Base rates: Both neglected and intuitive.
Bibliographic record
Abstract
Base-rate neglect refers to the tendency for people to underweight base-rate probabilities in favor of diagnostic information. It is commonly held that base-rate neglect occurs because effortful (Type 2) reasoning is required to process base-rate information, whereas diagnostic information is accessible to fast, intuitive (Type 1) processing (e.g., Kahneman & Frederick, 2002). To test this account, we instructed participants to respond to base-rate problems on the basis of "beliefs" or "statistics," both in free time (Experiments 1 and 3) and under a time limit (Experiment 2). Participants were given problems with salient stereotypes (e.g., "Jake lives in a beautiful home in a posh suburb") that either conflicted or coincided with base-rate probabilities (e.g., "Jake was randomly selected from a sample of 5 doctors and 995 nurses for conflict; 995 doctors and 5 nurses for nonconflict"). If utilizing base-rates requires Type 2 processing, they should not interfere with the processing of the presumably faster belief-based judgments, whereas belief-based judgments should always interfere with statistics judgments. However, base-rates interfered with belief judgments to the same extent as the stereotypes interfered with statistical judgments, as indexed by increased response time and decreased confidence for conflict problems relative to nonconflict. These data suggest that base-rates, while typically underweighted or neglected, do not require Type 2 processing and may, in fact, be accessible to Type 1 processing.
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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 | 0.006 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".