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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".