Becoming fluent overnight: Long-lasting influences of perceptual learning on metamemory.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Judgements of learning (JOLs) are metacognitive evaluations of future memory for newly learned information (Fiacconi et al., 2020; Koriat, 1997). The cue utilization view of JOLs states that individuals use a variety of cues when predicting future memory performance (Koriat, 1997). Critically, however, the majority of research aimed at understanding how different types of cues influence individuals' JOLs has focused on immediate memory assessments based on individuals' in-the-moment experiences or has utilized very brief retention intervals and relied on the representation of previously studied material (Rhodes & Tauber, 2011). Importantly, individuals' assessments of new learning may also be coloured by information learned further in the past when it is similar to the current information. Using a letter set training procedure (Fiacconi et al., 2020), we manipulated the fluency of to-be-learned material to examine whether previous learning would influence JOLs for new material over a 24-hr time period. As hypothesized, our results showed that previous learning did impact individuals' metamemory predictions, as JOLs for distinct but similar items were indeed higher than those for novel dissimilar items both immediately following training and 24 hr later. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it