Catching thoughts: self-caught experience sampling preferentially captures characteristic features of off-task experiences across the life span
Bibliographic record
Abstract
Abstract Understanding transient states, like off-task mind-wandering, is assumed to be improved by capitalizing on our ability to recognize changes in our stream of thought, a process known as meta-awareness. We test this assumption by comparing mind-wandering content when noticed by the participant (self-caught) against those thoughts reported after externally initiated probes (probe-caught). Thirty-eight older and 36 younger individuals completed a cognitive task. At the same time, multiple feature descriptions of thoughts (task-relevance, temporal focus, and self-referential) were captured using self and probe-caught methods. Using a pattern-learning approach, we established that self-caught experiences produce similar but generally “noisier” estimates compared to those reported at probes. However, self-caught experiences contained more off-task characteristics relative to reports at probes. Importantly, despite reductions in off-task thought, older adults retain the ability to self-catch experiences with these features. Our study establishes self-catching ability as an essential means of revealing the detailed content of off-task states, an ability relatively well maintained into old age.
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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 | 0.003 |
| 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 | 0.001 |
| 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".