Perceptual coupling and decoupling are associated with individual differences in working memory encoding and maintenance
Bibliographic record
Abstract
Working memory (WM) allows goal-relevant information to be encoded and maintained in mind, even when the contents of WM are incongruent with the immediate environment. While regions of heteromodal cortex are important for WM, the neural mechanisms that relate to individual differences in the encoding and maintenance of goal-relevant information remain unclear. Here, we used behavioral correlates of two large-scale heteromodal networks at rest, the default mode (DMN) and frontoparietal (FPN) networks, to understand their contributions to distinct features of WM. We assessed each individual's ability to resist distracting information during the encoding and maintenance phases of a visuospatial WM task. Individuals with stronger connectivity of DMN with medial visual and retrosplenial cortex were less affected by encoding distraction. Conversely, weaker connectivity of both DMN and FPN with visual regions was associated with better WM performance when target information was no longer in the environment and distractors were presented in the maintenance phase. Our study suggests that stronger coupling between heteromodal cortex and visual-spatial regions supports WM encoding by reducing the influence of concurrently presented distractors, while weaker visual coupling is associated with better maintenance of goal-relevant information because it relates to the capacity to ignore task-irrelevant changes in the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".