Order information is used to guide recall of long lists: Further evidence for the item-order account.
Bibliographic record
Abstract
Differences in memory for item order have been used to explain the absence of between-subjects (i.e., pure-list) effects in free recall for several encoding techniques, including the production effect, the finding that reading aloud benefits memory compared with reading silently. Notably, however, evidence in support of the item-order account (Nairne, Riegler, & Serra, 1991) has derived primarily from short-list paradigms. We provide novel evidence that the item-order account also applies when recalling long lists. In Experiment 1, participants studied and then free recalled 3 different long lists of words: pure aloud, pure silent, and mixed (half aloud, half silent). A Bayesian analysis supported a null pure-list production effect, and subsequent order analyses were largely consistent with the item-order account. These findings indicate that order information is retained in long-term memory and is useful in guiding subsequent free recall. In Experiment 2, a distractor task was inserted between the study and test phases, ensuring that only long-term memory processes were involved in recall: The pattern of results remained consistent with the item-order account. Order information can be retained in long-term memory for long lists, and is useful in guiding subsequent free recall, extending the domain of the item-order account. (PsycINFO Database Record
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 | 0.005 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".