Intrinsic connectivity of left ventrolateral prefrontal cortex predicts individual differences in controlled semantic retrieval
Bibliographic record
Abstract
Control processes allow us to constrain the retrieval of semantic information from long-term memory so that it is appropriate for the task or context. Control demands are influenced by the strength of the target information itself and by the circumstances in which it is retrieved, with more control needed when relatively weak aspects of knowledge are required and after the sustained retrieval of related concepts. To investigate the neurocognitive basis of individual differences in these aspects of semantic control, we used resting-state fMRI to characterise the intrinsic connectivity of left ventrolateral prefrontal cortex (VLPFC), implicated in controlled retrieval, and examined associations on a paced serial semantic task, in which participants were asked to detect category members amongst distractors. This task manipulated both the strength of target associations and the requirement to sustain retrieval within a narrow semantic category over time. We found that individuals with stronger connectivity between VLPFC and medial prefrontal cortex within the default mode network (DMN) showed better retrieval of strong associations (which are thought to be recalled more automatically). Stronger connectivity between the same VLPFC seed and another DMN region in medial parietal cortex was associated with larger declines in retrieval over the course of the category. In contrast, participants with stronger connectivity between VLPFC and cognitive control regions within the ventral attention network (VAN) had better controlled retrieval of weak associations and were better able to sustain their comprehension throughout the category. These effects overlapped in left insular cortex within the VAN, indicating that a common pattern of connectivity is associated with different aspects of controlled semantic retrieval induced by both the structure of long-term knowledge and the sustained retrieval of related information.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".