Macroscale brain states support the control of semantic cognition
Bibliographic record
Abstract
Understanding how the human brain adapts to varying cognitive demands is crucial in neuroscience. Here, we examined how networks involved in controlled semantic retrieval reconfigure themselves to generate neurocognitive states appropriate to different task contexts. We parametrically varied the demands of two semantic tasks - global association and feature matching judgments - and contrasted these effects of cognitive control with those of non-semantic tasks. We then characterized these effects on the cortical surface and within a whole-brain state space, anchored by the top three dimensions of intrinsic connectivity. Our results revealed that demanding semantic association tasks elicited more activation in the anterior regions of the prefrontal and temporal cortex. In contrast, difficult semantic feature matching tasks produced more posterior activation, aligning closely with regions engaged during multiple demanding non-semantic tasks. In both semantic feature matching and non-semantic contexts, the difficulty effects were situated towards the controlled end of a dimension capturing functional separation between cognitive control and default mode regions. Conversely, in semantic association tasks, the difficulty effects elicited similar responses across both cognitive control and default mode networks. Furthermore, controlled association and non-semantic control were located towards the heteromodal end of a heteromodal-unimodal dimension, while semantic feature matching involved a brain state that was more visual and unimodal. These findings demonstrate that a variety of brain states underpin controlled cognition. Specifically, cognitive control regions interact with heteromodal semantic knowledge system to identify contextually relevant conceptual overlaps (e.g., associating 'DOG' with 'BEACH'), and separate from heteromodal memory regions for modality-specific conceptual overlaps (e.g., connecting 'DALMATIAN' with 'BLACK AND WHITE').
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".