Precuneus stimulation alters the neural dynamics of autobiographical memory retrieval
Bibliographic record
Abstract
Autobiographical memory (AM) unfolds over time, but little is known about the dynamics of its retrieval. Space-based models of memory implicate the hippocampus, retrosplenial cortex, and precuneus in early memory computations. Here we used transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG) to investigate the causal role of the precuneus in the dynamics of AM retrieval. During early memory search and construction, precuneus stimulation compared to vertex stimulation led to delayed evoked neural activity within 1000 ms after cue presentation. During later memory elaboration, stimulation led to decreased sustained positivity. We further identified a parietal late positive component during memory elaboration, the amplitude of which was associated with spatial perspective during recollection. This association was disrupted following precuneus stimulation, suggesting that this region plays an important role in the neural representation of spatial perspective during AM. These findings demonstrate a causal role for the precuneus in early AM retrieval, during memory search before a specific memory is accessed, and in spatial context reinstatement during the initial stages of memory elaboration and re-experiencing. By utilizing the high temporal resolution of MEG and the causality of TMS, this study helps clarify the neural correlates of early naturalistic memory retrieval.
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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 | 0.000 |
| 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) | 0.001 | 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".