False memories of the future: A critique of the applications of probabilistic reasoning to the study of cognitive processes.
Bibliographic record
Abstract
The authors argue that the ways in which people-scientists and laymen-use probabilistic reasoning is predicated on a set of often questionable assumptions that are implicit and frequently go untested. They relate to the correspondence between the terms of a theory and the observations used to validate the theory and to the implicit understandings of intention and prior knowledge that arise between the conveyer and the receiver of information. The authors show several ways in which the use of probabilistic reasoning rests on a priori commitments to a partitioning of an outcome space and demonstrate that there are many more assumptions underlying the use of probabilistic reasoning than are usually acknowledged. They unfold these assumptions to show how several different interpretations of the same results in behavioral decision theory and cognitive psychology are equally well supported by "the facts." They then propose a more comprehensive approach to mapping cognitive processes than those currently used, one that is based on the analysis of all of the relevant alternative interpretations presented in the article.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".