Bibliographic record
Abstract
Attributing outcomes to luck is common across cultures, yet its psychological function remains unclear outside of decision-making or gambling contexts. While past work (e.g., Langer, 1975; Weiner, 1985; Darke & Freedman, 1997) suggests that attributing failure to luck can protect self-esteem, boost confidence, and even encourage risk-taking, much less is known about the function of such attributions in everyday situations. This study investigates how attributing success or failure to luck versus effort or skill influences self-efficacy, self-worth, and emotional responses. Using a modified Iowa Gambling Task, participants select from decks with predetermined win/loss patterns, while receiving manipulated feedback. Outcomes are framed either as luck-based (e.g., “That was unlucky”) or effort-based (e.g., “Adjust your strategy”). To capture the immediate effects of attribution, participants report their self-evaluations and emotions at regular intervals across trials. This design allows us to assess within-subject fluctuations over time, while also examining between-subject differences. We hypothesize that attributing failure to luck will serve a self-protective function, with participants reporting higher self-efficacy and less negative affect than in effort-based feedback conditions. Conversely, attributing success to effort is expected to provide stronger affirmation of competence than attributing success to luck. We further predict that these effects will be moderated by individual performance, such that participants in losing conditions may benefit more from luck attributions following failure. We also hypothesise some cultural difference or interaction regarding the functionality of luck, and plan to explore such variations. By clarifying the role of luck attributions in self-regulation, this research extends attribution theory beyond gambling contexts to everyday achievement settings. Our findings aim to illuminate how cultural and individual differences in attribution shape not only external behavior but also the internal processes of self-evaluation, motivation, and emotional regulation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".