Functional Brain Networks Underlying Autobiographical Event Simulation: An Update
Bibliographic record
Abstract
fMRI studies typically explore changes in the BOLD signal underlying discrete cognitive processes that occur over milliseconds to a few seconds. However, autobiographical cognition is a protracted process and requires fMRI tasks with longer trials to capture the temporal dynamics of the underlying brain networks. In the current study, we provided an updated analysis of the fMRI data obtained from a published autobiographical event simulation study, with a slow event-related design (34-sec trials), that involved participants recalling past, imagining past, and imagining future autobiographical events, as well as completing a semantic association control task. Our updated analysis using Constrained Principal Component Analysis for fMRI retrieved two networks reported in the original study: (1) the Default Mode Network, which activated during the autobiographical event simulation conditions but deactivated during the control condition, and (2) the Multiple Demand Network, which activated early in all conditions during the construction of the required representations (i.e., autobiographical events or semantic associates). Two novel networks also emerged: (1) the Response Network, which activated during the scale-rating phase, and (2) the Maintaining Internal Attention Network, which, while active in all conditions during the elaboration of details associated with the simulated events, was more strongly engaged during the imagination and semantic association control conditions. Our findings suggest that the Default Mode Network does not support autobiographical simulation alone, but it co-activates with the Multiple Demand Network and Maintaining Internal Attention Network, with the timing of activations depending on evolving task demands during the simulation process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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".