An individual differences analysis of the neurocognitive architecture of the semantic system at rest
Bibliographic record
Abstract
Efficient semantic cognition depends on accessing and selecting conceptual knowledge relevant to the current task or context. This study explored the neurocognitive architecture that supports this function by examining how individual variation in functional brain organisation predicts comprehension and semantic generation. Participants underwent resting state functional magnetic resonance imaging (fMRI) and, on separate days, performed written synonym judgement, and letter and category fluency tasks. We found that better synonym judgement for high frequency items was linked to greater functional coupling between posterior fusiform and anterior superior temporal cortex (aSTG), which might index orthographic-to-semantic access. However, stronger coupling between aSTG and ventromedial prefrontal cortex was associated with poor performance on the same trials, potentially reflecting greater difficulty in focussing retrieval on relevant features for high frequency items that appear in a greater range of contexts. Fluency performance was instead linked to variations in the functional coupling of the inferior frontal gyrus (IFG); anterior IFG was more coupled to regions of primary visual cortex for individuals who were good at category fluency, while poor letter fluency was predicted by stronger coupling between posterior IFG and retrosplenial cortex. These results show that individual differences in functional connectivity at rest predict semantic performance and are consistent with a component process account of semantic cognition in which representational information is shaped by control processes to fit the current requirements, in both comprehension and fluency tasks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".