Individual differences in working memory, secondary memory, and fluid intelligence: Evidence from the levels-of-processing span task.
Bibliographic record
Abstract
Individual differences in working memory (WM) are related to performance on secondary memory (SM), and fluid intelligence (gF) tests. However, the source of the relation remains unclear, in part because few studies have controlled for the nature of encoding; therefore, it is unclear whether individual variation is due to encoding, maintenance, or retrieval processes. In the current study, participants performed a WM task (the levels-of-processing span task; Rose, Myerson, Roediger III, & Hale, 2010) and a SM test that tested for both targets and the distracting processing words from the initial WM task. Deeper levels of processing at encoding did not benefit WM, but did benefit subsequent SM, although the amount of benefit was smaller for those with lower WM spans. This result suggests that, despite encoding cues that facilitate retrieval from SM, low spans may have engaged in shallower, maintenance-focused processing to maintain the words in WM. Low spans also recalled fewer targets, more distractors, and more extralist intrusions than high spans, although this was partially due to low spans' poorer recall of targets, which resulted in a greater number of opportunities to commit recall errors. Delayed recall of intrusions and commission of source errors (labeling targets as processing words and vice versa) were significant negative predictors of gF. These results suggest that the ability to use source information to recall relevant information and withhold recall of irrelevant information is a critical source of both individual variation in WM and the relation between WM, SM, and gF. (PsycINFO Database Record (c) 2013 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".