The moderating role of processing style in risk perceptions and risky decision making
Bibliographic record
Abstract
Abstract When evaluating risks, such as skydiving or taking an experimental drug, there are both possible harms and benefits to consider. In the current research, we hypothesize that individuals evaluating risks visually possibly see greater potential harms—but not more potential benefits—compared with those doing so verbally. This is likely because visualizing risks is inherently an affective experience, and negative affect (e.g., potential harms in risk taking) is more dominant than positive affect (e.g., potential benefits). This means that perceived harms are greater for visualizers, reducing their willingness to take risks. We obtain support for this theorizing across four studies, with visualizing individuals more likely to see harms from taking risks, leading to their risk aversion. This research thus demonstrates that visualizing risks asymmetrically shapes how individuals evaluate the two main components of risk taking (perceived harms, perceived benefits). We discuss the application of our findings to how individuals perceive risks in both the marketplace and policy settings.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".