Common and distinct correlates of construction and elaboration of episodic-autobiographical memory: An ALE meta-analysis
Bibliographic record
Abstract
The recollection of episodic-autobiographical memories (EAMs) entails a complex temporal dynamic, from initial "construction" to subsequent "elaboration" of memories. While there is consensus that EAM retrieval involves a distributed network of brain regions, it is still largely debated which regions specifically contribute to EAM construction and/or elaboration. To clarify this issue, we conducted an Activation Likelihood Estimation (ALE) meta-analysis based on the Preferred Reporting Items for Systematic-Reviews and Meta-Analyses (PRISMA) method. We found common recruitment of the left hippocampus and posterior cingulate cortex (PCC) during both phases. Additionally, EAM construction led to activations in the ventromedial prefrontal cortex, left angular gyrus (AG), right hippocampus, and precuneus, while the right inferior frontal gyrus was activated by EAM elaboration. Although most of these regions are distributed over the default mode network, the current findings highlight a differential contribution according to early (midline regions, left/right hippocampus, and left AG) versus later (left hippocampus, and PCC) recollection. Overall, these findings contribute to clarify the neural correlates that support the temporal dynamics of EAM recollection.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".