The Effect of Information Formats and Incidental Affect on Prior and Posterior Probability Judgments
Bibliographic record
Abstract
Background. Interpreting medical test results involves judging probabilities, including making Bayesian inferences such as judging the positive and negative predictive values. Although prior work has shown that experience formats (e.g., slide shows of representative patient cases) produce more accurate Bayesian inferences than description formats (e.g., verbal statistical summaries), there are disadvantages of using the experience format for real-world medical decision making that may be solved by presenting relevant information in a 2 × 2 table format. Furthermore, medical decisions are often made in stressful contexts, yet little is known about the influence of acute stress on the accuracy of Bayesian inferences. This study aimed to a) replicate the description-experience format effect on probabilistic judgments; b) examine judgment accuracy across description, experience, and a new 2 × 2 table format; and c) assess the effect of acute stress on probability judgments. Method. The study employed a 2 (stress condition) × 3 (format) factorial between-subjects design. Participants ( N = 165) completed a Bayesian inference task in which information about a medical screening test was presented in 1 of 3 formats (description, experience, 2 × 2 table), following a laboratory stress induction or a no-stress control condition. Results. Overall, the 2 × 2 table format produced the most accurate probability judgments, including Bayesian inferences, compared with the description and experience formats. Stress had no effect on judgment accuracy. Discussion. Given its accuracy and practicality, a 2 × 2 table may be better suited than description or experience formats for communicating probabilistic information in medical contexts.
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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 | 0.006 | 0.032 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".