Neural Correlates of Reactivation and Vividness Reveal Separable Contributions to Objective and Subjective Measures of Episodic Memory
Bibliographic record
Abstract
Abstract Episodic recollections vary in fidelity, sharpness, and strength—qualities that can be examined using both introspective judgements of mental states and objective measures of brain activity. Subjective and objective measures are both valid ways of “reading out” the content of someone’s internal mnemonic states, each with different strengths and weaknesses. St-Laurent and colleagues (2015) investigated the neural correlates of memory vividness ratings and neural reactivation during memory recall and found considerable overlap, suggesting common neural basis underlying these different markers of successful recollection. Here we extended this work with a much more extensive examination in which we used meta-analytic methods to pool four neuroimaging datasets in order to compare and contrast the neural substrates of neural reactivation and vividness judgements. While reactivation and vividness judgements correlated positively with one another and were associated with common univariate activity in the dorsal attention network and anterior hippocampus, some differences were also observed. Vividness judgments were tied to stronger activation in the striatum and dorsal attention network, together with suppression of default mode network nodes, and we also observed a trend for reactivation to be more closely associated with early visual cortex activity. A mediation analysis found support for the hypothesis that neural reactivation is necessary for vivid recollection, with activity in the anterior hippocampus associated with greater reactivation. Our results suggest that neural reactivation and vividness judgements reflect common recollective processing but differ in the extent to which they engage effortful, attentional processing. Additionally, the similarity between reactivation and vividness appears to arise, partly, through hippocampal processing during recollection.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".