Alternation between different types of evidence attenuates judgments of severity
Bibliographic record
Abstract
Most real-world judgments and decisions require the consideration of multiple types of evidence. For example, judging the severity of environmental damage, medical illness, or negative economic trends often involves tracking and integrating evidence from multiple sources (i.e. different natural disasters, physical symptoms, or financial indicators). We hypothesized that the requirement to track and integrate across distinct types of evidence would affect severity judgments of multifaceted problems, compared to simpler problems. To test this, we used scenarios depicting crop damage. Each scenario involved either two event types (i.e. mold damage and insect damage), or one event type. Participants judged the quality of the crop following each scenario. In Experiments 1 and 2, subjective judgments were attenuated if the scenario depicted multiple event types, relative to scenarios depicting single event types. This was evident as a shallower slope of subjective severity ratings, as a function of objectively quantifiable severity, for scenarios with multiple event types. In Experiment 3, we asked whether alternation between event types might contribute to this attenuation. Each scenario contained two event types, and the sequence of events either alternated frequently between types or was organized into two sequential groups. Subjective judgments were attenuated for scenarios with frequently alternating sequences. The results demonstrate that alternation between distinct event types attenuates subjective judgments of severity. This suggests that a requirement to integrate evidence across multiple sources places extra demands on the cognitive system, which reduces the perceived evidence strength.
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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 | 0.007 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".