Mapping cognition across lab and daily life using Experience-Sampling
Bibliographic record
Abstract
• Understanding cognition in daily life depends on mapping mechanistic accounts from the lab onto real world behaviour. • Applying PCA to experience sampling generated a ‘thought space’ that integrated data from both contexts in an unbiased manner. • The ‘thought space’ effectively organised activities in daily life and tasks in the lab with corresponding features. • Our study shows experience sampling supports richer models of cognition that encompass both controlled lab tasks and real-world situations. The goal of psychological research is to understand behaviour in daily life. Although lab studies provide the control necessary to identify cognitive mechanisms behind behaviour, how these controlled situations generalise to activities in daily life remains unclear. Experience-sampling provides useful descriptions of cognition in the lab and real world and the current study examined how thought patterns generated by multidimensional experience-sampling (mDES) generalise across both contexts. We combined data from five published studies to generate a common ‘thought-space’ using data from the lab and daily life. This space represented data from both lab and daily life in an unbiased manner and grouped lab tasks and daily life activities with similar features (e.g., working in daily life was similar to working memory in the lab). Our study establishes mDES can map cognition from lab and daily life within a common space, allowing for more ecologically valid descriptions of cognition and behaviour.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".