Set size and long-term memory/lexical effects in immediate serial recall: Testing the impurity principle
Bibliographic record
Abstract
The impurity principle (Surprenant & Neath, 2009b) states that because memory is fundamentally reconstructive, tasks and processes are not pure. This principle is based on a long line of research showing the effects of one memory system or process on another. Although the principle is widely accepted, many researchers appear hesitant to endorse it in extreme edge cases. One such case involves the effects of long-term memory and lexical factors when a small, closed set of items is used. According to this view, because the subject knows the set of items, there will be no effect of item information. In contrast, the impurity principle predicts that such effects can still be observed, because immediate serial recall with a small closed set of items is not a pure test of order information. Four experiments tested this edge case. In Experiments 1 and 2, we found concreteness effects when item uncertainty was minimized in both within-subjects (Exp. 1) and between-subjects (Exp. 2) designs. In Experiments 3 and 4, we found frequency effects when item uncertainty was minimized in both within-subjects (Exp. 3) and between-subjects (Exp. 4) designs. Analyses of intrusion and omission errors indicated that the sets of items had been learned. Analyses by experiment half also confirmed that the effects of concreteness and frequency were observable in the latter stages of the experiments, when there should have been even less doubt about the items. The results support the impurity principle and suggest that hesitation about accepting it in edge cases is unwarranted.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".