Exploring the use of phonological and semantic representations in working memory.
Bibliographic record
Abstract
In the traditional conception of working memory for word lists, phonological codes are used primarily, and semantic codes are often discarded or ignored. Yet, other evidence indicates an important role for semantic codes. We carried out a preplanned set of four experiments to determine whether phonological and semantic codes are used similarly or differently. In each trial, random lists of one, two, three, four, six, or eight words were followed by a probe to be judged present in the list or absent from it. Sometimes, a probe was absent from the list but rhymed with a list item (in Experiments 1 and 2) or was a synonym of a list item (in Experiments 3 and 4). A probe that was similar to a list item was to be rejected just like other nontarget probes, a reject-similar use (in Experiments 1 and 3), or it was to be placed in the same category as list items, an accept-similar use (in Experiments 2 and 4). The results were comparable in the accept-similar use of both phonological and semantic codes. However, the reject-similar use was interestingly different. Rejecting rhyming items was more difficult than rejecting control words, as expected, whereas rejecting synonyms was easier than rejecting control words, presumably due to a recall-to-reject process. This effect increased with memory load. We discuss theoretically important differences between the use of phonology and semantics in working memory. (PsycInfo Database Record (c) 2022 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".