context of everyday life and found that the two dimensions have different affective correlates. In Study 1, task-unrelated thought was associated with less positive concurrent valence than being on-task. However, freely moving thought was associated with more positive concurrent valence and was predictive of more positive valence at a subsequent timepoint. Freely moving thought, but not task-unrelated thought, also positively predicted concurrent arousal. Computational sentiment analyses of participants' thought descriptions offered convergent evidence of differential relationships between the two thought dimensions and affect. Study 2 used different measurement scales to assess whether (a) the pattern of findings replicated and (b) if the effects were robust to changes in measurement. The main findings were replicated: task-unrelated thought was negatively associated with concurrent valence, whereas freely moving thought was positively associated with concurrent valence. However, freely moving thought did not predict subsequent valence and was not related to concurrent arousal. The most robust findings related to concurrent valence. Although mind-wandering has acquired a relatively negative reputation to date, our findings suggest that there might be positive aspects that remain unexplored. (PsycInfo Database Record (c) 2021 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 | 0.006 |
| 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 | 0.000 | 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".