A default-control network cortical gradient differentiates the imagination of social and solitary experiences
Bibliographic record
Abstract
Abstract Understanding the neural basis of spontaneous thought - when attention shifts from external tasks to internally generated content such as reminiscing or planning - remains a central challenge in cognitive neuroscience. Progress in this area has lagged studies of externally driven cognition, in part because self-generated thought is difficult to control and measure. Recent work suggests that transitions between externally and internally focused cognitive states follow a continuous neural activation gradient that reflects opposing patterns of engagement of the frontoparietal control network versus the default mode network. To characterize internally focused cognitive states that prospectively engage the default mode network, we used functional Magnetic Resonance Imaging (fMRI) to measure brain activity while participants imagined a range of personal scenarios prompted by generic text cues (e.g., party, housework) to mimic natural thought. Before scanning, participants described each scenario verbally and rated its experiential feature content. Gradient-space analysis revealed a cognitive transition within imagined states: solitary activities (e.g., housework) preferentially recruited the frontoparietal network relative to the default mode network, whereas social activities (e.g., a party) showed the opposite pattern. Because all cognitive states were internally focused (imagined) and elicited via the same non-interactive task, these results refine interpretations of the frontoparietal-default mode gradient. They show that this gradient does not simply differentiate external task-positive from internal task-negative states but also tracks semantic differences between internal cognitive states.
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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 | 0.000 |
| 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".