Intrinsic neural timescales shape memory encoding and retrieval
Bibliographic record
Abstract
Historically, different memory processes like encoding and retrieval have been distinguished. However, recent models emphasize their continuum across the shorter and longer timescales of encoding and retrieval while, at the same time, both are featured by distinct cognitive demands. The exact neural mechanisms connecting and, at the same time, differentiating encoding and retrieval across their multiple timescales remain yet unclear, though. Using EEG, we here measure the brain's Intrinsic neural timescales (INT) by the autocorrelation window (ACW) during encoding and retrieval of memory. Our main findings are: (i) direct behavioral connection of encoding (spatial fit judgment of the pictures) with those of retrieval (precision, false alarms, accuracy); (ii) a clear state-dependent neural differentiation, with longer ACW during encoding (temporal integration) and shorter ACW during retrieval (temporal segregation), a distinction not observed for another dynamic measure, the power-law exponent (PLE); (iii) high trait-like stability of ACW, with an individual's ACW remaining strongly correlated across rest, encoding, and retrieval states; and (iv) this stable, trait-like ACW (but not its state-dependent modulation) robustly predicts memory performance, with longer trait ACW correlating with higher accuracy and precision and fewer false alarms. Together, we demonstrate that the brain's INT both connect and differentiate encoding and retrieval on both neural and behavioral grounds. This supports and extends current dynamic, e.g., temporal, models of memory by showing the key relevance of the brain's INT (as measured by the ACW) in shaping encoding and 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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".