Delay activity during visual working memory: A meta-analysis of 30 fMRI experiments
Bibliographic record
Abstract
Visual working memory refers to the temporary maintenance and manipulation of task-related visual information. Recent debate on the underlying neural substrates of visual working memory has focused on the delay period of relevant tasks. Persistent neural activity throughout the delay period has been recognized as a correlate of working memory, yet regions demonstrating sustained hemodynamic responses show inconsistency across individual studies. To develop a more precise understanding of delay-period activations during visual working memory, we conducted a coordinate-based meta-analysis on 30 fMRI experiments involving 515 healthy adults with a mean age of 25.65 years. The main analysis revealed a widespread frontoparietal network associated with delay-period activity, as well as activation in the right inferior temporal cortex. These findings were replicated using different meta-analytical algorithms and were shown to be robust against between-study heterogeneity and publication bias. Further meta-analyses on different subgroups of experiments with specific task demands and stimulus types revealed similar delay-period networks, with activations distributed across the frontal and parietal cortices. The roles of prefrontal regions, posterior parietal regions, and inferior temporal areas are reviewed and discussed in the context of content-specific storage. We conclude that cognitive operations that occur during the unfilled delay period in visual working memory tasks can be flexibly expressed across a frontoparietal-temporal network depending on experimental parameters.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".