The item/order account of word frequency effects: Evidence from serial order tests
Bibliographic record
Abstract
According to the item/order hypothesis, high-frequency words are processed more efficiently and therefore order information can be readily encoded. In contrast, low-frequency words are processed less efficiently and the focus on item-specific processing compromises order information. Most experiments testing this account use free recall, which has led to two problems: First, the role of order information is difficult to evaluate in free recall, and second, the data from free recall show all three possible patterns of results: memory for high-frequency words can be better than, the same as, or worse than that for low-frequency words. A series of experiments tested the item/order hypothesis using tests where the role of order information is less ambiguous. The item/order hypothesis predicts better performance for high- than low-frequency words when pure lists are used in both immediate serial recall (ISR) and serial reconstruction of order (SRO) tests. In contrast, when mixed (alternating) lists are used, it predicts better performance for low- than for high-frequency words with ISR tests, but equivalent performance with SRO tests. The experiments generally confirm these predictions, with the notable exception of a block order effect in SRO tasks: When a block of low-frequency lists preceded a block of high-frequency lists, a high-frequency advantage was observed but when a block of high-frequency lists preceded a block of low-frequency lists, no frequency effect was observed. A final experiment provides evidence that this block order effect is due to metacognitive factors.
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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".