Response generation, not response execution, influences feelings of rightness in reasoning
Bibliographic record
Abstract
(i.e., the ability to quickly generate an initial response) during processing influences one's likelihood of engaging reflectively when reasoning. This is a key facet of Metacognitive Reasoning Theory (MRT) and numerous studies have found support for this claim by showing that answers that come to mind quickly, are associated with higher feelings of rightness (FORs), and less reflective processing. However, the possibility remains that the critical determinant of FORs may be the speed of executing a response and not generating a response, given the nature of the evidence for this claim. Across two experiments, we manipulated the duration of the response execution to identify whether participants' FOR judgements are at least partially based on factors occurring after the initial mental generation of an answer. We found no evidence that FORs nor reflection are influenced by a manipulation of response execution. Broadly, the present investigation provides evidence that the relation between speed of response and FORs is likely due to the speed with which an answer is generated internally, and not the response execution phase. These findings are consistent with Metacognitive Reasoning Theory and provide further support for the suggestion that answer fluency is the critical variable in determining FORs. All data, scripts, and materials can be found at https://osf.io/f48az/.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".