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 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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".