A comparison of immediate serial recall and immediate serial recognition.
Bibliographic record
Abstract
In an immediate serial recall task, participants are presented with a list of items that they must subsequently report back in the original presentation order. Although immediate serial recall has long been taken as the standard short-term and working memory task, a growing body of literature has instead made use of immediate serial recognition. In immediate serial recognition, a list of items is presented and subsequently represented, either in the exact same order or with two adjacent items swapped. Participants must decide whether the order of the list items was the same or different across the two presentations. Whereas serial recall and serial recognition are often treated as comparable tasks, with a few differences that make one or the other more suitable for a given experiment, the relationship between them has not been carefully examined. In this article, we report a series of experiments that directly compare the effects of various manipulations using the two tasks. In Experiments 1 and 2 we show an effect of word frequency in serial recall but not serial recognition. In Experiments 3 and 4 we show an effect of semantic relatedness in serial recall but not serial recognition. Experiments 5 and 6 demonstrate that the two tests are similarly affected by word concreteness and acoustic similarity, respectively. We consider the theoretical and methodological implications of these findings. (PsycINFO Database Record (c) 2019 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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".