Bibliographic record
Abstract
Feeling of Rightness (FOR) is a metacognitive experience accompanying people's intuitive answers that predicts the probability of subsequently changing answers (Thompson, Prowse Turner, & Pennycook, 2011). Previous research suggested FOR judgments are influenced by cues such as fluency, i.e., the ease with which an answer comes to mind. In the current paper, we examine the relationship between FOR, fluency, and answer changes; in particular, we were interested in whether answer fluency drives the effect of FOR on subsequent behaviours pertaining to answer changes. Reasoners (N = 64) in each of four experiments were asked to determine the validity of 32 syllogisms that consisted of single-model and multiple-model problems. In addition, each problem was randomly paired with a question containing either a high anchor value (80% or 90%) or a low anchor value (10% or 20%). In the first two experiments, reasoners then provided a FOR rating on a scale from 0 to 100 and indicated whether they would like to attempt to re-answer the question. The last two experiments served as the control experiments in which the FOR judgements were removed. The anchoring manipulation affected FOR judgments but not re-answer choices; it also did not affect answer fluency. Thus influencing FOR without affecting answer fluency had no effect on people's subsequent re-answer choices. In contrast, fluency was a reliable predictor of both FOR and re-answer choices. That is, when answers came to mind slowly, FORs were lower and people were more likely to choose to re-answer the problems. Thus, fluency appears to mediate the relationship between FOR and re-answer choices.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".