Becoming fluent overnight: Long-lasting influences of perceptual learning on metamemory.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| 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".