Issues in Reasoning about Iffy Propositions: Reasoning Times in the Syntactic-Semantic Counter-Example Prompted Probabilistic Thinking and Reasoning Engine
Bibliographic record
Abstract
The Syntactic-Semantic Counter-Example Prompted Probabilistic Thinking and Reasoning Engine (SSCEPPTRE; Schroyens et al., 2001;Schroyens & Schaeken, 2003) predicts both conditional inference rates and reasoning times.Acceptance times of MP ('A therefore C"), AC ("C therefore A"), MT ("not-C therefore not-A) and DA (not-A therefore not-C) would follow the order: RT(MP/1) < RT(AC/1) < RT(DA/1) < RT(MT/1).For each of these arguments rejection times would be longer than acceptance times.These predictions are corroborated by an extensive study (N= 350) in which reasoning times were recorded.Participants also solved a truthtable task in which they had to evaluate the different contingencies ('A and C', 'A and not-C', 'not-A and C', 'not-A and not-C').Truth-table evaluation times confirm SSCEPPTRE's expectation that evaluating "not-A and C" takes more time than evaluating "A and not-C".
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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 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".