Functional Brain Networks Underlying Autobiographical Event Simulation: An Update
Bibliographic record
Abstract
Functional magnetic resonance imaging (fMRI) studies typically explore changes in the blood-oxygen-level-dependent (BOLD) signal underlying discrete cognitive processes that occur over milliseconds to a few seconds. However, autobiographical cognition is a more protracted process and requires fMRI tasks with longer trials to capture the temporal dynamics of the underlying brain networks. In the current study, we provide an updated analysis of the fMRI data obtained from a published autobiographical event simulation task, with a slow-event-related design (34-second trials), that involved participants recalling past autobiographical events, imagining past/future autobiographical events, and completing a semantic association control task. Our updated analysis using Constrained Principal Component Analysis for fMRI (fMRI-CPCA) retrieved two networks reported in the original results, including the Default Mode Network (DMN) which was activated in the autobiographical event simulation conditions but deactivated in the semantic association control condition, and the Multiple Demand Network (MDN) which peaked early during all conditions but was more sustained in the recall condition. Two novel networks emerged, including the Maintaining Internal Attention network (MAIN) which, while active for all conditions, was more strongly engaged during the imagination and semantic association control conditions than during the Recall condition, suggesting a role in constructing novel associations. These results suggest that the DMN does not support autobiographical simulation alone, but co-activates with the MDN and MAIN networks, with the timing of peak activations depending on evolving task demands during the simulation process.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".