Probability matching and statistical naïveté
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract People often probability match: they select choices based on the probability of outcomes. For example, when predicting 10 individual results of a spinner with 7 green and 3 purple sections, many people choose green mostly but not always, even though they would be better off always choosing it (i.e., maximizing). This behavior has perplexed cognitive scientists for decades. Why do people make such an obvious error? Here, we provide evidence that this difficulty may often arise from statistical naïveté: Even when shown the optimal strategy of maximizing, many people fail to recognize that it will produce better payouts than other strategies. In 3 preregistered experiments ( N = 907 Americans tested online), participants made 10 choices in a spinner game and estimated the payout for each of 3 strategies: probability matching, maximizing, and 50/50 guessing. The key finding across experiments is that while most maximizers recognize that maximizing results in higher payouts than matching, probability matchers predict similar payouts for each .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it