The block order effect in reconstruction of order tasks and metacognitive processing.
Bibliographic record
Abstract
In serial reconstruction of order tasks, high-frequency words are better remembered than otherwise equivalent low-frequency words. Neath and Quinlan (2021) found that although the usual high-frequency advantage was observed when subjects received a block of low-frequency lists first followed by a block of high-frequency lists, there was no frequency effect when subjects received a block of high-frequency lists followed by a block of low-frequency lists. In order to assess whether the block order effect simply reflects the inherent changeability of frequency effects, we manipulated concreteness, a much more stable effect. Experiment 1 found a block order effect with concreteness: The usual advantage for concrete over abstract words was observed only when the abstract block came first and the concrete block second; when the block order was reversed, no concreteness effect was seen. In Experiment 2, subjects did not know whether the test would be serial reconstruction of order or immediate serial recall until after list presentation. This eliminated the block order effect, just as when frequency was manipulated. Experiment 3 found a block order effect with a free reconstruction of order task and with both open and closed stimulus sets. Given that the pattern of results with concreteness is the same as with frequency, it suggests the block order effect is not unique to frequency and that a more general explanation, such as a metacognitive account, is needed. (PsycInfo Database Record (c) 2023 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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".