Bibliographic record
Abstract
Popular dual process theories of reasoning and decision making have characterized human thinking as an interplay of an intuitive and analytic reasoning process.Although monitoring the output of the two systems for conflict is crucial to avoid decision making errors there are some widely different views on the efficiency of the process.Kahneman (2002) claims that the monitoring of the intuitive system is typically quite lax whereas others such as Sloman (1996) and Epstein (1994) claim it is flawless and people typically experience a struggle between what they "know" and "feel" in case of a conflict.The present study contrasted these views.Participants solved classic base rate neglect problems while thinking aloud.Verbal protocols showed no evidence for an explicitly experienced conflict.As Kahneman predicted, participants hardly ever mentioned the base rates and seemed to base their judgment exclusively on heuristic reasoning.However, a more implicit measure of conflict detection based on participants' retrieval of the base rate information in an unannounced recall test showed that the base rates had been thoroughly processed.Results indicate that although the popular characterization of conflict detection as an actively experienced struggle needs to be revised there is nevertheless evidence for Sloman and Epstein's basic claim about the flawless operation of the conflict monitoring process.
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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 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".