Meaning and Attention Intertwined: Experimental and Experience-Sampling Findings
Bibliographic record
Abstract
How does one attain meaning? Though pivotal to well-being, this question has been explored mainly within symbolic and philosophical domains, with little focus on its cognitive processes. We present a theoretical integration of meaning and attention, followed by five studies investigating their relationship in lab experiments and everyday life (total N = 1,654). Experimental findings indicate that meaning increased attention (Studies 1 and 3), and attention increased meaning, but only when meaning could be found in a stimulus (Studies 2a, 2b, and 3). An experience-sampling study further reveals a positive meaning–attention association at dispositional, situational, and cross-levels (Study 4). Across varied daily activities, participants reported greater meaning when they paid more attention. These studies also explored the interplay of meaning and attention with boredom, negative emotions, and subjective well-being. Together, our results suggest that paying attention during everyday activities can, in some instances, enhance the experience of meaning.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".