Bibliographic record
Abstract
This study explores the influence of emotional states on decision-making under risk, particularly examining how can emotions like happiness and sadness affect human risk-seeking behaviors. The experiment involved 53 Chinese participants divided into two groups, each subjected to an emotion manipulation through a short film clip to induce happiness or sadness. After verifying emotional states, participants engaged in a gamble game designed to measure risk-seeking versus risk-averse choices across various scenarios involving gains and losses. The results revealed that participants in the sad condition exhibited a higher propensity for risk-seeking behavior (60%) compared to those in the happy condition (44.44%). Moreover, a significant difference was observed between gain and loss sections within the sad group, with risk-seeking behavior being more pronounced in the loss section. The t-test results (t = 2.66, p = 0.0104) indicated a statistically significant difference in risk-seeking behavior between the two emotional states. These findings suggest that emotion significantly impacts decision-making processes under risky situations, with sadness promoting greater risk-seeking tendencies. The study contributes to understanding the emotional drivers behind decision-making and highlights the importance of accounting for emotional states in models of risk-based decision-making.
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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 | 0.002 |
| 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 | 0.000 |
| 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".