Isolating the contribution of perceptual fluency to judgments of learning (JOLs): Evidence for reactivity in measuring the influence of fluency.
Bibliographic record
Abstract
Judgments of learning (JOLs) refer to explicit predictions regarding the likelihood of remembering newly acquired information on a later test of memory. In recent years, there has been considerable interest in understanding the processes that underlie such judgments. Recent theorizing on this matter has characterized JOLs as inferential in nature-that is, they are derived from the implicit utilization of a variety of different cues of which only some are diagnostic of future memory performance. The present series of experiments examine the potential role for one such cue, namely, perceptual fluency (i.e., the subjective ease of perceiving a stimulus) in guiding JOLs. Using a novel methodological approach adapted from Masson (1986), we demonstrate across 6 experiments that perceptual fluency per se can indeed inform predictions of future memory performance, but that its influence depends on the specific task requirements at the time JOLs are solicited. We discuss these results in relation to experience- versus theory-based contributions to metamemory judgments. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".