Revisiting the remember–know task: Replications of Gardiner and Java (1990)
Bibliographic record
Abstract
One of the most evidential behavioral results for two memory processes comes from Gardiner and Java (Memory & Cognition, 18, 23-30 1990). Participants provided more "remember" than "know" responses for old words but more know than remember responses for old nonwords. Moreover, there was no effect of word/nonword status for new items. The combination of a crossover interaction for old items with an invariance for new items provides strong evidence for two distinct processes while ruling out criteria or bias explanations. Here, we report a modern replication of this study. In three experiments, (Experiments 1, 2, and 4) with larger numbers of items and participants, we were unable to replicate the crossover. Instead, our data are more consistent with a single-process account. In a fourth experiment (Experiment 3), we were able to replicate Gardiner and Java's baseline results with a sure-unsure paradigm supporting a single-process explanation. It seems that Gardiner and Java's remarkable crossover result is not replicable.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".