Further evidence that the speed of working memory consolidation is a structural limit
Bibliographic record
Abstract
It has been proposed that the typically slow consolidation of information from vision to working memory (WM) is under flexible control, and thus can be speeded based on task demands. Recently (Carlos et al., 2023, doi: 10.3758/s13414-023-02757-7), we showed that consolidation is not sped even when it is prioritized over a subsequent competing decision task (T2). However, other research (Nieuwenstein et al., 2015, doi: 10.1167/15.12.739; Woytaszek, 2020) has manipulated the proportion of trials with T2 present and suggested that anticipated interference from competing tasks can lead to speeding of consolidation. Here, we present evidence against speeding of consolidation even when interference can be anticipated, providing an additional line of evidence against flexible control of WM consolidation. Using a within-subjects manipulation, participants completed blocks of a WM task with T2 presented at varying delays from the WM sample, on either 50% or 100% of trials. Retroactive interference from T2 onto WM was similar regardless of block (i.e., T2 probability). In another manipulation, we also varied the delay from T2 response to WM probe and found that this second delay’s duration had no effect on WM reports. Importantly, this suggests that changes in WM performance with sample-T2 delay measure only the interruption of WM consolidation and are not contaminated by proactive interference from T2 onto the report of information from WM. In sum, the present results are consistent with the transfer of information from vision to WM being a slow process that is not under flexible control—either from explicit volitional prioritization, or implicit demands to counter anticipated interference.
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 | 0.020 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".