A Single Process for Deductive and Inductive Inference? Examining the Impact of Conclusion Typicality and Argument Validity on Immediate Inferences
Bibliographic record
Abstract
Inductive and deductive inferences have been assumed to rely on two qualitatively distinct processes by dual-process theories. However, studies examining the predictions of this theory have yielded mixed results, with several studies showing that a single process underlies both deductive and inductive judgments. Previous studies have used a range of manipulations, response options, and analytical techniques, which might partly explain the inconclusive findings. In this study, we conducted five experiments (overall N = 614) manipulating the typicality of the category-member relationship (typical vs. atypical pairs) and the quantifier of premises (all/universal vs. most/particular) in reasoning arguments. Dual-process theories predict a double-dissociation pattern in which the quantifier manipulation would impact deductive judgments more than inductive judgments, while typicality would have the reverse effect. To test these predictions, we employed a range of experimental tasks (within- and between-subject), response formats (binary and Likert), and analytical techniques (Bayesian hierarchical regression and state-trace analysis). The results failed to support the dual-process theory in that the predicted double-dissociation effect was not observed in most of the experiments. These findings align with a single-process framework, as proposed by the new paradigm of reasoning, for both deductive and inductive inferences. The implications of these findings for both dual- and single-process accounts are discussed.
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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 | 0.040 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".