Meaning and attention intertwined: Experimental and experience-sampling findings
Bibliographic record
Abstract
How does one attain meaning? This age-old question is pivotal to well-being, yet its exploration has been predominantly within the symbolic and philosophic domains. A cognitive approach to this inquiry remains largely unexplored. Here, we propose that paying attention is a process through which one constructs and perceives meaning. In turn, meaning captures and sustains attention. Synthesizing relevant theories and empirical findings, we present a theoretical integration of meaning and attention, hypothesizing their relationship to be bi-directional and causal. Five studies (total N = 1,654) investigated this hypothesis in everyday life and lab experiments. A one-week experience-sampling study first shows a positive association between meaning and attention, evident at dispositional, situational and cross-levels (Study 1). Participants reported a higher sense of meaning when they paid more attention, a within-person effect consistently observed across varied daily activities. A series of experiments then investigated the directionality and causality of their relationship. Meaning was found to causally increase attention (Studies 2 & 4). Attention causally increased meaning, but only when meaning could be found in a stimulus (Studies 3a, 3b & 4). In these studies, we further explored the interplay of meaning and attention with boredom, negative emotions, and subjective well-being. Overall, our research shows that meaning and attention are closely intertwined, providing valuable insights into enhancing them in everyday life.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".