Patterns of Ongoing Thought in the Real-World
Bibliographic record
Abstract
Abstract Previous research has indicated that health and well-being are impacted on by both the way we think, and the things we do. In the laboratory, studies suggest that specific task contexts affect this process because the people we are with, the places we are in, and the activities we perform may influence our thought patterns. In our study participants completed multi-dimensional experience-sampling surveys eight times per day for 5 days to generate thought data across a variety of dimensions in daily life. Principal component analysis was used to decompose the experience sampling data, and linear mixed modelling related these patterns to the activity in daily life in which they emerged. Our study replicated the influence of socializing on patterns of ongoing thought observed in our prior study and established that this is part of a broader set of relationship that links our current activities to how our thoughts are organised in daily life. We also found that factors such as time of day and the physical location are associated with reported patterns of thought, factors that are important for future studies to explore. Our study suggests that sampling thinking in the real world may be able to provide a set of comprehensive thinking-activity mappings that will be useful to researchers and health care professionals interested in health and well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".