Responses to Success: Seeking Pleasant Experiences before a Task Is Complete?
Bibliographic record
Abstract
Although engaging in pleasant experiences following successful performance may be hedonically rewarding, in the present research we proposed that individuals might forego pleasant experiences when they have not yet completed a task. In Study 1 (N = 100), participants reported the extent to which they would like to engage in pleasant experiences in a hypothetical situation where their performance outcome on a task (successful vs. average) and task completion (task in progress vs. completed) were manipulated. In Study 2 (N = 115), participants were in a real situation in which they achieved either a successful or average performance outcome. Task completion was manipulated (task in progress vs. completed) and motivation to engage in a pleasant experience was assessed by a behavioral measure. Results of both studies provided support for our prediction by showing individuals to have a lower desire to engage in pleasant experiences following successful, but not average, performance when the task was in progress than when it was complete. These findings are discussed in light of the underlying mechanisms and consequences of the tendency to forego pleasant experiences.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".