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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".