The role of training, alternative models, and logical necessity in determining confidence in syllogistic reasoning
Bibliographic record
Abstract
Prior research shows that reasoners' confidence is poorly calibrated (Shynkaruk & Thompson, Citation2006). The goal of the current experiment was to increase calibration in syllogistic reasoning by training reasoners on (a) the concept of logical necessity and (b) the idea that more than one representation of the premises may be possible. Training improved accuracy and was also effective in remedying some systematic misunderstandings about the task: those in the training condition were better at estimating their overall performance than those who were untrained. However, training was less successful in helping reasoners to discriminate which items are most likely to cause them difficulties. In addition we explored other variables that may affect confidence and accuracy, such as the number of models required to represent the problem and whether or not the presented conclusion was necessitated by the premises, possible given the premises, or impossible given the premises. These variables had systematically different relationships to confidence and accuracy. Thus, we propose that confidence in reasoning judgements is analogous to confidence in memory retrievals, in that they are inferentially derived from cues that are not diagnostic in terms of accuracy.
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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 | 0.010 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".