Bibliographic record
Abstract
A key assumption of Mental Model theory (Johnson-Laird & Byrne, 1991, 2002) is that reasoners should use a minimal representation of the premises, called the initial model, in order to reduce the cognitive load involved in the processing of more than one model. However, there is no direct evidence for this postulate. In the following studies, we modified the ability of participants to process conditional (if-then) inferences in more complex ways by varying the degree of arbitrariness of the conditionals and by restricting the time allotted. Study 1 used premises with arbitrary relations with explicit negations in both terms in order to control for a possible matching strategy, with 9 s, 15 s, or unlimited processing time. Results show a significant number of initial model patterns, which increased with time. No evidence for use of a matching strategy was found. Study 2 involved arbitrary relations without negations, with 6 s or 8 s processing time. This study showed a significant increase in initial model patterns at the longer times. Study 3 used premises with familiar relations with either very limited processing times (5 s, 7 s) or an unlimited time condition. Results show very low numbers of initial model patterns in the three time conditions. Overall, these studies provide clear evidence that reasoners do use an initial model form of reasoning, and suggest that this is done mostly because of difficulty of processing more abstract content.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".