A systematic review and meta‐analysis of memory‐guided attention: Frontal and parietal activation suggests involvement of fronto‐parietal networks
Bibliographic record
Abstract
Prior knowledge and long-term memory can guide our attention to facilitate search for and detection of subtle targets embedded in a complex scene. A number of neuropsychological and experimental studies have investigated this effect, yet results in the field remain mixed, as there is a lack of consensus regarding the neural correlates thought to support memory-guided attention. The purpose of this systematic review and meta-analysis was to identify a common set of brain structures involved in memory-guided attention. Statistical analyses were computed on functional magnetic resonance imaging (fMRI) studies that presented participants with a task that required them to detect a target or a change embedded in repeated and novel complex visual displays. After a systematic search, 10 fMRI studies met the selection criteria and were included in the analysis. The results yielded four significant clusters. Activity in right inferior parietal (Brodmann area [BA] 9) and right superior parietal (BA 7) lobes suggests involvement of a fronto-parietal attention network, while activity in left mid-cingulate cortex (BA 23) and right middle frontal gyrus (BA 10) suggests involvement of a fronto-parietal control network. These findings are consistent with the notion that fronto-parietal circuits are important for interfacing retrieved memories with attentional systems to guide search. This article is categorized under: Psychology > Memory Psychology > Learning Psychology > Attention.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".