Experimental evidence that exerting effort increases meaning
Bibliographic record
Abstract
Efficiency demands that we work smarter and not harder, but is this better for our wellbeing? Here, we ask if exerting effort on a task can increase feelings of meaning and purpose. In six studies (N = 2883), we manipulated how much effort participants exerted on a task and then assessed how meaningful they found those tasks. In Studies 1 and 2, we presented hypothetical scenarios whereby participants imagined themselves (or others) exerting more or less effort on a writing task, and then asked participants how much meaning they believed they (or others) would derive. In Study 3, we randomly assigned participants to complete inherently meaningless tasks that were harder or easier to complete, and again asked them how meaningful they found the tasks. Study 4 varied the difficulty of a writing assignment by involving or excluding ChatGPT assistance and evaluated its meaningfulness. Study 5 investigated cognitive dissonance as a potential explanatory mechanism. In Study 6, we tested the shape of the effort-meaning relationship. In all studies, the more effort participants exerted (or imagined exerting), the more meaning they derived (or imagined deriving), though the results of Study 6 show this is only up to a point. These studies suggest a causal link, whereby effort begets feelings of meaning. They also suggest that part of the reason this link exists is that effort begets feeling of competence and mastery, although the evidence is preliminary and inconsistent. We found no evidence the effects were caused by post-hoc effort justification (i.e., cognitive dissonance). Effort, beyond being a mere cost, is a source of personal meaning and value, fundamentally influencing how individuals and observers perceive and derive satisfaction from tasks.
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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 | 0.000 | 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) | 0.003 | 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 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".